Shapes and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from images via human knowledge and works. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. However, edge direction ...

a r t i c l e i n f o a b s t r a c t Keywords: Saliency Low-rank and sparse analysis Shape feature Structure This paper advocates a novel multi-scale mesh saliency method using the powerful low-rank and sparse analysis in shape feature space. The technical core of our approach is a new shape descriptor that embraces both local geometry information and global structure information in an integra...

Below we provide the values of expected local accuracy curves as defined in Section 2 of the ISVC main paper, for each descriptor-landmark pair, varying the search radius rS from 1 to 200 mm. Landmarks with bilateral symmetry (left and right) were merged together by considering each a separate instance of the same test. The descriptor template for each landmark was computed as the median descri...

In image registration or matching, the feature extracted by using traditional methods does not include depth information which may lead to a mismatch of keypoints. this paper, we prove that when camera moves, ratio difference keypoint and its neighbor pixel before after movement approximates constant. That means normalization is invariant movement. Based on property, all differences pixels cons...

A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation ...

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, ...

In this paper a new approach for moving objects detection in video surveillance systems is proposed. It is based on iLBP (intensity local binary patterns) descriptor that combines the classic LBP (local binary patterns) and the multiple regressive pseudospectra model. The iLBP descriptor itself is considered together with computational algorithm that is based on the sign image representation. W...

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 ...

We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex...