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Direct use of the hand as an input device is an attractive method for providing natural human–computer interaction (HCI). Currently, the only technology that satisfies the advanced requirements of hand-based input for HCI is glove-based sensing. This technology, however, has several drawbacks including that it hinders the ease and naturalness with which the(More)
Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our(More)
Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using Principal Component Analysis (PCA). Without using(More)
Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by(More)
This paper presents a new indexing-based approach to fingerprint identification. Central to the proposed approach is the idea of associating a unique topological structure with the fingerprint minutiae using the Delaunay triangulation. This allows for choosing more "meaningful" minutiae groups (i.e., triangles) during indexing, preserves index selectivity,(More)
On-road vehicle detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classi£cation for rear-view vehicle detection. Speci£cally, we propose using Gabor £lters for vehicle feature extraction and Support Vector Machines (SVMs)(More)
Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle(More)
We consider the problem of gender classification from frontal facial images using genetic feature subset selection. We argue that feature selection is an important issue in gender classification and demonstrate that Genetic Algorithms (GA) can select good subsets of features (i.e., features that encode mostly gender information), reducing the classification(More)
We investigate the application of genetic algorithms (GAs) for recognizing real two-dimensional (2-D) or three-dimensional (3-D) objects from 2-D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e., we assume a predefined set of models), while our recognition strategy lies on the recently proposed theory of(More)
The focus of this work is on the problem of feature selection and classification for on-road vehicle detection. In particular, we propose using quantized Haar wavelet features and Support Vector Machines (SVMs) for rear-view vehicle detection. Wavelet features are particularly attractive for vehicle detection because they form a compact representation,(More)