|
|
Antonio Hernandez. (2010). Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation (Vol. 153). Master's thesis, , .
|
|
|
|
Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2010). Feature Detectors and Feature Descriptors: Where We Are Now (Vol. 154).
Abstract: Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.
|
|
|
|
Lluis Pere de las Heras. (2010). Syntactic Model for Semantic Document Analysis (Vol. 158).
|
|
|
|
Anjan Dutta. (2010). Symbol Spotting in Graphical Documents by Serialized Subgraph Matching (Vol. 159). Master's thesis, , .
|
|
|
|
Ekain Artola. (2010). Human Attention Map Prediction Combining Visual Features (Vol. 160). Bachelor's thesis, , .
|
|
|
|
David Fernandez. (2010). Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors (Vol. 161). Master's thesis, , .
|
|
|
|
Jon Almazan. (2010). Deforming the Blurred Shape Model for Shape Description and Recognition (Vol. 163). Master's thesis, , .
|
|
|
|
Nataliya Shapovalova. (2010). On Importance of Interaction and Context (Vol. 155). Master's thesis, , .
|
|
|
|
Zhanwu Xiong. (2010). A Pompd Model for Active Camera Control (Vol. 156). Master's thesis, , .
|
|
|
|
Fernando Barrera, Felipe Lumbreras, & Angel Sappa. (2010). Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space. In 17th IEEE International Conference on Image Processing (2749–2752).
Abstract: This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach.
|
|
|
|
Mohammad Rouhani, & Angel Sappa. (2010). A Fast accurate Implicit Polynomial Fitting Approach. In 17th IEEE International Conference on Image Processing (1429–1432).
Abstract: This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons.
|
|
|
|
Fadi Dornaika, & Bogdan Raducanu. (2010). Person-specific face shape estimation under varying head pose from single snapshots. In 20th International Conference on Pattern Recognition (3496–3499).
Abstract: This paper presents a new method for person-specific face shape estimation under varying head pose of a previously unseen person from a single image. We describe a featureless approach based on a deformable 3D model and a learned face subspace. The proposed approach is based on maximizing a likelihood measure associated with a learned face subspace, which is carried out by a stochastic and genetic optimizer. We conducted the experiments on a subset of Honda Video Database showing the feasibility and robustness of the proposed approach. For this reason, our approach could lend itself nicely to complex frameworks involving 3D face tracking and face gesture recognition in monocular videos.
|
|
|
|
Antonio Hernandez, Miguel Reyes, Sergio Escalera, & Petia Radeva. (2010). Spatio-Temporal GrabCut human segmentation for face and pose recovery. In IEEE International Workshop on Analysis and Modeling of Faces and Gestures (33–40).
Abstract: In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology.
|
|
|
|
Miguel Angel Bautista, Xavier Baro, Oriol Pujol, Petia Radeva, Jordi Vitria, & Sergio Escalera. (2010). Compact Evolutive Design of Error-Correcting Output Codes. In Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 119–128).
Abstract: The classi cation of large number of object categories is a challenging trend in the Machine Learning eld. In literature, this is often addressed using an ensemble of classi ers. In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for the combination of classi ers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classi ers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classi ers. Evolutionary computation is used for tuning the parameters of the classi ers and looking for the best Minimal ECOC code con guration. The results over several public UCI data sets and a challenging multi-class Computer Vision problem show that the proposed methodology obtains comparable and even better results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
Keywords: Ensemble of Dichotomizers; Error-Correcting Output Codes; Evolutionary optimization
|
|
|
|
Francesco Ciompi, Oriol Pujol, & Petia Radeva. (2010). A meta-learning approach to Conditional Random Fields using Error-Correcting Output Codes. In 20th International Conference on Pattern Recognition (710–713).
Abstract: We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when applied to two real detection problems.
|
|
