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Author	Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell
Title	Spectral sharpening by spherical sampling			Type	Journal Article
Year	2012	Publication	Journal of the Optical Society of America A	Abbreviated Journal	JOSA A
Volume	29	Issue	7	Pages	1199-1210
Keywords
Abstract	There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art.
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1084-7529	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ FVS2012			Serial	2000
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Author	Javier Vazquez; Maria Vanrell; Ramon Baldrich; Francesc Tous
Title	Color Constancy by Category Correlation			Type	Journal Article
Year	2012	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
Volume	21	Issue	4	Pages	1997-2007
Keywords
Abstract	Finding color representations which are stable to illuminant changes is still an open problem in computer vision. Until now most approaches have been based on physical constraints or statistical assumptions derived from the scene, while very little attention has been paid to the effects that selected illuminants have on the final color image representation. The novelty of this work is to propose perceptual constraints that are computed on the corrected images. We define the category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy.
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1057-7149	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ VVB2012			Serial	1999
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Author	Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson
Title	A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation			Type	Journal Article
Year	2012	Publication	Journal of Vision	Abbreviated Journal	VSS
Volume	12	Issue	6 (7)	Pages	1-14
Keywords
Abstract	When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data.
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ VOV2012			Serial	1998
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Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez
Title	Color Attributes for Object Detection			Type	Conference Article
Year	2012	Publication	25th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	3306-3313
Keywords	pedestrian detection
Abstract	State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.
Address	Providence; Rhode Island; USA;
Corporate Author				Thesis
Publisher	IEEE Xplore	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1063-6919	ISBN	978-1-4673-1226-4	Medium
Area		Expedition		Conference	CVPR
Notes	ADAS; CIC;			Approved	no
Call Number	Admin @ si @ KRW2012			Serial	1935
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Author	Maria del Camp Davesa
Title	Human action categorization in image sequences			Type	Report
Year	2011	Publication	CVC Technical Report	Abbreviated Journal
Volume	169	Issue		Pages
Keywords
Abstract
Address	Bellaterra (Spain)
Corporate Author	Computer Vision Center			Thesis	Master's thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference
Notes	CiC;CIC			Approved	no
Call Number	Admin @ si @ Dav2011			Serial	1934
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Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
Title	Categorical Focal Colours are Structurally Invariant Under Illuminant Changes			Type	Conference Article
Year	2011	Publication	European Conference on Visual Perception	Abbreviated Journal
Volume		Issue		Pages	196
Keywords
Abstract	The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes.
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title	Perception 40	Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference	ECVP
Notes	CIC			Approved	no
Call Number	Admin @ si @ RPV2011			Serial	1867
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Author	Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin
Title	Towards Automatic Concept Transfer			Type	Conference Article
Year	2011	Publication	Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering	Abbreviated Journal
Volume		Issue		Pages	167.176
Keywords	chromatic modeling, color concepts, color transfer, concept transfer
Abstract	This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study.
Address
Corporate Author				Thesis
Publisher	ACM Press	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN	978-1-4503-0907-3	Medium
Area		Expedition		Conference	NPAR
Notes	CIC			Approved	no
Call Number	Admin @ si @ MSM2011			Serial	1866
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Author	Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell
Title	Portmanteau Vocabularies for Multi-Cue Image Representation			Type	Conference Article
Year	2011	Publication	25th Annual Conference on Neural Information Processing Systems	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference	NIPS
Notes	CIC			Approved	no
Call Number	Admin @ si @ KWB2011			Serial	1865
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Author	Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title	Modulating Shape Features by Color Attention for Object Recognition			Type	Journal Article
Year	2012	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
Volume	98	Issue	1	Pages	49-64
Keywords
Abstract	Bag-of-words based image representation is a successful approach for object recognition. Generally, the subsequent stages of the process: feature detection,feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information.
Address
Corporate Author				Thesis
Publisher	Springer Netherlands	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	0920-5691	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ KWV2012			Serial	1864
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Author	Arjan Gijsenij; Theo Gevers; Joost Van de Weijer
Title	Improving Color Constancy by Photometric Edge Weighting			Type	Journal Article
Year	2012	Publication	IEEE Transaction on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
Volume	34	Issue	5	Pages	918-929
Keywords
Abstract	: Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy.
