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Yainuvis Socarras, David Vazquez, Antonio Lopez, David Geronimo and Theo Gevers. 2012. Improving HOG with Image Segmentation: Application to Human Detection. In J. Blanc-Talon et al., ed. 11th International Conference on Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 178–189. (LNCS.)
Abstract: In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
Keywords: Segmentation; Pedestrian Detection
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Fernando Barrera, Felipe Lumbreras and Angel Sappa. 2012. Evaluation of Similarity Functions in Multimodal Stereo. 9th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 320–329. (LNCS.)
Abstract: This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
Keywords: Aveiro, Portugal
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Miguel Oliveira, Angel Sappa and V. Santos. 2012. Color Correction using 3D Gaussian Mixture Models. 9th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 97–106. (LNCS.)
Abstract: The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
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Muhammad Anwer Rao, David Vazquez and Antonio Lopez. 2011. Color Contribution to Part-Based Person Detection in Different Types of Scenarios. In P. Real, D.D., H. Molina, A. Berciano, W. Kropatsch, ed. 14th International Conference on Computer Analysis of Images and Patterns. Berlin Heidelberg, Springer, 463–470.
Abstract: Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.
Keywords: Pedestrian Detection; Color
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Naveen Onkarappa and Angel Sappa. 2011. Space Variant Representations for Mobile Platform Vision Applications. In P. Real, D.D., H. Molina, A. Berciano, W. Kropatsch, ed. 14th International Conference on Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 146–154.
Abstract: The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
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Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto and Sergio S. Furuie. 2011. Inferring the Performance of Medical Imaging Algorithms. In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano and Walter Kropatsch, eds. 14th International Conference on Computer Analysis of Images and Patterns. Berlin, Springer-Verlag Berlin Heidelberg, 520–528. (LNCS.)
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
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Muhammad Anwer Rao, David Vazquez and Antonio Lopez. 2011. Opponent Colors for Human Detection. In J. Vitria, J.M. Sanches and M. Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis. Berlin Heidelberg, Springer, 363–370. (LNCS.)
Abstract: Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.
Keywords: Pedestrian Detection; Color; Part Based Models
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Marçal Rusiñol, David Aldavert, Dimosthenis Karatzas, Ricardo Toledo and Josep Llados. 2011. Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval. In P. Clough and 6 others, eds. 33rd European Conference on Information Retrieval. Berlin, Springer, 314–325. (LNCS.)
Abstract: In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.
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Naveen Onkarappa and Angel Sappa. 2010. On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow. 7th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 230–239. (LNCS.)
Abstract: This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach.
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David Aldavert, Ricardo Toledo, Arnau Ramisa and Ramon Lopez de Mantaras. 2009. Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing. 5th International Symposium on Visual Computing. Springer Berlin Heidelberg, 44–55.
Abstract: In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.
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