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Image-Based Separation of Reflective and Fluorescent Components Using Illumination Variant

Image-Based Separation of Reflective and Fluorescent Components Using Illumination Variant and Invariant Color

Cherry Zhang and Imari Sato,Member,IEEE

Abstract—Traditionally,researchers tend to exclude fluorescence from color appearance algorithms in computer vision and image processing because of its complexity.In reality,fluorescence is a very common phenomenon observed in many objects,from gems and corals,to different kinds of writing paper,and to our clothes.In this paper,we provide detailed theories of fluorescence

phenomenon.In particular,we show that the color appearance of fluorescence is unaffected by illumination in which it differs from ordinary reflectance.Moreover,we show that the color appearance of objects with reflective and fluorescent components can be represented as a linear combination of the two components.A linear model allows us to separate the two components using images taken under unknown illuminants using independent component analysis(ICA).The effectiveness of the proposed method is

demonstrated using digital images of various fluorescent objects.

Index Terms—Reflectance components separation,fluorescence emission,diffuse reflection,illumination

1I NTRODUCTION

I N the field of computer vision,recognizing objects and patterns by their color has always been a difficult problem because the color appearance of objects varies dramatically with surrounding illumination.Researchers in computa-tional color constancy proposed many algorithms and models to discount the effect of illumination and recover the true color of objects[1],[8],[4].Researchers in image reproduction and realistic rendering strive to accurately predict the color of objects under arbitrary illuminants. While the algorithms and techniques compute color appearance differently,they share one common assump-tion:None of the objects in the scene exhibit fluorescence. Fluorescence is the emission of visible light by objects that are exposed to a source light covering the ultraviolet range, (e.g.,sunlight and UV light).In reality,fluorescence is a very common phenomenon observed in many objects,from gems and corals,to different kinds of writing paper,and to our clothes(Fig.1).Through intensive studies on color constancy algorithms,Barnard concluded that fluorescent surfaces are common and present in20percent of randomly constructed scenes[3].Therefore,to handle color accurately, computer vision and image synthesis algorithms ought to take fluorescence into account.

By experimentation,we discovered that a composite object with both ordinary reflective and fluorescent compo-nents has the color appearance that is the sum of the two components interacting with illumination differently.To handle the two components correctly,it is necessary to separate them.This motivated us to develop a method for separating fluorescence from reflectance.Assume,in an ordinary color camera,the color of a generic pixel p c on the captured image is a linear contribution

p c?a c p c;Otb c p c;F;e1Twhere c?f R;G;B g,p c;O and p c;F are the color of the ordinary reflective and fluorescent components.Here,we assume that each surface point corresponding,i.e.,each pixel,contains single fluorescent components.Let a c and b c be the coefficients to represent the amount of contribution from each component to the pixel color p c.a c and b c depend on the interactions between each component and the light source that illuminates the object.Since we do not know the illumination condition under which p c is taken,we need to solve for p c;O and p c;F when only the pixel colors,p c,are known.To make this hard problem solvable,we assume that the reflective and fluorescent components of an image are statistically independent.The assumption is reasonable because,in the absence of image interpretation,the spatial distribution of fluorescent component provides no predic-tion on the spatial distribution of the reflective component. Based on the linear contribution model,we show that given the complete set of pixels f p c g for two images taken under different illuminants,p c;O and p c;F can be effectively recovered using independent component analysis(ICA).

This paper describes an attempt at separating the reflective and fluorescent components of an image.It makes the following contributions in the area of realistic color imaging:

.Providing a theory of fluorescent phenomenon.

.Showing that the color of a fluorescent surface is not affected by the color of its illuminant,in which it

differs from an ordinary reflective surface.

. C.Zhang is with Goldman Sachs.

.I.Sato is with the National Institute of Informatics,2-1-2Hitotsubashi,

Tokyo101-8430,Japan.E-mail:imarik@nii.ac.jp.

Manuscript received2Mar.2012;revised1Aug.2012;accepted16Sept.

2012;published online28Nov.2012.

Recommended for acceptance by P.Felzenszwalb,D.Forsyth,P.Fua,and

T.E.Boult.

For information on obtaining reprints of this article,please send e-mail to:

tpami@https://www.wendangku.net/doc/fb9941873.html,,and reference IEEECS Log Number

TPAMSI-2012-03-0159.

Digital Object Identifier no.10.1109/TPAMI.2012.255.

0162-8828/13/$31.00?2013IEEE Published by the IEEE Computer Society

.Proposing a method for separating the reflective and fluorescent components of an image using ICA,and

an improved method for relighting images under an

arbitrary illuminant.

The remainder of the paper is structured as follows: Section2summarizes earlier research in color constancy, color appearance,and rendering algorithms for fluorescent surfaces,as well as research in image separation.Section3 presents the theories and experimental results explaining how fluorescent surfaces interact with illuminants.Our algorithm and the results for separating ordinary reflective and fluorescent components of an image are presented in Sections4and5.Section6demonstrates the effectiveness of separating ordinary reflective and fluorescent components of an image for relighting scenes.In the conclusion,we discuss issues and future directions of our research.

2R ELATED W ORK

The color appearance of nonfluorescent surfaces has always been the main focus of color-related computer vision algorithms.For example,in computational color constancy, researchers attempt to recover the“true color”of objects under a reference illuminant[1],[8].The true color is then modified to predict the appearance of objects under other illuminants.Barnard et al.studied and compared existing color constancy algorithms by evaluating their performance with a large set of test images[4].Some of the test images contain fluorescent objects,but all of the evaluated color constancy algorithms treat them as ordinary objects.

Later on,researchers realized that assuming all objects are nonfluorescent greatly limits the accuracy of color algorithms because many objects around us exhibit fluores-cence.What makes fluorescence different from ordinary reflection is the transfer of energy from one wavelength to another:fluorescent materials absorb light at a certain wavelength and then re-emit it at other wavelengths, whereas ordinary reflective components reflect light at the same wavelength as incident.Glassner first incorporated the wavelength-shifting property of fluorescence into Kajiya’s rendering equation[10].In Johnson and Fairchild’s research,they provided brief explanations of fluorescence,and extended spectral rendering algorithms to take fluorescence into account[14].Later,Wilkie et al.proposed to render fluorescent emissions as diffuse reflections with a wave-length-shifting property,i.e.,a diffuse surface that reflects light at a wavelength different from the incident one[24].

