Jorge Bernal, Debora Gil, Carles Sanchez, & F. Javier Sanchez. (2014). Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis. In 1st MICCAI Workshop on Computer-Assisted and Robotic Endoscopy (Vol. 8899, pp. 1–10). LNCS. Springer International Publishing.
Abstract: In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation.
Keywords: Image Segmentation; Polyps, Colonoscopy; Valley Information; Energy Maps
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Francesco Brughi, Debora Gil, Llorenç Badiella, Eva Jove Casabella, & Oriol Ramos Terrades. (2014). Exploring the impact of inter-query variability on the performance of retrieval systems. In 11th International Conference on Image Analysis and Recognition (Vol. 8814, 413–420). LNCS. Springer International Publishing.
Abstract: This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes.
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Oualid M. Benkarim, Petia Radeva, & Laura Igual. (2014). Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation. In 8th Conference on Articulated Motion and Deformable Objects (Vol. 8563, pp. 138–147). LNCS. Springer International Publishing.
Abstract: The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
Keywords: MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classication
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Marc Bolaños, Maite Garolera, & Petia Radeva. (2014). Video Segmentation of Life-Logging Videos. In 8th Conference on Articulated Motion and Deformable Objects (Vol. 8563, pp. 1–9).
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Juan Ramon Terven Salinas, Joaquin Salas, & Bogdan Raducanu. (2014). Robust Head Gestures Recognition for Assistive Technology. In Pattern Recognition (Vol. 8495, pp. 152–161). LNCS. Springer International Publishing.
Abstract: This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
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David Geronimo, & Antonio Lopez. (2014). Vision-based Pedestrian Protection Systems for Intelligent Vehicles. Springer Briefs in Computer Vision.
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.
Keywords: Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users
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C. Alejandro Parraga. (2014). Color Vision, Computational Methods for. In Dieter Jaeger, & Ranu Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–11). Springer-Verlag Berlin Heidelberg.
Abstract: The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.
Keywords: Color computational vision; Computational neuroscience of color
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Svebor Karaman, Giuseppe Lisanti, Andrew Bagdanov, & Alberto del Bimbo. (2014). From re-identification to identity inference: Labeling consistency by local similarity constraints. In Person Re-Identification (Vol. 2, pp. 287–307). Springer London.
Abstract: In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery.
Keywords: re-identification; Identity inference; Conditional random fields; Video surveillance
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Alicia Fornes, & Gemma Sanchez. (2014). Analysis and Recognition of Music Scores. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. E, pp. 749–774). Springer London.
Abstract: The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented.
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Josep Llados, & Marçal Rusiñol. (2014). Graphics Recognition Techniques. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 489–521). Springer London.
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.
Keywords: Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation
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Salvatore Tabbone, & Oriol Ramos Terrades. (2014). An Overview of Symbol Recognition. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 523–551). Springer London.
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.
Keywords: Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting
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A.Kesidis, & Dimosthenis Karatzas. (2014). Logo and Trademark Recognition. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 591–646). Springer London.
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.
Keywords: Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems
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Ariel Amato, Ivan Huerta, Mikhail Mozerov, Xavier Roca, & Jordi Gonzalez. (2014). Moving Cast Shadows Detection Methods for Video Surveillance Applications. In Augmented Vision and Reality (Vol. 6, pp. 23–47). Springer Berlin Heidelberg.
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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Patricia Marquez, Debora Gil, R.Mester, & Aura Hernandez-Sabate. (2014). Local Analysis of Confidence Measures for Optical Flow Quality Evaluation. In 9th International Conference on Computer Vision Theory and Applications (Vol. 3, pp. 450–457).
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.
Keywords: Optical Flow; Confidence Measure; Performance Evaluation.
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Q. Xue, Laura Igual, A. Berenguel, M. Guerrieri, & L. Garrido. (2014). Active Contour Segmentation with Affine Coordinate-Based Parametrization. In 9th International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 5–14).
Abstract: In this paper, we present a new framework for image segmentation based on parametrized active contours. The contour and the points of the image space are parametrized using a set of reduced control points that have to form a closed polygon in two dimensional problems and a closed surface in three dimensional problems. By moving the control points, the active contour evolves. We use mean value coordinates as the parametrization tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon
or surface. Region-based energies such as the one proposed by Chan and Vese can be easily implemented in both two and three dimensional segmentation problems. We show the usefulness of our approach with several experiments.
Keywords: Active Contours; Affine Coordinates; Mean Value Coordinates
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