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Karel Paleček, David Geronimo, & Frederic Lerasle. (2012). Pre-attention cues for person detection. In Cognitive Behavioural Systems, COST 2102 International Training School (pp. 225–235). LNCS. Springer Berlin Heidelberg.
Abstract: Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.
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Joan Arnedo-Moreno, & Agata Lapedriza. (2010). Visualizing key authenticity: turning your face into your public key. In 6th China International Conference on Information Security and Cryptology (pp. 605–618). LNCS.
Abstract: Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process.
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Petia Radeva, Michal Drozdzal, Santiago Segui, Laura Igual, Carolina Malagelada, Fernando Azpiroz, et al. (2012). Active labeling: Application to wireless endoscopy analysis. In High Performance Computing and Simulation, International Conference on (pp. 174–181).
Abstract: Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”.
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Jose Carlos Rubio, Joan Serrat, & Antonio Lopez. (2012). Video Co-segmentation. In 11th Asian Conference on Computer Vision (Vol. 7725, pp. 13–24). LNCS. Springer Berlin Heidelberg.
Abstract: Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.
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Monica Piñol, Angel Sappa, & Ricardo Toledo. (2012). MultiTable Reinforcement for Visual Object Recognition. In 4th International Conference on Signal and Image Processing (Vol. 221, pp. 469–480). LNCS. Springer India.
Abstract: This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach.
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Mohammad Rouhani, & Angel Sappa. (2012). Non-Rigid Shape Registration: A Single Linear Least Squares Framework. In 12th European Conference on Computer Vision (Vol. 7578, pp. 264–277). LNCS. Springer Berlin Heidelberg.
Abstract: This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided.
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Miguel Oliveira, V.Santos, & Angel Sappa. (2012). Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition. In IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles.
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Marina Alberti, Simone Balocco, Xavier Carrillo, Josepa Mauri, & Petia Radeva. (2012). Automatic Non-Rigid Temporal Alignment of IVUS Sequences. In 15th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 1, pp. 642–650). Springer-Verlag Berlin, Heidelberg.
Abstract: Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability.
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Pedro Martins, Paulo Carvalho, & Carlo Gatta. (2012). Stable Salient Shapes. In International Conference on Digital Image Computing: Techniques and Applications.
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Jaume Gibert, Ernest Valveny, Horst Bunke, & Alicia Fornes. (2012). On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 135–143). LNCS. Springer-Berlag, Berlin.
Abstract: Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.
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Rui Hua, Oriol Pujol, Francesco Ciompi, Marina Alberti, Simone Balocco, Josepa Mauri, et al. (2012). Stent Strut Detection by Classifying a Wide Set of IVUS Features. In Computed Assisted Stenting Workshop.
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Adela Barbulescu, Wenjuan Gong, Jordi Gonzalez, Thomas B. Moeslund, & Xavier Roca. (2012). 3D Human Pose Estimation Using 2D Body Part Detectors. In 21st International Conference on Pattern Recognition (pp. 2484–2487).
Abstract: Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as support vector machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additional input data, such as silhouettes, or controlled camera settings. We present a framework that is capable of estimating the 3D pose of a person from single images or monocular image sequences without requiring background information and which is robust to camera variations. The framework models the non-linearity present in human pose estimation as it benefits from flexible learning approaches, including a highly customizable 2D detector. Results on the HumanEva benchmark show how they perform and influence the quality of the 3D pose estimates.
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Fadi Dornaika, A.Assoum, & Bogdan Raducanu. (2012). Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 575–583). LNCS. Springer Berlin Heidelberg.
Abstract: A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis.
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Bogdan Raducanu, & Fadi Dornaika. (2012). Out-of-Sample Embedding by Sparse Representation. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 336–344). Springer Berlin Heidelberg.
Abstract: A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques.
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Jaime Moreno, & Xavier Otazu. (2011). Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder. In IEEE International Conference on Multimedia and Expo (pp. 1–6).
Abstract: In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder.
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