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| Author | Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal | ||||
| Title | Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing | Type | Journal Article | ||
| Year | 2014 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 72 | Issue  |
1 | Pages | 515-539 |
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| Abstract | In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ SDT2014 | Serial | 2357 | ||
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| Author | Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo | ||||
| Title | Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 50 | Issue |
1 | Pages | 112-121 |
| Keywords | RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition | ||||
| Abstract | PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach. |
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| Notes | HuPBA;MV; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ HBP2014 | Serial | 2353 | ||
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| Author | Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca | ||||
| Title | Toward Real-Time Pedestrian Detection Based on a Deformable Template Model | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 15 | Issue |
1 | Pages | 355-364 |
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| Abstract | Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1524-9050 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; 601.213; 600.078 | Approved | no | ||
| Call Number | PGH2014 | Serial | 2350 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Embedding new observations via sparse-coding for non-linear manifold learning | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue |
1 | Pages | 480-492 |
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| Abstract | Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. | ||||
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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| Author | Naveen Onkarappa; Angel Sappa | ||||
| Title | Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 15 | Issue |
1 | Pages | 136-147 |
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| Abstract | IF: 3.064
Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields. |
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| ISSN | 1524-9050 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OnS2014a | Serial | 2386 | ||
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| Author | Cesar Isaza; Joaquin Salas; Bogdan Raducanu | ||||
| Title | Rendering ground truth data sets to detect shadows cast by static objects in outdoors | Type | Journal Article | ||
| Year | 2014 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 70 | Issue |
1 | Pages | 557-571 |
| Keywords | Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection | ||||
| Abstract | In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ ISR2014 | Serial | 2229 | ||
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| Author | Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou | ||||
| Title | Multi-Illuminant Estimation with Conditional Random Fields | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 23 | Issue |
1 | Pages | 83-95 |
| Keywords | color constancy; CRF; multi-illuminant | ||||
| Abstract | Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ BRW2014 | Serial | 2451 | ||
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| Author | Josep Llados; Marçal Rusiñol | ||||
| Title | Graphics Recognition Techniques | Type | Book Chapter | ||
| Year | 2014 | Publication | Handbook of Document Image Processing and Recognition | Abbreviated Journal | |
| Volume | D | Issue |
Pages | 489-521 | |
| Keywords | Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation | ||||
| Abstract | This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process. | ||||
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| Publisher | Springer London | Place of Publication | Editor | D. Doermann; K. Tombre | |
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| ISSN | ISBN | 978-0-85729-858-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ LlR2014 | Serial | 2380 | ||
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| Author | Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | An Overview of Symbol Recognition | Type | Book Chapter | ||
| Year | 2014 | Publication | Handbook of Document Image Processing and Recognition | Abbreviated Journal | |
| Volume | D | Issue |
Pages | 523-551 | |
| Keywords | Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting | ||||
| Abstract | According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity. | ||||
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| Publisher | Springer London | Place of Publication | Editor | D. Doermann; K. Tombre | |
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| ISSN | ISBN | 978-0-85729-858-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ TaT2014 | Serial | 2489 | ||
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| Author | Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal | ||||
| Title | A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue |
Pages | 7-11 | |
| Keywords | Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel | ||||
| Abstract | Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Bart Lamiroy; Jean-Marc Ogier | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-662-44853-3 | Medium | |
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DLB2014 | Serial | 2698 | ||
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| Author | David Geronimo; Antonio Lopez | ||||
| Title | Vision-based Pedestrian Protection Systems for Intelligent Vehicles | Type | Book Whole | ||
| Year | 2014 | Publication | SpringerBriefs in Computer Science | Abbreviated Journal | |
| Volume | Issue |
Pages | 1-114 | ||
| Keywords | Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users | ||||
| Abstract | Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented. | ||||
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| Publisher | Springer Briefs in Computer Vision | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-1-4614-7986-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | GeL2014 | Serial | 2325 | ||
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| Author | Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez | ||||
| Title | Moving Cast Shadows Detection Methods for Video Surveillance Applications | Type | Book Chapter | ||
| Year | 2014 | Publication | Augmented Vision and Reality | Abbreviated Journal | |
| Volume | 6 | Issue |
Pages | 23-47 | |
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| Abstract | Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 2190-5916 | ISBN | 978-3-642-37840-9 | Medium | |
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| Notes | ISE; 605.203; 600.049; 302.018; 302.012; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ AHM2014 | Serial | 2223 | ||
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| Author | Adria Ruiz; Joost Van de Weijer; Xavier Binefa | ||||
| Title | Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization | Type | Conference Article | ||
| Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. | ||||
| Address | Nottingham; UK; September 2014 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; CIC; 600.074; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ RWB2014 | Serial | 2508 | ||
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| Author | A.Kesidis; Dimosthenis Karatzas | ||||
| Title | Logo and Trademark Recognition | Type | Book Chapter | ||
| Year | 2014 | Publication | Handbook of Document Image Processing and Recognition | Abbreviated Journal | |
| Volume | D | Issue |
Pages | 591-646 | |
| Keywords | Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems | ||||
| Abstract | The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. | ||||
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| Publisher | Springer London | Place of Publication | Editor | D. Doermann; K. Tombre | |
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| ISSN | ISBN | 978-0-85729-858-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ KeK2014 | Serial | 2425 | ||
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| Author | Patricia Marquez; Debora Gil; R.Mester; Aura Hernandez-Sabate | ||||
| Title | Local Analysis of Confidence Measures for Optical Flow Quality Evaluation | Type | Conference Article | ||
| Year | 2014 | Publication | 9th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 3 | Issue |
Pages | 450-457 | |
| Keywords | Optical Flow; Confidence Measure; Performance Evaluation. | ||||
| Abstract | Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance. |
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| Address | Lisboa; January 2014 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | IAM; ADAS; 600.044; 600.060; 600.057; 601.145; 600.076; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ MGM2014 | Serial | 2432 | ||
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