We introduce a method for detecting concepts by using only visual information. In this method, firstly the feature vector of each frame is formed by concatenating histograms of local binary patterns extracted from overlapped and fixed-size rectangles within the frame. Then SVM classifiers are trained to classify frames as containing or not containing the concept. Experimental results show that ...

This paper presents a new method to characterize the texture of gray level bone radiographic images. The technique is inspired from the Local Binary Pattern descriptor which has been classically applied on two dimensional (2D) images. Our algorithm is a derived solution for the 1D projected fields of the 2D images. The method requires a series of preprocessing of images. A clinical study is led...

This paper addresses the multi-target tracking problem with the help of a matching method where moving objects are detected in each frame, tracked when it is possible and matched by similarity of covariance matrices when difficulties arrive. Three contributions are proposed. First, a compact vector based on color invariants and Local Binary Patterns Variance is compared to more classical featur...

This paper presents an effective local spatiotemporal descriptor for action recognition from depth video sequences. The unique property of our descriptor is that it takes the shape discrimination and action speed variations into account, intending to solve the problems of distinguishing different pose shapes and identifying the actions with different speeds in one goal. The entire algorithm is ...

One of the most challenging issues in iris recognition is the design of techniques able to ensure high accuracy even in adverse conditions. This paper deals with an approach to iris matching based on the combination of local features: Linear Binary Patterns (LBP) and discriminable textons (BLOBs) are presently exploited. The techniques have been refined ad hoc, to allow the extraction of signif...

The max-sum loopy belief propagation (LBP) algorithm was shown in [4] to produce strong results in a simple decentralised supply chain formation (SCF) scenario where goods are traded in single units. In this paper, we demonstrate the performance of LBP in a multi-unit SCF scenario with additional constraints. We also provide experimental analysis of LBP’s performance in dynamic scenarios where ...

Texture is an important characteristic for many types of images. In recent years very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBP) have been developed, which has led to significant progress in applying texture methods to different problems and applications. Due to this progress, the division between texture descriptors and more gene...

In this work it is described a framework for classifying face images using Adaboost and domain-partitioning based classifiers. The most interesting aspect of this framework is the capability of building classification systems with high accuracy in dynamical environments, which achieve, at the same time, high processing and training speed. We apply this framework to the specific problem of gende...

Detection of unusual objects amongst a highly textured background is a difficult problem, especially when the texture is manifest in the temporal dimension as well. Outdoor scenes involving waving trees or moving water are examples of such a scenario, but are nevertheless frequently encountered in real world vision applications. By defining a simple but rotationally sensitive Local Binary Patte...

Osteoporosis can be identified by looking at 2D x-ray images of the bone. The high degree of similarity between images of a healthy bone and a diseased one makes classification a challenge. A good bone texture characterization technique is essential for identifying osteoporosis cases. Standard texture feature extraction techniques like Local Binary Pattern (LBP), Gray Level Cooccurrence Matrix ...