Hullin et al.proposed new ways to model and render fluorescent objects by acquiring their bispectral bidirec-tional reflectance and reradiation distribution functions and the results showed significant improvement in fluorescent object modeling[12].Furthermore,Barnard proposed ways to improve color constancy algorithms to include spectral data of several fluorescent materials[3].Although Barnard solved some problems by including fluorescent objects in color constancy algorithms,his work was mainly based on experimental measurements.His paper did not provide comprehensive models for fluorescent surfaces.In our paper,we extend Barnard’s research by providing more detailed theories and accurate models for fluorescence.

To accurately predict the appearance of composite objects with both reflective and fluorescent components,it is important to separate the two components.Research in natural science[20]shows necessary procedures for measuring color of fluorescent materials in the spectral domain using optical devices.For example,Haneishi and Kamimura used spectral data taken under multiple light sources under known spectral distributions for characteriz-ing fluorescent samples[15].Alterman et al.separated the appearance of each fluorescent dye from a mixture by unmixing multiplexed images[2].

Nakajima and Tominaga used statistics of fluorescent materials to estimate fluorescent components of real material using multispectral images seen under sunlight [17].Recently,Tominaga et al.proposed a method for estimating the spectral radiance factor of fluorescent objects by using an imaging system consisting of a multiband camera and ordinary light sources[22].In the early stage of our research,we successfully separated the components of fluorescent sheets using spectral data captured by a spectrometer.The successful results motivated us to develop a more practical system for doing the separation using images taken by an ordinary digital camera.

The computer vision community has several methods for separating components of an image[18],[23].Some algo-rithms separate specular reflections from diffuse reflections. Some algorithms separate noncorrelated components of images.For example,Farid and Adelson proposed a method for separating a painting from the reflection of an observer on the glass in front of the painting,using two images[7].

Through the research of fluorescence microscopy,several separation techniques were developed for separating the spectra and concentration of fluorescent materials simulta-neously from given a hyperspectral image[11].Neher et al. proposed an algorithm based on nonnegative matrix factorization(NMF)for estimating the concentration of multiple fluorescence dyes of an image[19].Neher et al. concluded that NMF was very effective when there is little overlap between spectra of fluorescence materials and their concentration varies in the image.In our case,some overlap between reflective and fluorescent components in an image

Fig.1.Examples of fluorescent objects:gems bouncy balls,banana peel,and fluorescent sheets.

is expected,and thus NMF might not be effective.We found that Farid and Adelson’s problem closely resembles the fluorescence-separation problem we are interested in.More specifically,we assume that the reflective and fluorescent components of an image are statistically independent.The assumption is reasonable because,in the absence of image interpretation,the spatial distribution of fluorescent com-ponent provides no prediction on the spatial distribution of the reflective component.From this,an approach based on ICA is used in our case for separating reflective and fluorescent components.Gobinet et al.also suggested the use of ICA for estimation the concentrations of fluorescent materials[11].

3P ROPERTIES OF F LUORESCENT S URFACES

We start by looking at what fluorescence is.Most typical fluorescent material absorbs light in the near ultraviolet(UV) range from200nm to380nm,and re-emit visible light in 380nm to720nm.Some material absorbs short-wavelength visible light and re-emit longer wavelength visible light.The first type of special UV lights are not required to observe fluorescence because many natural lightings,such as day-light and cool fluorescent light,have strong UV components (Fig.2).After decades of studies,researchers can explain fluorescence phenomenon and two unique properties of fluorescent materials with concepts in quantum theory[6], [20]:Fluorescent material always emits light at longer wavelengths than the absorbed light,and the emission spectra of each fluorescent material always have the same frequency distribution(e.g.,shape)regardless of the spectra of incident light.

Due to their unique properties,the appearance of fluorescent surfaces must be computed differently than the reflective surfaces.The color of a reflective surface depends only on the illuminant and its reflectance.For example,the observed spectrumèe Tof a nonfluorescent object under illuminant I is

èe T?Ie TRe T;e2Twhere Ie Tis the spectrum of the illuminant and Re Tis the reflectance of the object at wavelength .

Fluorescent objects interact with illuminants differently than nonfluorescent objects.1Based on the molecular physics of fluorescent objects,researchers in computer graphics developed algorithms for rendering fluorescent objects[21],[14].For a pure fluorescent surface,the observed spectrum depends on the illuminant,the material’s excitation spectrum,and the emission spectrum. The excitation spectrum of a fluorescent object shows how much energy from the illuminant is absorbed at each wavelength;it is a function of the wavelength of the illuminant.For each wavelength in an excitation spectrum, there is a corresponding emission spectrum that shows the frequency distribution and intensity of the emitted light. Usually the emission spectrum is a function of wavelength in the visible range.The frequency distribution of all emission spectra is constant,but the intensity varies.

The properties of fluorescent objects are well shown with experimental results.Fig.3a shows the measured emission spectra of a red-orange fluorescent sheet.Each colored spectrum corresponds to the illuminant at different wavelengths,and they have the same frequency distribu-tion as one another.2Figs.3b and3c show the normalized3 excitation(dotted line)and emission(solid line)spectra of the red-orange and green-yellow fluorescent sheets,respec-tively.From the emission spectrum we can see that the red-orange sheet appears reddish orange when it is illuminated by light in the range of380nm to650nm(Fig.3b).We can also see that the green-yellow exhibits strong fluorescence only for illuminants with much energy between450nm and525nm(Fig.3c).

To obtain the observed spectrum of a pure fluorescent surface,we must consider the overall contribution from its illuminant,excitation,and emission.Suppose the illuminant is I and its intensity at wavelength i is Ie iT.Let K and J represent the normalized excitation and emission spectrum, respectively.Then the observed spectrum,èe ; iT,result-ing from the illuminant at i is

èe ; iT?Ie iTK0e iTJe T;e3Twhere

K0e iT

Ke iT

Ke iTd i

is the relative intensity of the excitation caused by the illuminant at wavelength i.Since Ie iTK0e iTis a scalar,all èe ; iTs have the same shape as Je T.Considering the illuminant at all wavelengths,the overall observed spec-trum is computed by summing upèe ; iTs for all wavelength i,i.e.,

èe T?

