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.

Offline Signature Verification Using Local Features and Decision Trees

Abstract: The most difficult problem of offline signature verification (SV) is that a signature is merely a static image missing a lot of the dynamic information associated with it. In this paper, three separate pseudo-dynamic features based on the gray level: gradient based local binary pattern (GLBP), statistical features of gray level co-occurrence matrix (SGLCM), simplified histogram of oriented gradients (SHOG) are proposed for writer-independent offline SV. These gray-level features can convey both texture information and the relative structural relationship of signature strokes. In addition, our experiments prove that the proposed features contain complementary information. Using random forests (RFs) as classifier, a fusion of the proposed features could achieve 7.42% and 0.08% average error rate (AER) for GPDS-253 and CEDAR datasets, respectively, which show the effectiveness of the proposed system. The implication of this paper is that part dynamic information could be extracted from a static gray level image.

Arabic Sign Language Recognition Using Optical Flow-Based Features and HMM

Abstract: Sign language is the main communication channel of deaf community It uses gestures and body language such as facial expressions, lib patterns, and hand shapes to convey meaning Sign language differs from one country to another Sign language recognition helps in removing barriers between people who understand only spoken language and those who understand sign language In this work, we propose an algorithm for segmenting videos of signs into sequences of still images and four techniques for Arabic sign language recognition, namely Modified Fourier Transform (MFT), Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and combination of HOG and Histogram of Optical Flow (HOG-HOF) These techniques are evaluated using Hidden Markov Model (HMM) The best performance is obtained with MFT features with 9911% accuracy This recognition rate shows the correctness and robustness of the proposed signs’ video segmentation algorithm

Ear recognition using pyramid histogram of orientation gradients

Abstract: Ear recognition Is still a standing problem In biometrics and has become an open research area in recent years. In this paper, we explore a new local feature extraction technique pyramid histogram of oriented gradients (PHOG) to represent ear images. However, the PHOG descriptor of the ear image is significantly large. To reduce the dimension of the PHOG descriptor, linear discriminant analysis (LDA) has been used to remove noise and avoid over fitting. Finally, the discriminant features are classified using nearest neighbor classifier. The PHOG has inherent properties to efficiently handle the problems of change in pose and partial occlusion of the ear images. The results of the proposed method are evaluated using two public ear databases, namely IIT Delhi ear database and University of Notre Dame ear database (Collections E). Our method with reduced features using LDA offers promising results and largely improves the recognition accuracy over existing methods.

HoG feature based detection of tissue deformations in ultrasound data

Abstract: The fast development of imaging techniques during last decades makes it possible to introduce intra-operative visualization as the integral part of surgical procedures. Therefore, the automated analysis of intra-operative ultrasound images is appreciated. The image processing, registration and visualization techniques help in better understanding and locate the operated region. To meet these needs, the paper presents an advanced algorithm for automated detection of tissue deformations caused by a biopsy needle. For this, feature set of Histogram of Gradients (HoG) is introduced. The extracted feature vectors are then used in image cell clustering step resulting in tissue deformation as well as biopsy needle detection. The applied there Kernelized Weighted C-Means clustering technique enables robust and accurate needle detection proven by sensitivity and specificity values at levels of 0.846 and 0.99, respectively.