Presentado al 18th International Conference on Pattern Recognition (ICPR)celebrado en  2006 en Hong Kong (China).The selection of the appropriate colorspace for tracking applications has not been an issue previously considered in the literature. Many color representations have been suggested, based on the invariance to illumination changes. Nevertheless, none of them is invariant enough to deal with general and unconstrained environments. In tracking tasks, we might prefer to represent image pixels into a colorspace where the distance between the target and background colorpoints were maximized, simplifying the task of the tracker. Based on this criterion, we propose an 'object dependent' colorspace, which is computed as a simple calibration procedure before tracking. Furthermore, this colorspace may be easily adapted at each frame. Synthetic and real experiments show how this colorspace allows for a better discrimination of the foreground and background, and permits to track in circumstances where the same tracking algorithm relying on other colorspaces would fail.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).This work was supported by CICYT project DPI2004-05414 from the Spanish Ministry of Science and Technology.Peer Reviewe

Moreno-Noguer, Francesc

Samaras, Dimitris

Sanfeliu, Alberto

[Correction Notice: An Erratum for this article was reported in Vol 49(1) of Developmental Psychology (see record 2013-00166-002). In the article, Study 2 is mistakenly described as a 9-month longitudinal study. However, this study covered 11 months.] The present research focuses on 2 factors that might help or hurt women to cope with the uncertainties associated with developmental transitions in modern societies (i.e., starting one's first job, graduating from high school, reentry to work after parental leave). We investigate (a) the role of openness to experience in coping with challenging transitions and (b) the (mal)adaptive consequences of adopting a traditional gender ideology. Starting with the assumption that transitional uncertainty has different consequences for women high or low in openness to experience, a first experiment (N = 61; 18–30 years) demonstrated that self-efficacy and well-being decrease after being confronted with transitional uncertainty among women low in openness. Two longitudinal studies investigated the (mal)adaptive consequences of adopting a traditional gender ideology for women high or low in openness in dealing with challenging transitions. Study 2 examined whether endorsing or rejecting traditional gender role beliefs might help female (but not male) students to maintain a sense of self-efficacy and subjective well-being during the transition of graduating from high school (N = 520, 17–22 years). Study 3 (N = 297; 20–53 years) tested the same model for women in middle adulthood during the transition from parental leave to reentry into work life. For both studies, latent growth analyses showed that endorsing traditional gender role beliefs contributed to self-efficacy and subjective well-being among women low in openness. By contrast, for women high in openness, rejecting traditional gender role beliefs had a positive effect on their relative level of self-efficacy and subjective well-being. Functions of ideologies in the context of challenging transitions are discussed

Weiss, David

Freund, Alexandra M

Wiese, Bettina S

ZORA

Mastering developmental transitions in young and middle adulthood: The interplay of openness to experience and traditional gender ideology on women's self-efficacy and subjective well-being

Freund, Alexandra M.

Wiese, Bettina Simone

Publikationsserver der RWTH Aachen University

Mastering Developmental Transitions in Young and Middle Adulthood: The Interplay of Openness to Experience and Traditional Gender Ideology on Women's Self-Efficacy and Subjective Well-Being

