Local Binary Pattern Analysis Research Articles

Remote intelligent perception system for multi-object detection.

During the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities. Advances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image. The performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset. Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Cyber Crime

Cyber Crime

An assessment of automatic speaker verification vulnerabilities to replay spoofing attacks

AbstractThis paper analyses the threat of replay spoofing or presentation attacks in the context of automatic speaker verification. As relatively high‐technology attacks, speech synthesis and voice conversion, which have thus far received far greater attention in the literature, are probably beyond the means of the average fraudster. The implementation of replay attacks, in contrast, requires no specific expertise nor sophisticated equipment. Replay attacks are thus likely to be the most prolific in practice, while their impact is relatively under‐researched. The work presented here aims to compare at a high level the threat of replay attacks with those of speech synthesis and voice conversion. The comparison is performed using strictly controlled protocols and with six different automatic speaker verification systems including a state‐of‐the‐art iVector/probabilistic linear discriminant analysis system. Experiments show that low‐effort replay attacks present at least a comparable threat to speech synthesis and voice conversion. The paper also describes and assesses two replay attack countermeasures. A relatively new approach based on the local binary pattern analysis of speech spectrograms is shown to outperform a competing approach based on the detection of far‐field recordings. Copyright © 2016 John Wiley & Sons, Ltd.

Early subchondral bone changes in an osteoarthritis model in wild type and tgf-alpha knockout mice

Early subchondral bone changes in an osteoarthritis model in wild type and tgf-alpha knockout mice

국부 이진 패턴 분석에 기초한 지절 결함 검출 시스템 구현

Paper manufacturing industries have huge facilities with automatic equipments. Especially, in order to improve the efficiency of the paper manufacturing processes, it is necessary to detect the paper cutting defect effectively and to classify the causes correctly. In this paper, we review the problems of web monitoring system and web inspection system that have been traditionally used in industries for defect detection. Then we propose a novel paper cutting defect detection method based on the local binary pattern analysis and its implementation to mitigate the practical problems in industry environment. The proposed algorithm classifies the defects into edge-type and region-type and then it is shown that the proposed system works stably on the real paper cutting defect detection system.

Face Recognition by Exploring Information Jointly in Space, Scale and Orientation

Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.