Ie iTK0e iTd i

Je T:e4T

Note that the range of i depends on the illuminant,and the range of is the range of the observed light we wish to measure.

3.1Mechanism of Fluorescence

The fluorescence phenomenon is explained by theories in molecular physics[16](Fig.4).Initially,the molecules of a fluorescent object are structured as a lattice in equilibrium in the ground energy state A.When the lattice is irradiated

Fig.2.Natural daylight and a fluorescent lamp both have strong UV and short-wavelength components.

1.Section 3.1describes the fluorescence phenomena by theories in molecular physics.

2.Here,the differences in the long wavelength are caused due to the measurement noise that does not reappear in other measurements.

3.Spectral power distribution is normalized so that the minimum intensity is0and the maximum intensity is1.0.

with incident light,the electrons are excited from the ground state A to a higher energy state B .After the transition,a rearrangement of the ions in the lattice takes place and the system assumes an equilibrium state C .Some energy is dissipated within the lattice during the transition from B to C .From the equilibrium state C ,light is emitted when electrons drop back to the ground state D .From D ,the structure of the lattice is rearranged again to reach the stable equilibrium ground state A .The lifetime of the excitation from state A to state B is 105times longer than the period of lattice rearrangement (B to C ).Thus,no matter how the electrons reach the excited state B ,the system comes to an equilibrium state (C )before emission.This implies that the intensity of the emitted spectra of a fluorescent object depends on the incident light,but the frequency distribution does not.

A nonfluorescent object absorbs photons and the elec-trons in the molecules are excited to a higher energy level but immediately drop back to the original energy state,releasing energy in the form of emitting a photon of equal wavelength.

3.2

Spectrum of Composite Object with Both Ordinary Reflective and Fluorescent Components

Many objects we see every day are neither pure reflective nor pure fluorescent,they are composites of ordinary reflective and fluorescent components.We measured the spectrum of a composite object under the illuminant at different wavelengths.Fig.5shows a typical setup of the experiment.The equipment needed is spectrometer,light source (lamp),and monochromator.The spectrometer is used to measure object’s spectrum.The light source covers both UV and visible light range (e.g.,Xe lamp).The monochromator is used to filter the light source to obtain illumination at a single wavelength.

Fig.6a shows the overall change in the observed spectrum of such object.Each colored line represents the observed spectrum corresponding to the illuminant at a particular wavelength.When the wavelength of the illuminant falls in the UV range or the high-energy range of the object’s excitation spectrum,the fluorescent component dominates;thus we observe fluorescence only (Fig.6b).When the wavelength of the illuminant falls in the visible light range,we observe a mixture of fluorescence and reflectance (Fig.6c).When the illuminant is in the low-energy range of visible light

Fig.

4.Energy-level diagram illustrating

fluorescence.

Fig.3.Measured excitation and emission spectra of fluorescent sheets.

Fig.5.Setup of the experiment.

and falls outside of the excitation spectrum,fluorescence diminishes and we only observe reflectance(Fig.6d).

Clearly,an object’s reflective and fluorescent compo-nents behave significantly differently.In the next section, we will show a unique property of the fluorescent component,and present our findings on how it interacts with illuminants differently from the reflective component.3.3Constant Chromaticity

The most intriguing property of fluorescence we discovered is that the emission spectrum of a fluorescent surface is,up to a constant,independent of its illuminant.Thus,fluor-escent surfaces have constant chromaticity when the illumination condition varies.The proof of this property can be shown mathematically.We can compute the tristimulus values of a fluorescent surface from(4)as

Ie iTK0e iTd i

"xe TJe Td ;e5TY?

Ie iTK0e iTd i

"ye TJe Td ;e6TZ?

Ie iTK0e iTd i

"ze TJe Td ;e7T

where"xe T;"ye T;"ze Tare the CIE color matching functions that specify the“unit amount”of tristimulus values at each wavelength, i is integrated over the range of the illuminant wavelength, is integrated over the range of visible light wavelength(380nm to720nm),and K0e iTis defined in(3).Let

X0?

"xe TJe Td ;e8T

Y0?

"ye TJe Td ;e9T

Z0?

"ze TJe Td e10T

be the reference tristimulus values of the normalized emission spectrum J.Substituting(8)-(10)into(5)-(7),we have X?kX0,Y?kY0,and Z?kZ0with k?

Ie iTK0e iTd i. Note that k is a scalar and its value depends on the spectral power distribution of the illuminant and the excitation spectrum.

Now,define reference chromaticities as

x0?

X0tY0tZ0

;

y0?

X0tY0tZ0

Then the x-chromaticity of the object under an arbitrary illuminant becomes

XtYtZ

kX0

kX0tkY0tkZ0

X0tY0tZ0

?x0:

e11T

Similarly,y?y0.Thus,the chromaticity of the fluorescent material is independent of both the illuminant and excita-tion spectrum;it only depends on the emission spectrum.

The claim of constant chromaticity was verified with experiments.The chromaticities of eight fluorescent and

Fig.6.Observed spectra of fluorescent sheet containing both reflective and fluorescent components.

nonfluorescent surfaces (Fig.7)were measured under 16different illuminants using a spectrometer.The illumi-nants include four CIE standard daylights,five indoor lights,and six colored illuminants whose chrominances were randomly chosen (Fig.8).Table 1shows the correlated color temperatures (CCT)of the CIE standard daylights and indoor lights.

Figs.9and 10show the chromaticities versus illuminants plots of the experiments.The color of each line corresponds to the color of a test surface under white light.The plots show that as illuminants change,the chromaticities of fluorescent surfaces do not change as dramatically as reflective surfaces.Table 2provides a more quantitative comparison.Under standard daylights,both the x -and y -chromaticities of nonfluorescent surfaces on average vary twice as much as fluorescent surfaces.Table 3shows that under random

colored illuminants,the variation in x -chromaticities of nonfluorescent surfaces is twice as much as fluorescent surfaces,and the y -chromaticities varies more than five times.The average euclidean distances of the fluorescent red-orange and yellow-orange surfaces are so small that a human cannot detect those color changes (for reference,see MacAdam ellipses).

