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| Author | Jesus Jaime Moreno Escobar |
 
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| Title | Perceptual Criteria on Image Compresions | Type | Book Whole | |||
| Year | 2011 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal  |
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| 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. | |||||
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu | ||
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| 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 | Eduard Vazquez |
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| 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 |
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| 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 | |||||
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Place of Publication | Editor | Ramon Baldrich | |||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Vaz2011b | Serial | 1835 | |||
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| Author | Fahad Shahbaz Khan |
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| Title | Coloring bag-of-words based image representations | Type | Book Whole | |||
| Year | 2011 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal |
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| 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. | |||||
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Place of Publication | Editor | Joost Van de Weijer;Maria Vanrell | |||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Kha2011 | Serial | 1838 | |||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez |
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| Title | Color Attributes for Object Detection | Type | Conference Article | |||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal |
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| 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. |
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| Address | Providence; Rhode Island; USA; | |||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | ||||
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| 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 | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell |
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| Title | Portmanteau Vocabularies for Multi-Cue Image Representation | Type | Conference Article | |||
| Year | 2011 | Publication | 25th Annual Conference on Neural Information Processing Systems | Abbreviated Journal |
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| 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 | |||||
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| Area | Expedition | Conference | NIPS | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ KWB2011 | Serial | 1865 | |||
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| Author | Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin |
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| 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 |
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| 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. | |||||
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| Publisher | ACM Press | Place of Publication | Editor | |||
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| 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 | Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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| Title | Categorical Focal Colours are Structurally Invariant Under Illuminant Changes | Type | Conference Article | |||
| Year | 2011 | Publication | European Conference on Visual Perception | Abbreviated Journal |
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| Volume | Issue | Pages | 196 | |||
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| 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. | |||||
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| Series Editor | Series Title | Perception 40 | Abbreviated Series Title | |||
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| Area | Expedition | Conference | ECVP | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ RPV2011 | Serial | 1867 | |||
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| Author | Naila Murray |
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| Title | Perceptual Feature Detection | Type | Report | |||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal |
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| Volume | 131 | Issue | Pages | |||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | |||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Mur2009 | Serial | 2390 | |||
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| Author | Maria del Camp Davesa |
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| Title | Human action categorization in image sequences | Type | Report | |||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal |
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| Volume | 169 | Issue | Pages | |||
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| Address | Bellaterra (Spain) | |||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | |||
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| Notes | CiC;CIC | Approved | no | |||
| Call Number | Admin @ si @ Dav2011 | Serial | 1934 | |||
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| Author | Joost Van de Weijer; Fahad Shahbaz Khan |
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| Title | Fusing Color and Shape for Bag-of-Words Based Object Recognition | Type | Conference Article | |||
| Year | 2013 | Publication | 4th Computational Color Imaging Workshop | Abbreviated Journal |
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| Volume | 7786 | Issue | Pages | 25-34 | ||
| Keywords | Object Recognition; color features; bag-of-words; image classification | |||||
| Abstract | In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. | |||||
| Address | Chiba; Japan; March 2013 | |||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | |||
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| ISSN | 0302-9743 | ISBN | 978-3-642-36699-4 | Medium | ||
| Area | Expedition | Conference | CCIW | |||
| Notes | CIC; 600.048 | Approved | no | |||
| Call Number | Admin @ si @ WeK2013 | Serial | 2283 | |||
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| Author | Joost Van de Weijer; Fahad Shahbaz Khan; Marc Masana |
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| Title | Interactive Visual and Semantic Image Retrieval | Type | Book Chapter | |||
| Year | 2013 | Publication | Multimodal Interaction in Image and Video Applications | Abbreviated Journal |
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| Volume | 48 | Issue | Pages | 31-35 | ||
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| Abstract | One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces. In this chapter we investigate how high-level information provided by recently developed object recognition techniques can improve interactive image retrieval. Wel apply a bagof- word based image representation method to automatically classify images in a number of categories. These additional labels are then applied to improve the image retrieval system. Next to these high-level semantic labels, we also apply a low-level image description to describe the composition and color scheme of the scene. Both descriptions are incorporated in a user feedback image retrieval setting. The main objective is to show that automatic labeling of images with semantic labels can improve image retrieval results. | |||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Angel Sappa; Jordi Vitria | ||
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| ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | ||
| Area | Expedition | Conference | ||||
| Notes | CIC; 605.203; 600.048 | Approved | no | |||
| Call Number | Admin @ si @ WKC2013 | Serial | 2284 | |||
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| Author | Albert Gordo |
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| Title | A Cyclic Page Layout Descriptor for Document Classification & Retrieval | Type | Report | |||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal |
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| Volume | 128 | Issue | Pages | |||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | |||
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| Notes | CIC;DAG | Approved | no | |||
| Call Number | Admin @ si @ Gor2009 | Serial | 2387 | |||
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| Author | Naila Murray; Luca Marchesotti; Florent Perronnin |
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| Title | AVA: A Large-Scale Database for Aesthetic Visual Analysis | Type | Conference Article | |||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal |
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| Volume | Issue | Pages | 2408-2415 | |||
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| Abstract | With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks | |||||
| Address | Providence, Rhode Islan | |||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | |||
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| Series Volume | Series Issue | Edition | ||||
| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MMP2012a | Serial | 2025 | |||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
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| Title | Names and Shades of Color for Intrinsic Image Estimation | Type | Conference Article | |||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal |
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| Volume | Issue | Pages | 278-285 | |||
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| Abstract | In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. | |||||
| Address | Providence, Rhode Island | |||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | |||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ SPB2012 | Serial | 2026 | |||
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| Author | Naila Murray; Luca Marchesotti; Florent Perronnin |
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| Title | Learning to Rank Images using Semantic and Aesthetic Labels | Type | Conference Article | |||
| Year | 2012 | Publication | 23rd British Machine Vision Conference | Abbreviated Journal |
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| Volume | Issue | Pages | 110.1-110.10 | |||
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| Abstract | Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one. | |||||
| Address | Guildford, London | |||||
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| ISSN | ISBN | 1-901725-46-4 | Medium | |||
| Area | Expedition | Conference | BMVC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MMP2012b | Serial | 2027 | |||
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| Author | Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus |
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| Title | Color Naming | Type | Book Chapter | |||
| Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal |
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| Volume | Issue | 17 | Pages | 287-317 | ||
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| Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek | ||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ WBV2012 | Serial | 2063 | |||
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| Author | Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez |
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| Title | Adaptation of a computer programming course to the EXHE requirements: evaluation five years later | Type | Miscellaneous | |||
| Year | 2012 | Publication | European Journal of Engineering Education | Abbreviated Journal |
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| Volume | 37 | Issue | 3 | Pages | 243-254 | |
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| Notes | DAG; CIC; OR; invisible;MV | Approved | no | |||
| Call Number | Admin @ si @ VBL2012 | Serial | 2070 | |||
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| Author | Shida Beigpour |
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| Title | Illumination and object reflectance modeling | Type | Book Whole | |||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal |
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| Abstract | More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information. | |||||
| Address | Barcelona | |||||
| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ernest Valveny | ||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Bei2013 | Serial | 2267 | |||
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