Geodesic invariant feature have been originally proposed to build a new local feature descriptor invariant not only to affine transformations, but also to general deformations. The aim of this paper is to investigate the possible improvements given by the use of color information in this kind of descriptor. We introduced color information both in geodesic feature construction and description. A...

Encoding local motion information using spatio-temporal features is a common approach in action recognition methods. These features are based on the information content inside subregions extracted at locations of interest in a video. In this paper, we propose a conceptually different approach to video feature extraction. We adopt an entropybased saliency framework and develop a method for estim...

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...

Orthogonal moment functions based on Tchebichef polynomials have found several applications in the eld of image analysis because of their superior feature representation capabilities. Local features represented by such moments could also be used in the design of e cient texture descriptors. This chapter introduces a novel method of constructing feature vectors from orthonormal Tchebichef moment...

We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by compar...

This paper addresses the question of computationally inexpensive yet discriminative and robust feature sets for real-world face recognition. The proposed descriptor named Patterns of Oriented Edge Magnitudes (POEM) has desirable properties: POEM (1) is an oriented, spatial multi-resolution descriptor capturing rich information about the original image; (2) is a multi-scale self-similarity based...

In this paper, we present SURE features – a novel combination of interest point detector and descriptor for 3D point clouds and depth images. We propose an entropy-based interest operator that selects distinctive points on surfaces. It measures the variation in surface orientation from surface normals in the local vicinity of a point. We complement our approach by the design of a view-pose-inva...

In this paper, we present an interest point detector and descriptor for 3D point clouds and depth images, coined SURE, and use it for recognizing semantically distinct places in indoor environments. We propose an interest operator that selects distinctive points on surfaces by measuring the variation in surface orientation based on surface normals in the local vicinity of a point. Furthermore, ...

This paper presents a color–texture descriptor based on the local mapped pattern approach for color–texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color–texture information from the images. These features are combined with texture ...

In this paper, we introduce BoSP (Bonn Salient Points), a method comprising a pair of a keypoint detector and descriptor in image data that are deeply geared to one another. Our detector identifies points of interest to be local maxima of appearance contrast to their surroundings in a statistical manner. This criterion admits a selection of particularly repeatable, but diverse looking keypoints...