Searching for authors named "Thomas Serre" – sorted by Relevance.
14 documents found, showing 1 through 10.
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Feature Selection for Face Detection
- …We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification crite…
- Cited by 4 (2 self) – Add To MetaCart
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Error weighted classifier combination for multi-modal human identification
- …In this paper we describe a technique of classifier combination used in a human identification system. The system integrates all available features from multi-modal sources within a Bayesian framework. The framework allows representing a class of popular classifier combination rules and methods with…
- Cited by 2 (1 self) – Add To MetaCart
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Learning features of intermediate complexity for the recognition of biological motion
- …Abstract. Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have not yet been clarified, one possible explanation is that the visual system extracts specific motion features th…
- Cited by 3 (2 self) – Add To MetaCart
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Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images
- …We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second s…
- Cited by 26 (4 self) – Add To MetaCart
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A new biologically motivated framework for robust object recognition
- …In this paper, we introduce a novel set of features for robust object recognition, which exhibits outstanding performances on a variety of object categories while being capable of learning from only a few training examples. Each element of this set is a complex feature obtained by combining position…
- Cited by 19 (5 self) – Add To MetaCart
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Object recognition with features inspired by visual cortex
- …We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edgedetectors over neighboring positions and multiple orientations. Our system’s architecture is motivated by a quantitative model of v…
- Cited by 66 (7 self) – Add To MetaCart
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Component-based Face Detection
- …We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components of a face. On…
- Cited by 53 (13 self) – Add To MetaCart
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Hierarchical classification and feature reduction for fast face detection with support vector machines
- …We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In the first step we build a hierarchy of classifiers. On the bottom level a simple and fast linear classifier analyzes the whole image and rejects large parts…
- Cited by 18 (3 self) – Add To MetaCart
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Robust object recognition with cortex-like mechanisms
- …Abstract—We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating b…
- Cited by 27 (6 self) – Add To MetaCart
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Categorization by Learning and Combining Object Parts
- …We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classifiers are then combined in a second stage to yield a h…
- Cited by 41 (14 self) – Add To MetaCart
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