The fluorescent green-yellow surface has a greater variance than red-orange and yellow-orange surfaces because its reflective component is more prominent than other fluorescent surfaces and its excitation spectrum also covers a narrower wavelength range;it exhibits strong fluorescence only for illuminants with energy between 450nm and 525nm (Fig.3c).Some colored illuminants in the experiments have weaker intensity in the 450nm to 520nm range.Therefore,a fluorescent green-yellow surface

Fig.7.Fluorescent and nonfluorescent

test surfaces.Left

right:red-orange,green-yellow,yellow-orange,cyan,yellow,red,green,purple.

Fig.8.Illuminants used in verifying constant chromaticity.

Descriptions and CCT’s of CIE Standard

Illuminants

Fig.9.x -chromaticities (top)and y -chromaticities (bottom)plots for test surfaces under various illuminants.

has a greater variance in color change due to its reflective component than red-orange and yellow-orange surfaces.Another interesting observation is that fluorescent surfaces tend to show higher saturation and lightness values than nonfluorescent surfaces.This observation is consistent with the psychophysical study by Fujine et al.that suggests that high-saturation and high-lightness surfaces are perceived as fluorescent [9].

The property of having constant chromaticity gives us a great way to distinguish fluorescence from reflectance.In the next section,a method for separating fluorescence from reflectance is presented.

S EPARATING R EFLECTIVE AND F LUORESCENT C OMPONENTS OF AN I MAGE

A regular charged-couple device (CCD)camera has three color channels:Red (R),Green (G),and Blue (B).When we take an image of composite objects with both ordinary reflective and fluorescent components using a CCD camera,the color of the pixel p c for each channel c ?f R;G;

B g is the sum of the color for reflective component p c;O and fluorescent component p c;F .That is,

p c ?p c;O tp c;F :

e12T

As mentioned before,the proposed approach assumes single fluorescent component for each surface point:(12)is not valid for a surface point made of multiple fluorescent components.

The color of a reflective component is computed as

p c;O ?Z

"c e TI e TR e Td ;e13Twhere "c

e Tis the response curve of channel c for the CCD camera. is integrated over the range of visible light.

The color of the fluorescent component can be computed from (4)as

p c;F ?Z I e i TK 0

e i Td i Z

"c e TJ e Td :e14TSubstituting (13)and (14)to (12),we have

p c ?Z

e TI e TR e Td t

I e i TK 0e i Td i

"c e TJ e Td :e15T

We assume that the responses of a CCD camera have

fairly narrow bandwidth,that is,light goes through the camera at a particular wavelength to reach the sensor [5].The narrowband assumption is often used in color con-stancy algorithms,so we can simplify (15)as

TABLE 2

Variations in the Chromaticities under CIE Standard Daylights and Indoor

Lights

Variations in the Chromaticities under

Random Colored

Illuminants

Fig.10.xy -chromaticity plots for test surfaces under various illuminants.

p c ?"c e c TI e c TR e c TtZ I e i TK 0e i Td i

e c TJ e c T;e16T

where c for c ?f R;G;B g is set to be the wavelength of

each R,G,B channel of the camera.This narrowband assumption is nearly valid if hyperspectral cameras are used instead of an RGB camera.

Now,for each channel,p is represented as a linear combination of the reflective component R and fluorescent component J .Both R and J are unknown to us.The spectral distribution of the illuminant under which p is taken is also unknown.To tackle the problem of blindly separating R and J from the only known variable p ,we make the assumption that the two components are independent.This assumption is reasonable since in our case,the spatial distribution of fluorescent component provides no predic-tion of the spacial distribution of the ordinary reflective components;we expect no correlation between images of the two components.Based on this assumption and (16),we solve the blind separation problem by applying ICA [13]to images of the objects taken under different illuminants.Since ICA requires the number of measurements to be greater than or equal to the number of independent components,we take images P 1and P 2of a fluorescent object under two distinct illuminants I 1and I 2.4Formulate the problem based on (16)for each c ?f R;G;B g channel as

P j 1e c T

P j 2e c T !?r 1e c Tf 1e c Tr 2e c Tf 2e c T !R j e c TJ j e c T

!;e17Twhere P j i is the j th pixel value for i th illumination,R j and

J j are the ordinary reflectance and fluorescence at the j th pixel,r i e c T?"c e c TI i e c T,and

f i e c T?"c e c TZ

I i e i TK 0e i Td i :Here,j represent all pixels in the image.

Let Q c ?M c S c be the short form of the matrix equation above.We call Q c the input matrix ,M c the mixing matrix ,and S c the signal matrix .If we input Q c to ICA,5ICA will first

estimate M c ,and S c is computed as M à1

c Q c .To obtain the image with only the reflective component,we combine R j e c Tin S c for c ?f R;G;B g .Similarly,to obtain the image with only the fluorescent component,we combine J j e c Tin S c .

4.1Ambiguities of ICA

Even though ICA works well for solving our problem,it imposes two ambiguities.First,we cannot determine the “order”of the independent components R and J .In other words,we do not know which resulting component is for fluorescence and which is for reflectance.The reason is that both the mixing matrix and the signal matrix are unknown;for the same set of data Q c ,ICA could recover the pair M c and S c as either

M c ?r

f 1r 2

f 2

!;S c ?R

j J j !or

M c ?f

r 1f 2

r 2

!;S c ?J

j R j

!:The second ambiguity is “scaling.”ICA recovers the mixing matrix within a scale factor of the true mixing matrix.In other words,we cannot compute the absolute intensity of the pixels for each component.Again,the reason is that both M c and S c are unknown;any multipliers in M c can be canceled out by dividing the same scalars in S c .That is,

r 1f 1r 2f 2 !R j

J j !?r 1= f 1= r 2= f 2= ! R j J j

4.1.1Solving the “Ordering”Ambiguity

We solve the ordering ambiguity by using the unique

property of the fluorescent component.Let x j i and y j

i represent the x -,y -chromaticity of the j th pixel in the i th https://www.wendangku.net/doc/fb9941873.html,pute the chromaticity difference at the j th pixel between two input images as

d j

?????????????????????????????????????????????????àx j 1àx j 2á2tày j 1ày j 2á2q :e18TIn Section 3,we showed that d j is very small if j shows the brightness of fluorescent component.Let s j 1and s j

2be the recovered intensities at the j th pixel in the two images computed by ICA.

