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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
J.R. Serra; J.B. Subirana |
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Title |
Extraccion de estructuras interesantes en imagenes |
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Report |
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1996 |
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CVC Tecnical Report #14 |
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Admin @ si @ SeS1996c |
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216 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
J.R. Serra; J.B. Subirana |
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Title |
Perceptual Grouping on Texture Images Using Non-Cartesian Networks |
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1996 |
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IEEE International Conference on Pattern Recognition. Vol B, pp. 462–466 |
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Admin @ si @ SeS1996a |
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217 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
J.R. Serra; J.B. Subirana |
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Title |
Perceptual grouping on texture images using non-cartesian networks |
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1996 |
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CVC Technical Report #11 |
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Admin @ si @ SeS1996b |
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218 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
J.R. Serra; S. Casadei; J.B. Subirana |
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Title |
Non-Cartesian Networks for Middle Level Vision. |
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Miscellaneous |
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1995 |
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VI National Simposium on Pattern Recognition and image Analysis |
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Cordoba |
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Admin @ si @ SCS1995 |
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232 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
![goto web page url](img/www.gif)
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Title |
Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
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Journal Article |
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2013 |
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Expert Systems with Applications |
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EXWA |
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40 |
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17 |
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6707-6712 |
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Neural gas; Expert vision; Eye-tracking; Fixations |
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Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
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0957-4174 |
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ADAS |
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no |
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Admin @ si @ CRM2013 |
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2438 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaime Lopez-Krahe; Josep Llados; Enric Marti |
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Architectural Floor Plan Analysis |
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2000 |
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CVonline |
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Edimburg, UK |
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University of Edinburgh |
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Robert B. Fisher |
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online pdf |
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DAG;IAM |
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IAM @ iam @ LLM2000 |
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1561 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaime Moreno |
![goto web page url](img/www.gif)
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Title |
Perceptual Criteria on Image Compresions |
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Book Whole |
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2011 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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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Ph.D. thesis |
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Ediciones Graficas Rey |
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Xavier Otazu |
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978-84-938351-3-2 |
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CIC |
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no |
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Admin @ si @ Mor2011 |
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1786 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaime Moreno; Xavier Otazu |
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Title |
Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder |
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Conference Article |
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2011 |
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IEEE International Conference on Multimedia and Expo |
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1-6 |
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In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder. |
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1945-7871 |
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978-1-61284-348-3 |
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ICME |
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Admin @ si @ MoO2011a |
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2176 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaime Moreno; Xavier Otazu |
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Title |
Image coder based on Hilbert scanning of embedded quadTrees |
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Conference Article |
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2011 |
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Data Compression Conference |
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470-470 |
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In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. |
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Admin @ si @ MoO2011b |
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2177 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaime Moreno; Xavier Otazu; Maria Vanrell |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Local Perceptual Weighting in JPEG2000 for Color Images |
Type |
Conference Article |
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2010 |
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5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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255–260 |
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The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM (Chromatic Induction Wavelet Model). |
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Joensuu, Finland |
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9781617388897 |
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CGIV/MCS |
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CAT @ cat @ MOV2010a |
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1307 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaime Moreno; Xavier Otazu; Maria Vanrell |
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Title |
Contribution of CIWaM in JPEG2000 Quantization for Color Images |
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Conference Article |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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132–136 |
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The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM(ChromaticInductionWaveletModel). |
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Gjovik (Norway) |
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CREATE |
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no |
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CAT @ cat @ MOV2010b |
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1308 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Selective Search for Object Recognition |
Type |
Journal Article |
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Year |
2013 |
Publication |
International Journal of Computer Vision |
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IJCV |
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104 |
Issue |
2 |
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154-171 |
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This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). |
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0920-5691 |
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ALTRES;ISE |
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no |
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Admin @ si @ USG2013 |
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2362 |
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Jaume Amores |
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Title |
Multiple Instance Classification: review, taxonomy and comparative study |
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Journal Article |
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2013 |
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Artificial Intelligence |
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AI |
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201 |
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81-105 |
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Multi-instance learning; Codebook; Bag-of-Words |
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Abstract |
Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
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Elsevier Science Publishers Ltd. Essex, UK |
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0004-3702 |
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ADAS; 601.042; 600.057 |
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Admin @ si @ Amo2013 |
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2273 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaume Amores |
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Title |
MILDE: multiple instance learning by discriminative embedding |
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Journal Article |
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2015 |
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Knowledge and Information Systems |
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KAIS |
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42 |
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2 |
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381-407 |
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Multi-instance learning; Codebook; Bag of words |
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While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
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Springer London |
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0219-1377 |
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ADAS; 601.042; 600.057; 600.076 |
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Admin @ si @ Amo2015 |
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2383 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
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Conference Article |
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Year |
2010 |
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20th International Conference on Pattern Recognition |
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4246–4250 |
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Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance. |
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Istanbul, Turkey |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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ADAS |
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ADAS @ adas @ Amo2010 |
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1295 |
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