Address	Los Alamitos; CA; USA;
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	0162-8828	ISBN		Medium
Area		Expedition		Conference
Notes	CIC;ISE			Approved	no
Call Number	Admin @ si @ GGW2012			Serial	1850
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Author	Fahad Shahbaz Khan
Title	Coloring bag-of-words based image representations			Type	Book Whole
Year	2011	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	Put succinctly, the bag-of-words based image representation is the most successful approach for object and scene recognition. Within the bag-of-words framework the optimal fusion of multiple cues, such as shape, texture and color, still remains an active research domain. There exist two main approaches to combine color and shape information within the bag-of-words framework. The first approach called, early fusion, fuses color and shape at the feature level as a result of which a joint colorshape vocabulary is produced. The second approach, called late fusion, concatenates histogram representation of both color and shape, obtained independently. In the first part of this thesis, we analyze the theoretical implications of both early and late feature fusion. We demonstrate that both these approaches are suboptimal for a subset of object categories. Consequently, we propose a novel method for recognizing object categories when using multiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom-up and top-down attention maps. Subsequently, the color attention maps are used to modulate the weights of the shape features. Shape features are given more weight in regions with higher attention and vice versa. The approach is tested on several benchmark object recognition data sets and the results clearly demonstrate the effectiveness of our proposed method. In the second part of the thesis, we investigate the problem of obtaining compact spatial pyramid representations for object and scene recognition. Spatial pyramids have been successfully applied to incorporate spatial information into bag-of-words based image representation. However, a major drawback of spatial pyramids is that it leads to high dimensional image representations. We present a novel framework for obtaining compact pyramid representation. The approach reduces the size of a high dimensional pyramid representation upto an order of magnitude without any significant reduction in accuracy. Moreover, we also investigate the optimal combination of multiple features such as color and shape within the context of our compact pyramid representation. Finally, we describe a novel technique to build discriminative visual words from multiple cues learned independently from training images. To this end, we use an information theoretic vocabulary compression technique to find discriminative combinations of visual cues and the resulting visual vocabulary is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. The approach is tested on standard object recognition data sets. The results obtained clearly demonstrate the effectiveness of our approach.
Address
Corporate Author				Thesis	Ph.D. thesis
Publisher		Place of Publication		Editor	Joost Van de Weijer;Maria Vanrell
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ Kha2011			Serial	1838
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Author	Eduard Vazquez
Title	Unsupervised image segmentation based on material reflectance description and saliency			Type	Book Whole
Year	2011	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show
Address
Corporate Author				Thesis	Ph.D. thesis
Publisher		Place of Publication		Editor	Ramon Baldrich
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ Vaz2011b			Serial	1835
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Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
Title	Low-dimensional and Comprehensive Color Texture Description			Type	Journal Article
Year	2012	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
Volume	116	Issue	I	Pages	54-67
Keywords
Abstract	Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are deﬁned independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges). A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent deﬁnition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is deﬁned from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1077-3142	ISBN		Medium
Area		Expedition		Conference
Notes	CAT;CIC			Approved	no
Call Number	Admin @ si @ ASV2012			Serial	1827
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Author	Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez
Title	Discriminative Compact Pyramids for Object and Scene Recognition			Type	Journal Article
Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
Volume	45	Issue	4	Pages	1627-1636
Keywords
Abstract	Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	0031-3203	ISBN		Medium
Area		Expedition		Conference
Notes	ISE; CAT;CIC			Approved	no
Call Number	Admin @ si @ EKW2012			Serial	1807
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Author	Jesus Jaime Moreno Escobar
Title	Perceptual Criteria on Image Compresions			Type	Book Whole
Year	2011	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	Nowadays, digital images are used in many areas in everyday life, but they tend to be big. This increases amount of information leads us to the problem of image data storage. For example, it is common to have a representation a color pixel as a 24-bit number, where the channels red, green, and blue employ 8 bits each. In consequence, this kind of color pixel can specify one of 224 ¼ 16:78 million colors. Therefore, an image at a resolution of 512 £ 512 that allocates 24 bits per pixel, occupies 786,432 bytes. That is why image compression is important. An important feature of image compression is that it can be lossy or lossless. A compressed image is acceptable provided these losses of image information are not perceived by the eye. It is possible to assume that a portion of this information is redundant. Lossless Image Compression is defined as to mathematically decode the same image which was encoded. In Lossy Image Compression needs to identify two features inside the image: the redundancy and the irrelevancy of information. Thus, lossy compression modifies the image data in such a way when they are encoded and decoded, the recovered image is similar enough to the original one. How similar is the recovered image in comparison to the original image is defined prior to the compression process, and it depends on the implementation to be performed. In lossy compression, current image compression schemes remove information considered irrelevant by using mathematical criteria. One of the problems of these schemes is that although the numerical quality of the compressed image is low, it shows a high visual image quality, e.g. it does not show a lot of visible artifacts. It is because these mathematical criteria, used to remove information, do not take into account if the viewed information is perceived by the Human Visual System. Therefore, the aim of an image compression scheme designed to obtain images that do not show artifacts although their numerical quality can be low, is to eliminate the information that is not visible by the Human Visual System. Hence, this Ph.D. thesis proposes to exploit the visual redundancy existing in an image