If each image pixel contains either a fluorescent or a nonfluorescent reflective component,the ordering ambi-guity can be solved by simply counting the number of pixels with high chromaticity difference in these two images.However,in this work,we consider a composite object with both ordinary reflective and fluorescent compo-nents and this makes each image pixel contain some contributions from both components.

To handle this,we first normalize s j 1and s j

2as

s 0j 1?às j 1á2P i às i 1á2;s 0j

2?às j 2á2P i às i 2á2:e19TThen multiply the relative intensities by the chromaticity

difference d j ,and sum over all pixels

t 1?

X j

s 0j 1d j ;t 2?X j

s 0j

2d j :e20TIf t 1is smaller than t 2,then the image represented by s 1is the fluorescent component.Otherwise,the image repre-sented by s 1is the fluorescent component.Intuitively,if the j th pixel contains the fluorescent component,s 0j will be big but d j is small.Therefore,the overall t value for image with fluorescent component is always smaller.This technique assumes that the objects have much stronger fluorescent component than ordinary reflective component.

4.1.2Solving the “Scaling”Problem In the mixing matrix,the integral part of scalar f 1e c T?"c

e c TR I i e i TK 0e i Td i depends only on the spectral dis-tribution of the illuminant and excitation of the fluorescent

4.The formula considers single fluorescent material.The number of measurements should be increased depending on the number of fluorescent components to treat each material as an independent component.

5.We used the FastICA package in MATLAB in our experiments.

component and thus must be the same among all RGB channels.This fact can be used for solving the scaling ambiguity of the fluorescent component if"ce cTare known. For each c?f R;G;B g,we scale the computed s c by f1="ce cT.That is,

J0je cT?s c"ce cT=f1:e21TJ0je cTs estimate the relative intensities in RGB channels and provide the necessary color balance in the final image for fluorescent component.

To achieve correct color balance for the reflective components,we look for or include a reference patch with white reflectance in the input images.The recovered images for RGB channels are combined in a way such that the white patch remains white in the reflectance-only image.If we have a material of known fluorescence such as white paper with bluish fluorescence,we can utilize the known fluores-cence for achieving correct color balance of fluorescent component as well.

4.2Independence of Reflective and Fluorescent

Components

ICA allows two source signals to be separated from two mixed signals based on the assumption that the sources are statistically independent.The use of ICA leads to some limitations on the types of scenes applicable to the proposed approach.First,ICA needs to examine input signals where source signals are mixed differently.From this,the spectra of illumination should vary largely but cover some portions of the spectral reflectance of reflective components as well as the excitation spectra of fluorescent components so that the contribution of both components are noticeable.In addition,cast shadows are not desirable and are thus avoided.

Second,the reflective and fluorescent components should be distributed differently among scene points. Wilkie showed that there is a similarity between the fluorescence emission and the ideal diffuse reflection in terms of their shadings[24].A reasonable separation cannot be achieved by the proposed approach if a scene contains an object that contains uniform diffuse reflective and fluor-escent components.In general,natural objects such as gems and corals contain reflective and fluorescent components with different concentrations from place to place.This should contribute to making their reflective and fluorescent components statistically independent.

The mutual information is one measure of the depen-dence between two source signals.To examine the dependence of the reflective and fluorescent components observed in an image,we provide a test scene and separately observe the reflective and fluorescent compo-nents of the scene using two light sources whose spectral distributions are carefully chosen based on the knowledge of the excitation and emission spectra of the fluorescent component(Fig.3c).

The test scene consists of green-yellow fluorescent sheets (flower)on top of a nonfluorescent background image (Fig.11left).We use a blue light whose spectrum is within the range of the excitation spectrum for observing the fluorescent component and a red light whose spectrum is outside of the excitation spectra for observing the reflective component.Fig.11shows the reflective(middle)and fluorescent(right)components observed under each light in the red channel of a CCD camera.

Then the mutual information between two images P1 and P2is defined as

TeP1;P2T?

j2P1

i2P2

Bej;iTlog

Bej;iT

;e22T

where Bej;iTis the joint probability distribution function of P1and P2and BejTand BeiTare the marginal probability distribution functions of P1and P2,respectively.In this example,neither BejTnor BeiTfollows the normal distribu-tion,and the mutual information between the reflective component image and the fluorescent component image is 0.26bit.For reference,the entropy of the image(8bit grayscale)of the scene seen under white light in the red channel of the camera is6.7bit.In this example,the mutual information between two images is very low and it is reasonable to assume that the reflective and fluorescent components are statistically independent.

This example above shows the statistical independence of one particular scene.Further investigation of how statistical independence is valid for natural scenes by analyzing more sample scenes is important.We will further investigate this issue as one of our future research directions.

5R ESULTS AND A NALYSIS

We tested our method with images taken with an ordinary CCD camera.The spectral sensitivities of this camera is shown in Fig.12.The first scene is an image made with color sheets.The sheets contain different amounts of fluorescence and reflectance(Fig.13a).The top two flowers are made of fluorescent sheets that appear bright yellow and bright red-orange under white light.The flower in the middle and the leaves are made of dark red and dark green

Fig.11.Image seen under white light(left),reflective component image (middle),and fluorescent component image

(right).

Fig.12.Spectral sensitivities of the camera.

nonfluorescent sheets.The background is made of a nonreflective light purple sheet.

We first recovered fluorescent and reflective components using images taken under a green illuminant and a pink illuminant (Fig.14a).The recovered fluorescent component (Fig.14b)shows that the color of the fluorescent component of the yellow fluorescent sheet is,in fact,green.In Section 3,we showed the measured emission spectrum of the sheet (Fig.3c),which suggests that the color of the fluorescent component is green.Furthermore,we took images of the fluorescent flowers under UV light,which provided “ground truth”for the color of the fluorescent components (Fig.13b).Our recovered appearance agrees with experi-mental results,as well as the “ground truth.”The dark red and dark green sheets used in making the scene have ordinary reflectance only since the color of the middle flower and the leaves in the recovered reflective component (Fig.14c)is the same as the color seen under white light.It is also worth noting that the yellow fluorescent flower appears to be orange in the recovered image for the reflective https://www.wendangku.net/doc/fb9941873.html,bining the orange color with the green color in the fluorescent component gives the flower its final yellow appearance.Moreover,the red-orange fluorescent sheet has much stronger fluorescence compare to the yellow fluorescent sheet.Therefore,its color

appearance is almost all contributed by the fluorescent component.

