Estimation of human orientation contributes to improving the accuracy of human behavior recognition. However, estimation of human orientation is a challenging task because of the variable appearance of the human body. The wide variety of poses, sizes and clothes combined with a complicated background degrades the estimation accuracy. Therefore, we propose a method for estimating human orientati...

The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with var...

This paper presents a technique to classify the circular traffic sign based-on HOG (histogram of oriented gradients) and a ring partitioned matching. The method divides an image into several ring areas, and calculates the HOG feature on each ring area. In the matching process, the weight is assigned to each ring for calculating the distance of HOG feature between tested image and reference imag...

An image of an eye is known to have a certain shape, roughly ellipse shaped. And so the Histogram of Oriented Gradient (HOG) feature is a good descriptor for this type of detection. In this paper, the eye detector is trained with HOG features using an Adaptive Boosting (AdaBoost) algorithm to train and learn a set of weak classifiers. It is shown to work fairly well although it underperforms wh...

Human Detection in Images is a contemporary Computer Vision problem, still welcoming improved solutions. This subset area of object detection has seen many attempts made towards efficient implementation and in this project proposal we describe one based on Histogram of Oriented Gradients which proves to be superior than the rest in terms of both Detection rate and Error rate when using a Linear...

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self le...

Human detection is a challenging classification problem which has many potential applications including monitoring pedestrian junctions, young children in school and old people in hospitals, and several security, surveillance and civilian applications. Various approaches have been proposed to solve this problem. We have studied and implemented a scheme using Histogram of Oriented Gradients (HOG...

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment.Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal...

A method using Zernike Moments (ZM) and Histogram of Oriented Gradients (HOG) for classification and recognition of plant leaf images is proposed in this paper. After preprocessing, we compute the shape features of a leaf using Zernike Moments (ZM) and texture features using Histogram of Oriented Gradients (HOG) and then Support Vector Machine (SVM) classifier is used for plant leaf image class...