Wiese, Bettina

RWTH Publications

Digital.CSIC

A Target Dependent Colorspace for Robust TrackingFrancesc Moreno-Noguer and Alberto SanfeliuInstitut de Robo`tica i Informa`tica, UPC-CSICLlorens Artigas 4-6, 08028, Barcelona, Spainfmoreno, asanfeliu@iri.upc.eduDimitris SamarasComputer Science DepartmentSUNY Stony Brook, NY 11794-4400, USAsamaras@cs.sunysb.eduAbstractThe selection of the appropriate colorspace for trackingapplications has not been an issue previously considered inthe literature. Many color representations have been sug-gested, based on the invariance to illumination changes.Nevertheless, none of them is invariant enough to deal withgeneral and unconstrained environments. In tracking tasks,we might prefer to represent image pixels into a colorspacewhere the distance between the target and background col-orpoints were maximized, simplifying the task of the tracker.Based on this criterion, we propose an ‘object dependent’colorspace, which is computed as a simple calibration pro-cedure before tracking. Furthermore, this colorspace maybe easily adapted at each frame. Synthetic and real experi-ments show how this colorspace allows for a better discrim-ination of the foreground and background, and permits totrack in circumstances where the same tracking algorithmrelying on other colorspaces would fail. 11 IntroductionAn important initial issue for any color-based trackingalgorithm, concerns to the selection of the colorspace wherethe data is going to be represented. Many colorspaces havebeen proposed in the literature: for instance RGB, HSV ,XY Z, Y UV , Y IQ, and its corresponding normalized ver-sions. Unfortunately, there is not a clear consensus aboutwhich one to use, and the different alternatives have been in-distinctly utilized: The RGB colorspace is used in [6,9]. Theefforts described in [1,10] represent color by the normalizedRGBmodel. Maybe the most extensively used colorspace isthe HSV [8,11,13], and a two dimensional version consider-ing only the HS components [3,7]. Some approaches [4,12]are based on the YUV as well.All this variety indicates that there is not a criterion forthe selection of the appropriate colorspace. In most of thepreviously cited approaches, the selection is based on atrial and error procedure among the various available col-orspaces. In other circumstances, the selection is based onthe invariance of the color representation to illumination1This work was supported by CICYT project DPI2004-05414 from theSpanish Ministry of Science and Technology.changes, and normalized or colorspaces where chromaticityis separated from intensity (such as HSV , Y UV or Y IQ)are utilized. Nevertheless, none of the existing colorspacesis robust enough to deal with real and general environments.In contrast to previous approaches, we propose to selectthe colorspace using the following criteria focused on visualtracking applications: 1) Since the ﬁrst goal in tracking isto discriminate the object of interest from the rest of thescene, an important function of the colorspace should be tomaximize the separation between the target and backgroundcolor points. 2) Furthermore, in order to deal with dynamicenvironments, the colorspace should demonstrate a certaindegree of invariance to illumination changes. Alternatively,the representation of the colorspace should permit an easyadaptation to these changing conditions.In this work we suggest to use an ‘object dependent’colorspace which satisﬁes both previous criteria. The col-orspace, which we call Fisher colorspace, is computed asa simple calibration procedure before tracking, based onthe Linear Discriminant Analysis (LDA) [2]. We prove,by a set of synthetic and real experiments, that the Fishercolorspace provides better representations of the data, interms of fore/background separability, than other existentcolorspaces. We also show that this colorspace is invariantto uniform scaling and shifting of the illumination, and thatits simple parameterization may be easily updated through-out time, thus becoming an adaptable colorspace.2 Fisher colorspaceThe representation of the target and background througha color model maximizing the separation of both classes(criterion 1) may be analyzed as a standard classiﬁcationproblem based on Discriminant Analysis. We are interestedin the linear techniques, since as will be shown in the fol-lowing subsection, this offers certain robustness to illumi-nation changes (criterion 2). Therefore, using LDA [2], theproblem may be reduced to the search of the hyperplane(Fisher plane) that best separates both classes.The Fisher colorspace may be computed from a singleRGB training image, where the points belonging to the ob-ject and background are manually identiﬁed. Assume thatThe 18th International Conference on Pattern Recognition (ICPR'06)0-7695-2521-0/06 $20.00  © 2006Authorized licensed use limited to: IEEE Xplore Customer. Downloaded on February 18, 2009 at 11:44 from IEEE Xplore.  