The second scene consists of two fluorescent sheets on top of a nonfluorescent background image with complex color patterns.Our method succeeded in identifying the green and red-orange color of the fluorescent sheets.The recovered image for fluorescent component (Fig.15b (left))does not show the background image at all,which clearly demon-strates the correctness and effectiveness of our method.Our method is effective on scenes with nonplanar surfaces as well.Fig.16shows the recovered results for a scene consists of nonplanar objects.The fluorescent sticks and nonfluorescent jar are separated into two images.The color of the sticks (Fig.16d)matches with the ground truth (Fig.16b),and the shadings of the objects are included in the recovered reflective and fluorescent components.

6A PPLICATION IN R ELIGHTING I MAGES

The ability to separate ordinary reflective and fluorescent components of an image allows an alternative yet better way to relight images.Traditional relight algorithms relight an image uniformly by multiplying the ratio between the source illumination and target illumination to the image seen under the source illumination.

For instance,Fig.17a is an image seen under white light.The image consists of two fluorescent sheets (the flowers)on top of a nonfluorescent background image with ordinary reflective surfaces.If we apply a straightforward relight algorithm based on the illumination ratio,we obtain an image in which the color of fluorescent objects in particular is not reproduced accurately (Fig.17c).In contrast,Fig.17d shows the result produced by separately processing ordinary reflective and fluorescent components of the image.Here we relight only the reflective component using the traditional approach and preserve the fluorescent https://www.wendangku.net/doc/fb9941873.html,pared to the result produced by traditional relighting algorithms (Fig.17c),it is evident that the new approach produces output image with more accurate color.It should be noted that the intensity of the fluorescent components should change depending on illumination spectra.This phenomenon cannot be handled by the current

Fig.14.

Recovered fluorescent

and reflective

components

using images taken under green and pink illuminants.

Fig.15.Fluorescent objects

on top of a reflective image with complex color patterns.

Fig.13.Colored sheets under white light and fluorescent sheets under UV light.

approach.One of the future directions of this research includes estimating the excitation spectra of fluorescent components to simulate this phenomenon.

Extracting the fluorescent components of real objects has great potential for many applications such as quality inspection of plants,food hygiene,hygiene maintenance of a factory,and medical diagnosis.

7D ISCUSSIONS ,L IMITATION,AND C ONCLUSIONS

We provided theories of fluorescence phenomenon and explained the difference in appearance between fluores-cence and reflectance.We also proposed a method for separating the fluorescent and reflective components of objects using images captured under two illuminants.The results showed that our method is robust and effective under a variety of situations.Traditionally,researchers in computer vision have been excluding fluorescent objects from their consideration due to the complexity and lack of theoretical basis on fluorescence phenomenon.Our separa-tion method provides additional information about objects in a scene,which has great importance to improving existing computer vision algorithms that consider object appearance.Our result showed that the intensity of fluorescence varies proportionally to the illuminant;we may be able to use this property to infer information about the illuminants.

There are a few limitations to our research that are worth attention and further investigation.First,we assume that for an image,there is no correlation between the spatial distributions of reflective and fluorescent components.We will further explore statistical evidence to support the

assumption.Second,the experiments were conducted in a setting that allows us to have full control of the illumination conditions,environments,inputs,and noise levels.The results we obtained in a well-controlled setting allow us to derive a theoretical model more easily.In the future,we would conduct experiments with settings that resemble the reality more,such as outdoors with natural light,and use the results to fine-tune our model.Third,the proposed separation method assumes that a CCD camera has narrowband responses since such an assumption allows us to derive an initial model.Even though this assumption is made by many color constancy algorithms,it remains controversial.Therefore,it is worth fine-tuning the model more to take non-narrowband responses into consideration.

A CKNOWLEDGMENTS

This research was supported in part by the Ministry of Education,Science,Sports,and Culture Grant-in-Aid for Scientific Research on Innovative Areas.The authors are grateful to Dr.Shin’ya Nishida for helpful suggestions.

R EFERENCES

[1]V.Agarwal and B.R.Abidi,“An Overview of Color Constancy Algorithms,”J.Pattern Recognition Research,vol.1,pp.42-54,2006.[2]M.Alterman,Y.Schechner,and A.Weiss,“Multiplexed Fluores-cence Unmixing,”Proc.IEEE Int’l https://www.wendangku.net/doc/fb9941873.html,putational Photography,pp.1-8,2010.

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H.Farid and E.Adelson,“Separating Reflections and Lighting Using Independent Components Analysis,”Proc.IEEE https://www.wendangku.net/doc/fb9941873.html,puter Vision and Pattern Recognition,vol.1,pp.267-275,1999.

Fig.17.Ground truth versus relighted image produced by traditional and new relighting

algorithms.

Fig.16.Fluorescent and reflective components of an image with real objects.

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Cherry Zhang received the bachelor of mathe-

matics degree(honors in computer science,with

distinction)and the master’s degree in mathe-

matics,both from the University of Waterloo,

Canada,in2008and2011,respectively.During

her undergraduate studies,she worked at four

different companies as an intern.She gained

significant work experience in IT and network

administration,software development,and video

processing algorithm development.During her graduate studies,she focused on doing research in computer graphics under the supervision of Professor Bill Cowan.She also did a summer internship at the National Institute of Informatics in Japan,at which time, she published a paper in CVPR11and received a best student paper honorable mention award.She is currently working as a technology analyst at Goldman Sachs,an investment bank in New York.

