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Angel Sappa and M.A. Garcia. 2004. Hierarchical Clustering of 3D Objects and its Application to Minimum Distance Computation. IEEE International Conference on Robotics & Automation, 5287–5292, New Orleans, LA (USA), ISBN: 0–7803–8232–3.
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G. Roig, Xavier Boix, F. de la Torre, Joan Serrat and C. Vilella. 2011. Hierarchical CRF with product label spaces for parts-based Models. IEEE Conference on Automatic Face and Gesture Recognition.
Abstract: Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset.
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Jiaolong Xu, David Vazquez, Krystian Mikolajczyk and Antonio Lopez. 2016. Hierarchical online domain adaptation of deformable part-based models. IEEE International Conference on Robotics and Automation.5536–5541.
Abstract: We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points.
Keywords: Domain Adaptation; Pedestrian Detection
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Jose Manuel Alvarez, Antonio Lopez and Ramon Baldrich. 2008. Illuminant Invariant Model-Based Road Segmentation. IEEE Intelligent Vehicles Symposium,.1155–1180.
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Jose Carlos Rubio, Joan Serrat, Antonio Lopez and N. Paragios. 2012. Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs. 21st International Conference on Pattern Recognition.2664–2667.
Abstract: We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint.
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Mohammad Rouhani and Angel Sappa. 2011. Implicit B-Spline Fitting Using the 3L Algorithm. 18th IEEE International Conference on Image Processing.893–896.
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Fadi Dornaika and Angel Sappa. 2007. Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data. In J. Braz, A.R., H. Araujo and J. Jorge,, ed. Advances in Computer Graphics and Computer Vision,. Springer Verlag, 354–366.
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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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Jiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2014. Incremental Domain Adaptation of Deformable Part-based Models. In Valstar, M. and F., Andrew and Pridmore, Tony, ed. 25th British Machine Vision Conference. BMVA Press.
Abstract: Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.
Keywords: Pedestrian Detection; Part-based models; Domain Adaptation
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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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