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| Author | Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva | ||||
| Title | Circular Blurred Shape Model for Multiclass Symbol Recognition | Type | Journal Article | ||
| Year | 2011 | Publication  |
IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) | Abbreviated Journal | TSMCB |
| Volume | 41 | Issue | 2 | Pages | 497-506 |
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| Abstract | In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1083-4419 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB; DAG;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EFP2011 | Serial | 1784 | ||
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| Author | Arjan Gijsenij; Theo Gevers | ||||
| Title | Color Constancy Using Natural Image Statistics and Scene Semantics | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 33 | Issue | 4 | Pages | 687-698 |
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| Abstract | Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ GiG2011 | Serial | 1724 | ||
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| Author | Koen E.A. van de Sande; Theo Gevers; Cees G.M. Snoek | ||||
| Title | Empowering Visual Categorization with the GPU | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
| Volume | 13 | Issue | 1 | Pages | 60-70 |
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| Abstract | Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe drawback of this model is its high computational cost. As the trend to increase computational power in newer CPU and GPU architectures is to increase their level of parallelism, exploiting this parallelism becomes an important direction to handle the computational cost of the bag-of-words approach. When optimizing a system based on the bag-of-words approach, the goal is to minimize the time it takes to process batches of images. Additionally, we also consider power usage as an evaluation metric. In this paper, we analyze the bag-of-words model for visual categorization in terms of computational cost and identify two major bottlenecks: the quantization step and the classification step. We address these two bottlenecks by proposing two efficient algorithms for quantization and classification by exploiting the GPU hardware and the CUDA parallel programming model. The algorithms are designed to (1) keep categorization accuracy intact, (2) decompose the problem and (3) give the same numerical results. In the experiments on large scale datasets it is shown that, by using a parallel implementation on the Geforce GTX260 GPU, classifying unseen images is 4.8 times faster than a quad-core CPU version on the Core i7 920, while giving the exact same numerical results. In addition, we show how the algorithms can be generalized to other applications, such as text retrieval and video retrieval. Moreover, when the obtained speedup is used to process extra video frames in a video retrieval benchmark, the accuracy of visual categorization is improved by 29%. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ SGS2011b | Serial | 1729 | ||
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| Author | Maria Salamo; Sergio Escalera | ||||
| Title | Increasing Retrieval Quality in Conversational Recommenders | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Knowledge and Data Engineering | Abbreviated Journal | TKDE |
| Volume | 99 | Issue | Pages | 1-1 | |
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| Abstract | IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851 A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches |
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| Publisher | IEEE | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1041-4347 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ SaE2011 | Serial | 1713 | ||
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| Author | Jose Manuel Alvarez; Antonio Lopez | ||||
| Title | Road Detection Based on Illuminant Invariance | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 12 | Issue | 1 | Pages | 184-193 |
| Keywords | road detection | ||||
| Abstract | By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ AlL2011 | Serial | 1456 | ||
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| Author | Fadi Dornaika; Jose Manuel Alvarez; Angel Sappa; Antonio Lopez | ||||
| Title | A New Framework for Stereo Sensor Pose through Road Segmentation and Registration | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 12 | Issue | 4 | Pages | 954-966 |
| Keywords | road detection | ||||
| Abstract | This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework. | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1524-9050 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ DAS2011; ADAS @ adas @ das2011a | Serial | 1833 | ||
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| Author | Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez | ||||
| Title | Video Alignment for Change Detection | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 20 | Issue | 7 | Pages | 1858-1869 |
| Keywords | video alignment | ||||
