Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.

Eye detection and localization in a facial image based on partial geometric shape of iris and eyelid under practical scenarios

Abstract: The accurate detection and localization of an eye in a facial image is important for many computer vision applications, such as face recognition, fatigued driving detection, and gaze estimation. Although research on eye detection has matured, most existing eye detection methods produce poor performance in various practical scenarios where there exists variation in facial expressions or illumination, people wearing clear eyeglasses, and so on. We have proposed a method that can locate eyes under the above-mentioned varied environmental conditions. The proposed approach follows two steps: eye candidate detection and eye candidate verification. In the first step, two features, namely semicircular edge shape and semiellipse edge shape features, are proposed to detect the eye candidates. In the second step, all of the selected eye candidates are verified using a support vector machine trained with the fusion of local binary pattern, cell mean intensity, and histogram of oriented gradients features. The proposed method has been tested under different conditions of AR, the Chinese Academy of Sciences’ Pose, Expression, Accessories, and Lighting, and the facial recognition technology databases. The experimental results suggest that the proposed method provides better performance as compared to the existing methods in terms of precision and recall.

Internet-Vision Based Vehicle Model Query System Using Eigenfaces and Pyramid of Histogram of Oriented Gradients

Abstract: This paper presents a new framework and feature set for vehicle model query system. By giving model names or manufacturer names as keywords, the desired vehicle images can be queried from target videos or vehicle image databases using internet-vision approach. In this framework, sample images are automatically retrieved from internet via search engine or car related website. Logos and frontal masks are segmented and are used for recognizing the manufacturer name and model of the vehicles, respectively. Eigenfaces and Pyramid Histogram of Oriented Gradients (PHOG) are proposed as features for recognition process. The experiments show that the proposed method can provide recognition rate of 98.2 % for manufacturer logo recognition process, and 94.00% for vehicle model recognition process. The performance of the entire framework of our proposed query system is also evaluated via precision and recall which are obtained as 87.67% and 80.00%, respectively.

Scale- and orientation-invariant keypoints in higher-dimensional data

Abstract: Description of keypoints, or local image features, is widely employed in computer vision. However, the most successful techniques do not extend immediately to more than two spatial dimensions. In this paper, we describe robust methods for extracting local orientations and gradient histograms from higher-dimensional data, using these techniques to develop a three-dimensional analogue of the popular Scale-Invariant Feature Transform (SIFT). We apply our algorithm to intra-patient registration of magnetic resonance (MR) images, with promising results. Our implementation will be released as open-source software.

Advanced Processing Techniques for Detection and Classification of Skin Lesions

Abstract: The paper presents how the skin cancer in forms of melanoma can be identified based on the digital image processing of the Iesion. The solution is based on the extraction of seven features (deterministic and statistic type) from the image of a skin lesion: perimeter, area, diameter, fractal dimension, lacunarity, histogram of oriented gradients, and local binary patterns. Each feature has attached a specific classifier and the diagnosis is obtained by using a voting scheme in the final classifier. The experimental results on a free database demonstrate that the method provides a high accuracy.