Imari Sato received the BS degree in policy

management from Keio University,Japan,in

1994.After studying at the Robotics Institute of

Carnegie Mellon University as a visiting scholar,

she received the MS and PhD degrees in

interdisciplinary information studies from the

University of Tokyo in2002and2005,respec-

tively.In2005,she joined the National Institute

of Informatics,where she is currently an

associate professor.Her primary research inter-ests include the fields of computer vision(physics-based vision,image-based modeling)and computer graphics(image-based rendering, augmented reality).She has received various research awards, including the IPSJ Nagao Special Researcher Award(2010),The Young Scientists’Prize from The Commendation for Science and Technology by the Minister of Education,Culture,Sports,Science and Technology(2009),and the Microsoft Research Japan New Faculty award(2011).She is a member of the IEEE.

.For more information on this or any other computing topic, please visit our Digital Library at https://www.wendangku.net/doc/fb9941873.html,/publications/dlib.

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Index Terms:Multisource noise,speech enhancement,speech qual-ity,non-stationary noise. 1.INTRODUCTION Target speaker separation describes the problem of estimating an unknown clean speech signal recorded by one or several micro-phones in a noisy environment with possible presence of competing speaker(s).The problem?nds applications in many different areas of speech communications,including mobile telephony,robust au-tomatic speech recognition and hearing aids.The research in this area has been carried on for decades-with reporting some success-ful high quality speech enhancement systems.As a noise reduc-tion device is expected to work in noisy environment without a prior knowledge of the noise type,recent research effort has been directed toward studying the robustness of these algorithms in nonstationary noise,including low signal-to-noise ratios(SNRs)[1]. As one step toward studying the problem of enhancing a tar-get speech signal in a multisource environment with nonstationary background noise,recently,the PASCAL challenge,termed as com-putational hearing in multisource environments(CHiME)was orga-nized[2].The challenge addresses several critical aspects on the The work of Pejman mowlaee was partially funded by the European project DIRHA(FP7-ICT-2011-7-288121),by ASD(Acoustic Sensing& Design)and Speech Processing Solutions GmbH Vienna.The work of Rahim Saeidi was funded by the European Community’s Seventh Framework Pro-gramme(FP72007-2013)under grant agreement no.238803.original problem of enhancing and recognizing of a target speech from its noisy version observed in a real-life listening environment mainly characterized by rather low SNR ratios whereas the noise sources are unpredictable,abrupt and highly non-stationary. 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To take advantage of both groups,several methods on com-bining a beamforming stage with a speech enhancement stage as a post-processor have been suggested[5,16].The post-processor at-

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关于坚持的英语演讲稿 Results are not important, but they can persist for many years as a commemoration of. Many years ago, as a result of habits and overeating formed one of obesity, as well as indicators of overall physical disorders, so that affects my work and life. In friends to encourage and supervise, the participated in the team Now considered to have been more than three years, neither the fine rain, regardless of winter heat, a day out with 5:00 time. The beginning, have been discouraged, suffering, and disappointment, but in the end of the urging of friends, to re-get up, stand on the playground. 成绩并不重要，但可以作为坚持多年晨跑的一个纪念。多年前，由于庸懒习惯和暴饮暴食，形成了一身的肥胖，以及体检指标的全盘失常，以致于影响到了我的工作和生活。在好友的鼓励和督促下，参加了晨跑队伍。现在算来，已经三年多了，无论天晴下雨，不管寒冬酷暑，每天五点准时起来出门晨跑。开始时，也曾气馁过、痛苦过、失望过，但最后都在好友们的催促下，重新爬起来，站到了操场上。 In fact, I did not build big, nor strong muscles, not a sport-born people. Over the past few years to adhere to it, because I have a team behind, the strength of a strongteam here, very grateful to our team, for a long time, we encourage each other, and with sweat, enjoying common health happy. For example, Friends of the several run in order to maintain order and unable to attend the 10，000 meters race, and they are always concerned about the brothers and promptly inform the place and time, gives us confidence and courage. At the same time, also came on their own inner desire and pursuit for a good health, who wrote many of their own log in order to refuel for their own, and inspiring. 其实我没有高大身材，也没健壮肌肉，天生不属于运动型的人。几年来能够坚持下来，因为我的背后有一个团队，有着强大团队的力量，在这里，非常感谢我们的晨跑队，长期以来，我们相互鼓励着，一起流汗，共同享受着健康带来的快

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1.How to build a business that lasts100years 0:11Imagine that you are a product designer.And you've designed a product,a new type of product,called the human immune system.You're pitching this product to a skeptical,strictly no-nonsense manager.Let's call him Bob.I think we all know at least one Bob,right?How would that go? 0:34Bob,I've got this incredible idea for a completely new type of personal health product.It's called the human immune system.I can see from your face that you're having some problems with this.Don't worry.I know it's very complicated.I don't want to take you through the gory details,I just want to tell you about some of the amazing features of this product.First of all,it cleverly uses redundancy by having millions of copies of each component--leukocytes,white blood cells--before they're actually needed,to create a massive buffer against the unexpected.And it cleverly leverages diversity by having not just leukocytes but B cells,T cells,natural killer cells,antibodies.The components don't really matter.The point is that together,this diversity of different approaches can cope with more or less anything that evolution has been able to throw up.And the design is completely modular.You have the surface barrier of the human skin,you have the very rapidly reacting innate immune system and then you have the highly targeted adaptive immune system.The point is,that if one system fails,another can take over,creating a virtually foolproof system. 1:54I can see I'm losing you,Bob,but stay with me,because here is the really killer feature.The product is completely adaptive.It's able to actually develop targeted antibodies to threats that it's never even met before.It actually also does this with incredible prudence,detecting and reacting to every tiny threat,and furthermore, remembering every previous threat,in case they are ever encountered again.What I'm pitching you today is actually not a stand-alone product.The product is embedded in the larger system of the human body,and it works in complete harmony with that system,to create this unprecedented level of biological protection.So Bob,just tell me honestly,what do you think of my product? 2:47And Bob may say something like,I sincerely appreciate the effort and passion that have gone into your presentation,blah blah blah-- 2:56(Laughter) 2:58But honestly,it's total nonsense.You seem to be saying that the key selling points of your product are that it is inefficient and complex.Didn't they teach you 80-20?And furthermore,you're saying that this product is siloed.It overreacts, makes things up as it goes along and is actually designed for somebody else's benefit. I'm sorry to break it to you,but I don't think this one is a winner.