| Abstract | In this work, we address the problem of aligning two video sequences. Such alignment refers to synchronization, i.e., the establishment of temporal correspondence between frames of the first and second video, followed by spatial registration of all the temporally corresponding frames. Video synchronization and alignment have been attempted before, but most often in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, restrictive assumptions have been applied, including linear time correspondence or the knowledge of the complete trajectories of corresponding scene points; to some extent, these assumptions limit the practical applicability of any solutions developed. We intend to solve the more general problem of aligning video sequences recorded by independently moving cameras that follow similar trajectories, based only on the fusion of image intensity and GPS information. The novelty of our approach is to pose the synchronization as a MAP inference problem on a Bayesian network including the observations from these two sensor types, which have been proved complementary. Alignment results are presented in the context of videos recorded from vehicles driving along the same track at different times, for different road types. In addition, we explore two applications of the proposed video alignment method, both based on change detection between aligned videos. One is the detection of vehicles, which could be of use in ADAS. The other is online difference spotting videos of surveillance rounds. | ||||
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| Notes | ADAS; IF | Approved | no | ||
| Call Number | DPS 2011; ADAS @ adas @ dps2011 | Serial | 1705 | ||
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| Author | Ariel Amato; Mikhail Mozerov; Andrew Bagdanov; Jordi Gonzalez | ||||
| Title | Accurate Moving Cast Shadow Suppression Based on Local Color Constancy detection | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 20 | Issue | 10 | Pages | 2954 - 2966 |
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| Abstract | This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions. | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ AMB2011 | Serial | 1716 | ||
| Permanent link to this record | |||||
| Author | Arjan Gijsenij; Theo Gevers; Joost Van de Weijer | ||||
| Title | Computational Color Constancy: Survey and Experiments | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 20 | Issue | 9 | Pages | 2475-2489 |
| Keywords | computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting | ||||
| Abstract | Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE;CIC | Approved | no | ||
| Call Number | Admin @ si @ GGW2011 | Serial | 1717 | ||
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| Author | Jose Seabra; Francesco Ciompi; Oriol Pujol; J. Mauri; Petia Radeva; Joao Sanchez | ||||
| Title | Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Biomedical Engineering | Abbreviated Journal | TBME |
| Volume | 58 | Issue | 5 | Pages | 1314-1324 |
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| Abstract | Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology. | ||||
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ SCP2011 | Serial | 1712 | ||
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| Author | Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell | ||||
| Title | Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 33 | Issue | 5 | Pages | 917-930 |
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| Abstract | The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. | ||||
| Address | Los Alamitos; CA; USA; | ||||
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| Publisher | IEEE Computer Society | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ VBW2011 | Serial | 1715 | ||
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| Author | Mario Rojas; David Masip; Jordi Vitria | ||||
| Title | Predicting Dominance Judgements Automatically: A Machine Learning Approach. | Type | Conference Article | ||
| Year | 2011 | Publication |
IEEE International Workshop on Social Behavior Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 939-944 | ||
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| Abstract | The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task. | ||||
| Address | Santa Barbara, CA | ||||
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| ISSN | ISBN | 978-1-4244-9140-7 | Medium | ||
| Area | Expedition | Conference | SBA | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RMV2011b | Serial | 1760 | ||
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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 | |
| Volume | Issue | Pages | 1-6 | ||
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| Abstract | 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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| ISSN | 1945-7871 | ISBN | 978-1-61284-348-3 | Medium | |
| Area | Expedition | Conference | ICME | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MoO2011a | Serial | 2176 | ||
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| Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate | ||||
| Title | A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth | Type | Conference Article | ||
| Year | 2011 | Publication |
IEEE International Conference on Computer Vision – Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2042-2049 | ||
| Keywords | IEEE International Conference on Computer Vision – Workshops | ||||
| Abstract | Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems. | ||||
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| Publisher | IEEE | Place of Publication | Barcelona (Spain) | Editor | |
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | ICCVW | ||
| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ MGH2011 | Serial | 1682 | ||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga | ||||
| Title | Saliency Estimation Using a Non-Parametric Low-Level Vision Model | Type | Conference Article | ||
| Year | 2011 | Publication |
IEEE conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 433-440 | ||
| Keywords | Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms | ||||
| Abstract | Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. | ||||
| Address | Colorado Springs | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4577-0394-2 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MVO2011 | Serial | 1757 | ||
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