Restrictions apply.the set of n image pixels are arranged into a n×3matrixC=[c1, . . . , cn]T . nO of these pixels belong to the object O,represented by CO=[cO,1, . . . , cO,nO ]T and the rest of nBpixelsCB=[cB,1, . . . , cB,nB ]T belong to the background B.We wish to determine which plane is the most effective indiscriminating between these two subsets of points. Let usdenote such a plane byW = [w1,w2]T ∈ R2×3, wherew1and w2 are vectors in the RGB space, spanning the pointslying on the plane. The projection of CO and CB onto thisplane, generates the sets FO = COWT ∈ RnO×2 andFB = CBWT ∈ RnB×2, respectively.The goal of the LDA is to ﬁnd the best orientation ofthe plane W, such that the separation of the projected sub-sets FO and FB is maximized. In order to determine sucha plane, LDA considers the maximization of the objectivefunction J(W) = (WSbWT )(WSwWT )−1 where Sw isthe within class scatter matrix and Sb is the between classscatter matrix:Sw =∑ε={O,B}nεnnε∑i=1(cε,i − c¯ε)(cε,i − c¯ε)T (1)Sb =∑ε={O,B}nε(c¯ε − c¯)(c¯ε − c¯)T (2)with, c¯ε = 1nε∑nεi=1 cε,i being the ε-class mean vector, andc¯ =∑ε={O,B}nεn c¯ε the total mean vector.The classic LDA method maximizes the J objectivefunction by constructing the rows of W with the eigenvec-tors of S−1w Sb having the highest eigenvalues. Nevertheless,this approach has a limitation, since S−1w Sb is not a full rankmatrix, and in the two class problem discussed here, the hy-perplane W would be a line. Although we are interested inprojecting the data onto a linear space (to increase robust-ness to illumination changes), by projecting the data onto aline we might lose too much information. A better choice,consists of projecting the RGB data onto a plane. There-fore, we will use the nonparametric version of LDA [2].The key point of this LDA extension is that it computes thebetween class scatter matrix Sb using local information andthe K-Nearest Neighbors (KNN) rule, which allows to ob-tain a full rank S−1w Sb matrix. For further details about thenonparametric LDA, the reader is referred to [2].Fig. 1 depicts the main steps to compute the Fisher plane,for two different targets in the same image. Fig. 1a showsthe scene and in Fig. 1b all the image pixels are representedin the RGB colorspace. Note that the Fisher colorspace is‘target dependent’. For instance, observe in Fig. 1c the dif-ference of the Fisher planes obtained for different targets:the ladybird and the ﬂower petals. For example, if we wishto track the ladybird (Fig. 1d), the points belonging to theobject and background are initially provided (Fig. 1e), andthe Fisher plane is computed based on the non-parametricLDA (Fig. 1f). Similar stages are depicted in Fig. 1g-i fortracking the ﬂower petals.(a) (b) (c)(d) (e) (f)(g) (h) (i)Figure 1. Computing the Fisher plane for two dif-ferent targets.2.1 Fisher colorspace in the presence oflighting changesNext, we will see that the Fisher colorspace is invari-ant to certain lighting effects, such as uniform scaling andshifting of illumination. Without loss of generality, wewill assume the parametric version of the LDA, describedby the maximization of the objective function J(W) =(WSbWT )(WSwWT )−1 where Sw and Sb are deﬁnedby Eqs. 1 and 2, respectively.Lemma 1 The Fisher plane is invariant to a uniform illu-mination scalingProof: Given all image points ci, i = 1, . . . , n representedin the RGB colorspace, a uniform illumination scaling isdeﬁned by the mapping S: ci → αci, where α is thescaling factor.We assume that the classiﬁcation of the image points intothe object (O) and background (B) classes is available. Un-der these circumstances, the following statements about thetotal mean and the class means are satisﬁed:S(c¯ε) = S(1nεnε∑i=1cε,i)=1nεnε∑i=1αcε,i = αc¯εS(c¯) = S⎛⎝ ∑ε={O,B}nεnc¯ε⎞⎠ = ∑ε={O,B}nεnS(c¯ε) = αc¯As a consequence, the transformed within class scattermatrix and between class scatter matrix may be written asS(Sw) = α2Sw S(Sb) = α2SbThe 18th International Conference on Pattern Recognition (ICPR'06)0-7695-2521-0/06 $20.00  © 2006Authorized licensed use limited to: IEEE Xplore Customer. Downloaded on February 18, 2009 at 11:44 from IEEE Xplore.  Restrictions apply.