Katerine Diaz, Francesc J. Ferri, & W. Diaz. (2013). Fast Approximated Discriminative Common Vectors using rank-one SVD updates. In 20th International Conference On Neural Information Processing (Vol. 8228, pp. 368–375). LNCS. Springer Berlin Heidelberg.
Abstract: An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz
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Fadi Dornaika, Alireza Bosaghzadeh, & Bogdan Raducanu. (2013). Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition. In Human Behavior Understanding 4th International Workshop (Vol. 8212, pp. 124–135). Springer International Publishing.
Abstract: Workshop on Human Behavior Understanding
Human-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods.
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Francesco Ciompi, Simone Balocco, Carles Caus, J. Mauri, & Petia Radeva. (2013). Stent shape estimation through a comprehensive interpretation of intravascular ultrasound images. In 16th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 8150, pp. 345–352). LNCS. Springer Berlin Heidelberg.
Abstract: We present a method for automatic struts detection and stent shape estimation in cross-sectional intravascular ultrasound images. A stent shape is first estimated through a comprehensive interpretation of the vessel morphology, performed using a supervised context-aware multi-class classification scheme. Then, the successive strut identification exploits both local appearance and the defined stent shape. The method is tested on 589 images obtained from 80 patients, achieving a F-measure of 74.1% and an averaged distance between manual and automatic struts of 0.10 mm.
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Naveen Onkarappa, & Angel Sappa. (2013). Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 483–490). LNCS. Springer Berlin Heidelberg.
Abstract: Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
Keywords: Optical flow; regularization; Driver Assistance Systems; Performance Evaluation
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Marcelo D. Pistarelli, Angel Sappa, & Ricardo Toledo. (2013). Multispectral Stereo Image Correspondence. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 217–224). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.
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Fahad Shahbaz Khan, Joost Van de Weijer, Sadiq Ali, & Michael Felsberg. (2013). Evaluating the impact of color on texture recognition. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8047, pp. 154–162). Springer Berlin Heidelberg.
Abstract: State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.
Keywords: Color; Texture; image representation
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Debora Gil, Agnes Borras, Sergio Vera, & Miguel Angel Gonzalez Ballester. (2013). A Validation Benchmark for Assessment of Medial Surface Quality for Medical Applications. In 9th International Conference on Computer Vision Systems (Vol. 7963, pp. 334–343). LNCS. Springer Berlin Heidelberg.
Abstract: Confident use of medial surfaces in medical decision support systems requires evaluating their quality for detecting pathological deformations and describing anatomical volumes. Validation in the medical imaging field is a challenging task mainly due to the difficulties for getting consensual ground truth. In this paper we propose a validation benchmark for assessing medial surfaces in the context of medical applications. Our benchmark includes a home-made database of synthetic medial surfaces and volumes and specific scores for evaluating surface accuracy, its stability against volume deformations and its capabilities for accurate reconstruction of anatomical volumes.
Keywords: Medial Surfaces; Shape Representation; Medical Applications; Performance Evaluation
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Patricia Marquez, Debora Gil, Aura Hernandez-Sabate, & Daniel Kondermann. (2013). When Is A Confidence Measure Good Enough? In 9th International Conference on Computer Vision Systems (Vol. 7963, pp. 344–353). LNCS. Springer Link.
Abstract: Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality.
Keywords: Optical flow, confidence measure, performance evaluation
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Gioacchino Vino, & Angel Sappa. (2013). Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach. In 10th International Conference on Image Analysis and Recognition (Vol. 7950, pp. 354–363). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
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Francesco Ciompi, Rui Hua, Simone Balocco, Marina Alberti, Oriol Pujol, Carles Caus, et al. (2013). Learning to Detect Stent Struts in Intravascular Ultrasound. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 575–583). Springer Berlin Heidelberg.
Abstract: In this paper we tackle the automatic detection of struts elements (metallic braces of a stent device) in Intravascular Ultrasound (IVUS) sequences. The proposed method is based on context-aware classification of IVUS images, where we use Multi-Class Multi-Scale Stacked Sequential Learning (M2SSL). Additionally, we introduce a novel technique to reduce the amount of required contextual features. The comparison with binary and multi-class learning is also performed, using a dataset of IVUS images with struts manually annotated by an expert. The best performing configuration reaches a F-measure F = 63.97% .
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Francisco Alvaro, Francisco Cruz, Joan Andreu Sanchez, Oriol Ramos Terrades, & Jose Miguel Bemedi. (2013). Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 133–140). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model.
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Adriana Romero, & Carlo Gatta. (2013). Do We Really Need All These Neurons? In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 460–467). LNCS. Springer Berlin Heidelberg.
Abstract: Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch.
Keywords: Retricted Boltzmann Machine; hidden units; unsupervised learning; classification
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Antonio Clavelli, Dimosthenis Karatzas, Josep Llados, Mario Ferraro, & Giuseppe Boccignone. (2013). Towards Modelling an Attention-Based Text Localization Process. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 296–303). LNCS. Springer Berlin Heidelberg.
Abstract: This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented.
Keywords: text localization; visual attention; eye guidance
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Nuria Cirera, Alicia Fornes, Volkmar Frinken, & Josep Llados. (2013). Hybrid grammar language model for handwritten historical documents recognition. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 117–124). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout. Using a hidden Markov model-based recognition framework, a word-based grammar with a closed dictionary is enhanced by a character sequence recognition method. This allows to recognize out-of-dictionary words in controlled parts of the recognition, while keeping a closed vocabulary restriction for other parts. While the current status is work in progress, we can report an improvement in terms of character error rate.
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Daniel Sanchez, J.C.Ortega, & Miguel Angel Bautista. (2013). Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 50–58). LNCS. Springer Berlin Heidelberg.
Abstract: Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches.
Keywords: Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts
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