关于工作的优秀英语演讲稿

关于工作的优秀英语演讲稿 Different people have various ambitions. Some want to be engineers or doctors in the future. Some want to be scientists or businessmen. Still some wish to be teachers or lawers when they grow up in the days to come. Unlike other people, I prefer to be a farmer. However, it is not easy to be a farmer for Iwill be looked upon by others. Anyway,what I am trying to do is to make great contributions to agriculture. It is well known that farming is the basic of the country. Above all, farming is not only a challenge but also a good opportunity for the young. We can also make a big profit by growing vegetables and food in a scientific way. Besides we can apply what we have learned in school to farming. Thus our countryside will become more and more properous. I believe that any man with knowledge can do whatever they can so long as this job can meet his or her interest. All the working position can provide him with a good chance to become a talent. 1 ————来源网络整理，仅供供参考

个人先进事迹简介

个人先进事迹简介 01 在思想政治方面,xxxx同学积极向上,热爱祖国、热爱中国共产党,拥护中国共产党的领导.利用课余时间和党课机会认真学习政治理论,积极向党组织靠拢. 在学习上,xxxx同学认为只有把学习成绩确实提高才能为将来的实践打下扎实的基础,成为社会有用人才.学习努力、成绩优良. 在生活中,善于与人沟通,乐观向上,乐于助人.有健全的人格意识和良好的心理素质和从容、坦诚、乐观、快乐的生活态度,乐于帮助身边的同学,受到师生的好评. 02 xxx同学认真学习政治理论,积极上进,在校期间获得原院级三好生,和校级三好生,优秀团员称号,并获得三等奖学金. 在学习上遇到不理解的地方也常常向老师请教,还勇于向老师提出质疑.在完成自己学业的同时,能主动帮助其他同学解决学习上的难题,和其他同学共同探讨,共同进步. 在社会实践方面,xxxx同学参与了中国儿童文学精品“悦”读书系,插画绘制工作,xxxx同学在班中担任宣传委员,工作积极主动,认真负责,有较强的组织能力.能够在老师、班主任的指导下独立完成学院、班级布置的各项工作. 03 xxx同学在政治思想方面积极进取,严格要求自己.在学习方面刻苦努力,不断钻研,学习成绩优异,连续两年荣获国家励志奖学金；作

为一名学生干部,她总是充满激情的迎接并完成各项工作,荣获优秀团干部称号.在社会实践和志愿者活动中起到模范带头作用. 04 xxxx同学在思想方面,积极要求进步,为人诚实,尊敬师长.严格要求自己.在大一期间就积极参加了党课初、高级班的学习,拥护中国共产党的领导,并积极向党组织靠拢. 在工作上,作为班中的学习委员,对待工作兢兢业业、尽职尽责的完成班集体的各项工作任务.并在班级和系里能够起骨干带头作用.热心为同学服务,工作责任心强. 在学习上,学习目的明确、态度端正、刻苦努力,连续两学年在班级的综合测评排名中获得第1.并荣获院级二等奖学金、三好生、优秀班干部、优秀团员等奖项. 在社会实践方面,积极参加学校和班级组织的各项政治活动,并在志愿者活动中起到模范带头作用.积极锻炼身体.能够处理好学习与工作的关系,乐于助人,团结班中每一位同学,谦虚好学,受到师生的好评. 05 在思想方面,xxxx同学积极向上,热爱祖国、热爱中国共产党,拥护中国共产党的领导.作为一名共产党员时刻起到积极的带头作用,利用课余时间和党课机会认真学习政治理论. 在工作上,作为班中的团支部书记,xxxx同学积极策划组织各类团活动,具有良好的组织能力. 在学习上,xxxx同学学习努力、成绩优良、并热心帮助在学习上有困难的同学,连续两年获得二等奖学金. 在生活中,善于与人沟通,乐观向上,乐于助人.有健全的人格意识和良好的心理素质.

自我管理演讲稿英语翻译

尊敬的领导，老师，亲爱的同学们，大家好！我是5班的梁浩东。今天早上我坐车来学校的路上，我仔细观察了路上形形色色的人，有开着小车衣着精致的叔叔阿姨，有市场带着倦容的卖各种早点的阿姨，还有偶尔穿梭于人群中衣衫褴褛的乞丐。于是我问自己，十几年后我会成为怎样的自己，想成为社会成功人士还是碌碌无为的人呢，答案肯定是前者。那么十几年后我怎样才能如愿以偿呢，成为一个受人尊重，有价值的人呢？正如我今天演讲的题目是：自主管理。大家都知道爱玩是我们孩子的天性，学习也是我们的责任和义务。要怎样处理好这些矛盾，提高自主管理呢？首先，我们要有小主人翁思想，自己做自己的主人，要认识到我们学习，生活这一切都是我们自己走自己的人生路，并不是为了报答父母，更不是为了敷衍老师。我认为自主管理又可以理解为自我管理，在学习和生活中无处不在，比如通过老师，小组长来管理约束行为和同学们对自身行为的管理都属于自我管理。比如我们到一个旅游景点，看到一块大石头，有的同学特别兴奋，会想在上面刻上：某某某到此一游话。这时你就需要自我管理，你需要提醒自己，这样做会破坏景点，而且是一种素质低下的表现。你设想一下，如果别人家小孩去你家墙上乱涂乱画，你是何种感受。同样我们把自主管理放到学习上，在我们想偷懒，想逃避，想放弃的时候，我们可以通过自主管理来避免这些，通过他人或者自己的力量来完成。例如我会制定作息时间计划表，里面包括学习，运动，玩耍等内容的完成时间。那些学校学习尖子，他们学习好是智商高于我们吗，其实不然，在我所了解的哪些优秀的学霸传授经验里，就提到要能够自我管理，规范好学习时间的分分秒秒，只有辛勤的付出，才能取得优异成绩。在现实生活中，无数成功人士告诉我们自主管理的重要性。十几年后我想成为一位优秀的，为国家多做贡献的人。亲爱的同学们，你们们？让我们从现在开始重视和执行自主管理，十几年后成为那个你想成为的人。谢谢大家！

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