Adaptive Local Binary Pattern Research Articles

Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation

Automatic extraction of blood vessels is an important step in computer-aided diagnosis in ophthalmology. The blood vessels have different widths, orientations, and structures. Therefore, the extracting of the proper feature vector is a critical step especially in the classifier-based vessel segmentation methods. In this paper, a new multi-scale rotation-invariant local binary pattern operator is employed to extract efficient feature vector for different types of vessels in the retinal images. To estimate the vesselness value of each pixel, the obtained multi-scale feature vector is applied to an adaptive neuro-fuzzy inference system. Then by applying proper top-hat transform, thresholding, and length filtering, the thick and thin vessels are highlighted separately. The performance of the proposed method is measured on the publicly available DRIVE and STARE databases. The average accuracy 0.942 along with true positive rate (TPR) 0.752 and false positive rate (FPR) 0.041 is very close to the manual segmentation rates obtained by the second observer. The proposed method is also compared with several state-of-the-art methods. The proposed method shows higher average TPR in the same range of FPR and accuracy.

Detection of artificial and scene text in images and video frames

Textual information in images and video frames constitutes a valuable source of high-level semantics for multimedia indexing and retrieval systems. Text detection is the most crucial step in a multimedia text extraction system and although it has been extensively studied the past decade still, it does not exist a generic architecture that would work for artificial and scene text in multimedia content. In this paper we propose a system for text detection of both artificial and scene text in images and video frames. The system is based on a machine learning stage which uses an Random Forest classifier and a highly discriminative feature set produced by using a new texture operator called Multilevel Adaptive Color edge Local Binary Pattern (MACeLBP). MACeLBP describes the spatial distribution of color edges in multiple adaptive levels of contrast. Then, a gradient-based algorithm is applied to achieve distinction among text lines as well as refinement in the localization of the text lines. The whole algorithm is situated in a multiresolution framework to achieve invariance to scale for the detection of text lines. Finally, an optional connected-component step segments text lines into words based on the distances between the resulting components. The experimental results are produced by applying a concise evaluation methodology and prove the superior performance achieved by the proposed text detection system for artificial and scene text in images and video frames.

Aurora image segmentation by combining patch and texture thresholding

The proportion of aurora to the field-of-view in temporal series of all-sky images is an important index to investigate the evolvement of aurora. To obtain such an index, a crucial phase is to segment the aurora from the background of sky. A new aurora segmentation approach, including a feature extraction method and the segmentation algorithm, is presented in this paper. The proposed feature extraction method, called adaptive local binary patterns (ALBP), selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of aurora, the segmentation algorithm is designed into two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method. As it is simple and efficient, our implementation is suitable for large-scale datasets. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy.

Fabric defect detection based on adaptive LBP and SVM

An advanced local binary patterns method was proposed to describe the main image features.Adaptive Local Binary Patterns (ALBP) method selected the frequently occurring patterns to construct the main pattern set,which avoids using the same pattern set to depict different texture structures in the traditional uniform local binary patterns.Based on the proposed method,an effective fabric defect detection algorithm of Support Vector Machine (SVM) was designed.First,the features of the training samples were extracted according to the set and were fed to SVM.Then the testing image was equally divided into detection windows from which ALBP features were also extracted and were classified by the trained SVM model.The experiments exhibit the detection effect of the proposed method is comparatively better than traditional LBP in terms of visual effect and detection accuracy.

A texture-based method for modeling the background and detecting moving objects

This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.