(a) (b) (c) (d)Figure 2. Fisher colorspace & lighting changes.Finally, the objective function to be maximized is:S(J(W)) = S(WSbWTWSwWT)=WS(Sb)WTWS(Sw)WT = J(W)Thus, the criteria used to compute the Fisher plane for twoimages related by a linear scaling are exactly the same.Therefore, we conclude that the Fisher plane is invariant to auniform illumination scaling. Lemma 2 The Fisher plane is invariant to uniform illumi-nation shiftingProof: Given all image points ci, i = 1, . . . , n representedin the RGB colorspace, a uniform lighting shifting is deﬁnedby the mapping T : ci → ci + β, where β is the shiftingfactor.Following a similar procedure than for Lemma 1, it canbe shown that T (J(W)) = J(W), i.e, the Fisher plane isalso invariant to a illumination shifting effect. 3 Evaluation of the Fisher colorspace3.1 Invariance to illuminationThe invariance of the Fisher plane to illuminationchanges is demonstrated in the following experiment, wherethe images of a still scenario illuminated by natural lightinghave been acquired during a whole day (Figs. 2(a,b) showtwo representative frames).Several foreground objects have been selected, and foreach of them, the Fisher plane has been computed through-out the whole sequence. The results show that the Fisherplanes (represented by their normal vectors) form separateclusters for every individual target, and the variance in eachcluster is relatively small, proving that the Fisher colorspaceis quite invariant to illumination changes. In Fig. 2d wedepict the distribution of the normals to the Fisher planefor two different targets (indicated in Fig. 2c). The unitarysphere represents the space of all possible conﬁgurations ofthe normal to the Fisher plane. Observe how for each tar-get, the Fisher plane distributions just occupy a small regiononto the conﬁguration space.3.2 Fisher versus other colorspacesNext, we will compare the performance of the pro-posed Fisher colorspace, versus other commonly used col-orspaces. In order to make a fair comparison we will con-sider only those colorspaces deﬁned by two variables, such(a) (b)Fisher rg rb gb xyxz yz HS HV SVFigure 3. Performance of the Fisher colorspacefor camouﬂaging targets.as the combination of two components of the normalizedRGB (namely rg, rb and gb), two components of the nor-malized XYZ (xy, xz, and yz), and two components of theHSV colorspace.The ﬁrst experiment shows an example where the fore-ground is in some degree camouﬂaged with the background(Fig. 3a shows the test image, where the central circle isthe target). In these circumstances, the Fisher colorspaceclearly performs better than other colorspaces. Fig. 3bshows the color distributions of all image points into theRGB colorspace. Blue dots correspond to the backgroundpoints, and red dots are the target color points. Note thatboth classes are in close contact. In spite of this, the projec-tion of the colorpoints onto the Fisher plane does not over-lap the target and background classes. On the other hand,when we represent the points using the other colorspaces,the two classes are greatly overlapping, which will causedifﬁculties in future tracking tasks.A more precise comparison is obtained by computingthe distance between the target and the background rep-resentations. Given the set of object color points FO ={fO,1, . . . , fO,nO} and the set of background color pointsFB = {fB,1, . . . , fB,nB} we compute the distance betweenboth sets with the following metric (which is in accordancewith the nonparametric LDA):dist(FO,FB) =1nOnO∑i=11kk∑j=1‖fO,i −NN jB(fO,i)‖2|det(Sw)|whereNN jB(fO,i) is the j-th nearest neighbor in the setFBto a point fO,i, ‖ · ‖ is the Euclidean norm, and | · | the ab-solute value function. Using this metric, we have computedthe fore/background distance for six different targets. Theresults are shown in Table 1. Note that in most of the cases,the Fisher colorspace provides the largest separation.The 18th International Conference on Pattern Recognition (ICPR'06)0-7695-2521-0/06 $20.00  © 2006Authorized licensed use limited to: IEEE Xplore Customer. Downloaded on February 18, 2009 at 11:44 from IEEE Xplore.  