by reducing those features that can be unperceivable for the Human Visual System. First, we define an image quality assessment, which is highly correlated with the psychophysical experiments performed by human observers. The proposed CwPSNR metrics weights the well-known PSNR by using a particular perceptual low level model of the Human Visual System, e.g. the Chromatic Induction Wavelet Model (CIWaM). Second, we propose an image compression algorithm (called Hi-SET), which exploits the high correlation and self-similarity of pixels in a given area or neighborhood by means of a fractal function. Hi-SET possesses the main features that modern image compressors have, that is, it is an embedded coder, which allows a progressive transmission. Third, we propose a perceptual quantizer (½SQ), which is a modification of the uniform scalar quantizer. The ½SQ is applied to a pixel set in a certain Wavelet sub-band, that is, a global quantization. Unlike this, the proposed modification allows to perform a local pixel-by-pixel forward and inverse quantization, introducing into this process a perceptual distortion which depends on the surround spatial information of the pixel. Combining ½SQ method with the Hi-SET image compressor, we define a perceptual image compressor, called ©SET. Finally, a coding method for Region of Interest areas is presented, ½GBbBShift, which perceptually weights pixels into these areas and maintains only the more important perceivable features in the rest of the image. Results presented in this report show that CwPSNR is the best-ranked image quality method when it is applied to the most common image compression distortions such as JPEG and JPEG2000. CwPSNR shows the best correlation with the judgement of human observers, which is based on the results of psychophysical experiments obtained for relevant image quality databases such as TID2008, LIVE, CSIQ and IVC. Furthermore, Hi-SET coder obtains better results both for compression ratios and perceptual image quality than the JPEG2000 coder and other coders that use a Hilbert Fractal for image compression. Hence, when the proposed perceptual quantization is introduced to Hi-SET coder, our compressor improves its numerical and perceptual e±ciency. When ½GBbBShift method applied to Hi-SET is compared against MaxShift method applied to the JPEG2000 standard and Hi-SET, the images coded by our ROI method get the best results when the overall image quality is estimated. Both the proposed perceptual quantization and the ½GBbBShift method are generalized algorithms that can be applied to other Wavelet based image compression algorithms such as JPEG2000, SPIHT or SPECK.
Address
Corporate Author				Thesis	Ph.D. thesis
Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Xavier Otazu
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN	978-84-938351-3-2	Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ Mor2011			Serial	1786
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Author	Javier Vazquez
Title	Colour Constancy in Natural Through Colour Naming and Sensor Sharpening			Type	Book Whole
Year	2011	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	Colour is derived from three physical properties: incident light, object reflectance and sensor sensitivities. Incident light varies under natural conditions; hence, recovering scene illuminant is an important issue in computational colour. One way to deal with this problem under calibrated conditions is by following three steps, 1) building a narrow-band sensor basis to accomplish the diagonal model, 2) building a feasible set of illuminants, and 3) defining criteria to select the best illuminant. In this work we focus on colour constancy for natural images by introducing perceptual criteria in the first and third stages. To deal with the illuminant selection step, we hypothesise that basic colour categories can be used as anchor categories to recover the best illuminant. These colour names are related to the way that the human visual system has evolved to encode relevant natural colour statistics. Therefore the recovered image provides the best representation of the scene labelled with the basic colour terms. We demonstrate with several experiments how this selection criterion achieves current state-of-art results in computational colour constancy. In addition to this result, we psychophysically prove that usual angular error used in colour constancy does not correlate with human preferences, and we propose a new perceptual colour constancy evaluation. The implementation of this selection criterion strongly relies on the use of a diagonal model for illuminant change. Consequently, the second contribution focuses on building an appropriate narrow-band sensor basis to represent natural images. We propose to use the spectral sharpening technique to compute a unique narrow-band basis optimised to represent a large set of natural reflectances under natural illuminants and given in the basis of human cones. The proposed sensors allow predicting unique hues and the World colour Survey data independently of the illuminant by using a compact singularity function. Additionally, we studied different families of sharp sensors to minimise different perceptual measures. This study brought us to extend the spherical sampling procedure from 3D to 6D. Several research lines still remain open. One natural extension would be to measure the effects of using the computed sharp sensors on the category hypothesis, while another might be to insert spatial contextual information to improve category hypothesis. Finally, much work still needs to be done to explore how individual sensors can be adjusted to the colours in a scene.
Address
Corporate Author				Thesis	Ph.D. thesis
Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Maria Vanrell;Graham D. Finlayson
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ Vaz2011a			Serial	1785
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Author	Shida Beigpour; Joost Van de Weijer
Title	Object Recoloring Based on Intrinsic Image Estimation			Type	Conference Article
Year	2011	Publication	13th IEEE International Conference in Computer Vision	Abbreviated Journal
Volume		Issue		Pages	327 - 334
Keywords
Abstract	Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods.
Address	Barcelona
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1550-5499	ISBN	978-1-4577-1101-5	Medium
Area		Expedition		Conference	ICCV
Notes	CIC			Approved	no
Call Number	Admin @ si @ BeW2011			Serial	1781
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Author	Joost Van de Weijer; Shida Beigpour
Title	The Dichromatic Reflection Model: Future Research Directions and Applications			Type	Conference Article
Year	2011	Publication	International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications	Abbreviated Journal
Volume		Issue		Pages
Keywords	dblp
Abstract	The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.
Address	Algarve, Portugal
Corporate Author				Thesis
Publisher	SciTePress	Place of Publication		Editor	Mestetskiy, Leonid and Braz, José
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN	978-989-8425-47-8	Medium
Area		Expedition		Conference	VISIGRAPP
Notes	CIC			Approved	no
Call Number	Admin @ si @ WeB2011			Serial	1778
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