Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; i...

In this work, we introduce a new fusion framework for multispectral face recognition in the textures space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. The proposed texture space is based on the use of LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) techniques. Also, a new adaptive texture descriptor is presented....

Methods based on local image features have recently shown promise for texture classification tasks, especially in the presence of large intra-class variation due to illumination, scale, and viewpoint changes. Inspired by the theories of image structure analysis, this paper presents a simple, efficient, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this appr...

Received Nov 3, 2016 Revised Jan 7, 2017 Accepted Feb 16, 2017 Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local ...

This paper proposes a novel method for texture classification using high-order local pattern descriptor: Local Derivative Pattern (LDP). LDP is used to encode directional pattern features based on local derivative variations. The nth order LDP is proposed to encode the (n-1)th order local derivative direction variations, which can capture more detailed information. The local texture information...

In this paper, a new texture descriptor named "Fractional Local Neighborhood Intensity Pattern" (FLNIP) has been proposed for content based image retrieval (CBIR). It is an extension of the Local Neighborhood Intensity Pattern (LNIP)[1]. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3×3 window by considering the relationship with adjacen...

Image matching is a key part of many remote sensing image processing and image analysis. The traditional gray correlation matching algorithm based on corner point because they do not have the rotational invariance requires manual intervention to roughly match can not be automated. SIFT (Scale invariant feature transform) algorithm to solve the image rotation, scaling and other issues, but for t...

An efficient local fuzzy color and global color-texture descriptor is proposed for content-based image retrieval. In our retrieval system, an image is represented by a set of color-clustering-based segmented regions and global color-texture descriptors. Each segmented region corresponds to an object or parts of an object and is represented by a fuzzified color feature. A fuzzy region matching s...

We present a line-scan stereo system and descriptor-based dense stereo matching for highperformance vision applications. The stochastic binary local descriptor (STABLE) descriptor is a local binary descriptor that builds upon the principles of compressed sensing theory. The most important properties of STABLE are the independence of the descriptor length from the matching window size and the po...