Restrictions apply.T1 T2 T3 T4 T5 T6Fisher 429.1 2445.5 202.1 133.8 9.9 74.2rg 464.3 214.8 8.2 63.9 2.7 4.8rb 115.5 219.1 4.0 61.6 1.8 4.9gb 117.5 183.7 4.5 64.5 2.9 4.8xy 141.3 275.0 2.8 54.3 1.7 4.5xz 115.1 303.7 3.5 70.9 1.9 6.8yz 87.7 274.1 4.7 88.0 4.2 7.9HS 5.8 5.6 147.7 4.5 3.1 5.1HV 5.4 1.3 156.1 0.3 5.1 16.4SV 1.0 16.2 110.3 1.4 5.1 32.8Table 1. Foreground/Background distances.3.3 Integrating the Fisher colorspace intoa tracking frameworkAlthough we have proved that the Fisher colorspace ex-hibits a certain robustness to illumination changes, in a realworld environment we should consider much more complexartifacts which might modify signiﬁcantly the conﬁgurationof the Fisher plane at each frame. As a consequence, an-other important property of the proposed colorspace, is theease with which in can be updated and integrated into atracking framework.Based on the work presented in [5], we designed a track-ing algorithm, where the target was represented simulta-neously by multiple cues. Among these cues, we consid-ered the Fisher plane, parameterized by its normal vector(n=w1×w2), and whose state was continuously updatedand estimated using a particle ﬁlter formulation.The basic idea of this approach, consisted of approx-imating the state of n by a set of p weighted samples{ni, πi}pi=1, with πi being the weight associated with sam-ple ni. The updating procedure, had two stages. At thebeginning of a particular iteration, the most likely sam-ples {ni}pi=1 were randomly propagated, to the set {n˜i}pi=1.Subsequently, these propagated samples were weighted ac-cording to π˜i ∝ dist(FW,i,FW¯,i), where ‘dist’ is the met-ric function previously deﬁned. FW,i and FW¯,i are a setof RGB points inside and outside of an image region W ,which have been projected on the Fisher plane determinedby n˜i. RegionW represents an area in the image where theobject is expected to be (just a coarse estimation). Finally,the set {n˜i, π˜i}pi=1 approximates the state of the Fisherplane, at the end of the iteration.Using this formulation, tracking was achieved in com-plex scenes (with abrupt lighting changes and clutteredbackgrounds) such as those shown in Fig. 4.4 ConclusionsIn this paper, we have introduced the Fisher colorspace,a color representation of the image points which is appro-priate for tracking tasks. The main features of this ‘object-dependent’ colorspace is that it offers a color representationwhere the distance between the target and background isframe#25 frame#50 frame#60 frame#90 frame#99frame#86 frame#95 frame#96 frame#101 frame#105Figure 4. Tracking results of complex sequencesadapting the Fisher colorspace.maximized. This property is extremely important in orderto simplify the task of any tracking algorithm. We have alsoproved that the Fisher plane offers certain robustness to il-lumination changes. Furthermore, its simple representationby only a 3D vector parameterizing the normal direction ofthe plane, permits to integrate the colorspace representationinto a more general tracking framework, and conceive it asany other object feature which may be updated throughouttime. Both synthetic and real experiments have proved thatthe Fisher colorspace is much more effective than other ex-istent colorpaces.References[1] Crowley, J., Berard, F.: Multi-modal tracking of faces forvideo communications. CVPR, 1997[2] Fukunaga, k.: Introduction to statistical pattern recognition.Academic Press, 1990[3] Hayman, E., Eklundh, J.O.: Probabilistic and voting ap-proaches to cue integration for ﬁgure-ground segmentation.ECCV, 2002[4] Khan, S., Shah, M.: Object based segmentation of video us-ing color, motion and spatial infomation. CVPR, 2001[5] Moreno-Noguer, F., Sanfeliu, A., Samaras, D.: Integra-tion of conditionally dependent object features for robust ﬁg-ure/background segmentation. ICCV, 2005[6] Nummiaro, K., Koller-Meier, E., Van Gool, L.: An adaptivecolor-based particle ﬁlter. Im. and Vision Comput., 2003[7] Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-basedprobabilistic Tracking. ECCV, 2002[8] Raja, Y., McKenna, S., Gong, S.: Color model selection andadaption in dynamic scenes. ECCV, 1998[9] Rasmussen, C., Hager, G.D.: Probabilistic data associationmethods for tracking complex visual objects. PAMI, 2001[10] Sherrah, J., Gong, S.: Continuous global evidence-basedbayesian modality fusion for simultaneous tracking of multi-ple objects. ICCV, 2001[11] Spengler, M., Schiele, B.: Towards robust multi-cue integra-tion for visual tracking. Mach. Vision & Applications, 2003[12] Wren, C., Azarbayejani, A., Darrel, T., Pentland, A.:Pﬁnder: Real-time tracking of the human body. PAMI, 1997[13] Wu, Y., Huang, T.S.: Nonstationary color tracking forvision-based human-computer interaction. Trans. Neural Net-works, 2002The 18th International Conference on Pattern Recognition (ICPR'06)0-7695-2521-0/06 $20.00  © 2006Authorized licensed use limited to: IEEE Xplore Customer. Downloaded on February 18, 2009 at 11:44 from IEEE Xplore.  Restrictions apply.