| Records |
| Author |
Mikhail Mozerov |
| Title |
Constrained Optical Flow Estimation as a Matching Problem |
Type |
Journal Article |
| Year |
2013 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
22 |
Issue |
5 |
Pages |
2044-2055 |
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| Abstract |
In general, discretization in the motion vector domain yields an intractable number of labels. In this paper we propose an approach that can reduce general optical flow to the constrained matching problem by pre-estimating a 2D disparity labeling map of the desired discrete motion vector function. One of the goals of the proposed paper is estimating coarse distribution of motion vectors and then utilizing this distribution as global constraints for discrete optical flow estimation. This pre-estimation is done with a simple frame-to-frame correlation technique also known as the digital symmetric-phase-only-filter (SPOF). We discover a strong correlation between the output of the SPOF and the motion vector distribution of the related optical flow. The two step matching paradigm for optical flow estimation is applied: pixel accuracy (integer flow), and subpixel accuracy estimation. The matching problem is solved by global optimization. Experiments on the Middlebury optical flow datasets confirm our intuitive assumptions about strong correlation between motion vector distribution of optical flow and maximal peaks of SPOF outputs. The overall performance of the proposed method is promising and achieves state-of-the-art results on the Middlebury benchmark. |
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1057-7149 |
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ISE |
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no |
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Admin @ si @ Moz2013 |
Serial |
2191 |
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| Author |
Mikhail Mozerov; Joost Van de Weijer |
| Title |
One-view occlusion detection for stereo matching with a fully connected CRF model |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
28 |
Issue |
6 |
Pages |
2936-2947 |
| Keywords |
Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter |
| Abstract |
In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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LAMP; 600.098; 600.109; 602.133; 600.120 |
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no |
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Admin @ si @ MoW2019 |
Serial |
3221 |
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| Author |
Mikhail Mozerov; Joost Van de Weijer |
| Title |
Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering |
Type |
Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
24 |
Issue |
12 |
Pages |
5842-5853 |
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| Abstract |
The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. |
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1057-7149 |
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LAMP; 600.079;ISE |
Approved |
no |
| Call Number |
Admin @ si @ MoW2015b |
Serial |
2689 |
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| Author |
Mikhail Mozerov; Joost Van de Weijer |
| Title |
Accurate stereo matching by two step global optimization |
Type |
Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
24 |
Issue |
3 |
Pages |
1153-1163 |
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| Abstract |
In stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques. Due to their global extend energy minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost filtering approaches obtain better results. In this paper we intend to combine both approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost fil tering methods. Based on this observation we propose to perform stereo matching as a two-step energy minimization algorithm. We consider two MRF models: a fully connected model defined on the complete set of pixels in an image and a conventional locally connected model. We solve the energy minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo datasets show that the proposed method achieves state-of-the-arts results. |
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1057-7149 |
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ISE; LAMP; 600.079; 600.078 |
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no |
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Admin @ si @ MoW2015a |
Serial |
2568 |
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| Author |
Ahmed Mounir Gad |
| Title |
Object Localization Enhancement by Multiple Segmentation Fusion |
Type |
Report |
| Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
152 |
Issue |
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Thesis |
Master's thesis |
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no |
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Admin @ si @ Mou2010 |
Serial |
1346 |
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| Author |
C. Molina; J.B. Subirana |
| Title |
Polynomial-Time Algorithm for 2D object recognition. |
Type |
Miscellaneous |
| Year |
1995 |
Publication |
VI National Simposium on Pattern Recognition and image Analysis |
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Cordoba |
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Admin @ si @ MoS1995b |
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231 |
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| Author |
C. Molina; J.B. Subirana |
| Title |
Reduction of complexity for object recognition algorithms |
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Report |
| Year |
1995 |
Publication |
CVC Technical Report #03 |
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CVC (UAB) |
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Admin @ si @ MoS1995a |
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225 |
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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 |
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Issue |
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Pages |
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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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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Xavier Otazu |
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ISBN |
978-84-938351-3-2 |
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CIC |
Approved |
no |
| Call Number |
Admin @ si @ Mor2011 |
Serial |
1786 |
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| Author |
Jaime Moreno; Xavier Otazu |
| Title |
Image coder based on Hilbert scanning of embedded quadTrees |
Type |
Conference Article |
| Year |
2011 |
Publication |
Data Compression Conference |
Abbreviated Journal |
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Issue |
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Pages |
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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DCC |
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CIC |
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no |
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Admin @ si @ MoO2011b |
Serial |
2177 |
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| Author |
Jaime Moreno; Xavier Otazu |
| Title |
Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE International Conference on Multimedia and Expo |
Abbreviated Journal |
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Issue |
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Pages |
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 |
ISBN |
978-1-61284-348-3 |
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Conference |
ICME |
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CIC |
Approved |
no |
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Admin @ si @ MoO2011a |
Serial |
2176 |
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| Author |
David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
| Title |
Automated Prediction of Preferences Using Facial Expressions |
Type |
Journal Article |
| Year |
2014 |
Publication |
PloS one |
Abbreviated Journal |
Plos |
| Volume |
9 |
Issue |
2 |
Pages |
e87434 |
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We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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OR;MV |
Approved |
no |
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Admin @ si @ MNT2014 |
Serial |
2453 |
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| Author |
Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva |
| Title |
Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN |
Type |
Journal Article |
| Year |
2023 |
Publication |
Food Control |
Abbreviated Journal |
FC |
| Volume |
147 |
Issue |
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Pages |
109554 |
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| Abstract |
Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. |
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MILAB |
Approved |
no |
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Admin @ si @ MNJ2023 |
Serial |
3882 |
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| Author |
Naila Murray; Luca Marchesotti; Florent Perronnin |
| Title |
Learning to Rank Images using Semantic and Aesthetic Labels |
Type |
Conference Article |
| Year |
2012 |
Publication |
23rd British Machine Vision Conference |
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Issue |
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Pages |
110.1-110.10 |
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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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ISBN |
1-901725-46-4 |
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Conference |
BMVC |
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CIC |
Approved |
no |
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Admin @ si @ MMP2012b |
Serial |
2027 |
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| Author |
Naila Murray; Luca Marchesotti; Florent Perronnin |
| 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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Issue |
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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 |
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Providence, Rhode Islan |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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no |
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Admin @ si @ MMP2012a |
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2025 |
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| Author |
Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach |
| Title |
Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge |
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Journal Article |
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2022 |
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Gastrointestinal Endoscopy |
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96 |
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2 |
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370-372 |
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ISE |
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Admin @ si @ MMB2022 |
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3701 |
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