This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge ...

We propose a considerably faster approximation of the well known SIFT method. The main idea is to use efficient data structures for both, the detector and the descriptor. The detection of interest regions is considerably speed-up by using an integral image for scale space computation. The descriptor which is based on orientation histograms, is accelerated by the use of an integral orientation h...

For many years, various local descriptors that are insensitive to geometric changes such as viewpoint, rotation, and scale changes, have been attracting attention due to their promising performance. However, most existing local descriptors including the SIFT (Scale Invariant Feature Transform) are based on luminance information rather than color information thereby resulting in instability to p...

In this paper, we propose a method for extracting image features which utilizes 2 ndorder statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addit...

This paper outlines image processes for object detection and featurematchweightingutilising stereoscopic imagepairs, the Scale Invariant Feature Transform (SIFT) [13,4] and 3D reconstruction. The process is called FEWER; Feature Extraction andWeighting for EnhancedRecognition. The object detection technique is based on noise subtraction utilising the false positive matches from random features....

Multi-biometrics has recently emerged as a mean of more robust and efficient personal verification and identification. Exploiting information from multiple sources at various levels i.e., feature, score, rank or decision, the false acceptance and rejection rates can be considerably reduced. Among all, feature level fusion is relatively an understudied problem. This paper addresses the feature l...

Characterizing noisy or ancient documents is a challenging problem up to now. Many techniques have been done in order to effectuate feature extraction and image indexation for such documents. Global approaches are in general less robust and exact than local approaches. That’s why, we propose in this paper, a hybrid system based on global approach (fractal dimension), and a local one, based on S...

The SIFT feature extractor was introduced by Lowe in 1999. This algorithm provides invariant features and the corresponding local descriptors. The descriptors are then used in the image matching process. We propose an overview of this algorithm: the methodology and the tricky steps of its implementation, properties of the detector and descriptor. We analyze the structure of detected features. W...