Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Detecting humans accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. This paper consists of efficient human detection in static images using Hist...

Automated rebar detection in images from groundpenetrating radar (GPR) is a challenging problem and difficult to perform in real-time as a result of relatively low contrast images and the size of the images. This paper presents a rebar localization algorithm, which can accurately locate the pixel locations of rebar within a GPR scan image. The proposed algorithm uses image classification and st...

We propose a compact descriptor for the purpose of dense image matching and object recognition. The descriptor is calculated based on local gradients about each point in an image. It contains the averages of gradients at four different windows surrounding a center point. The descriptor is calculated much faster than histogram of oriented gradients (HOG). Additionally, it will be shown that it i...

In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchableHoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchableHoG locally characterizes the complexity of an...

This paper proposes a multiple exposure images based traffic light recognition method. For traffic light recognition, color segmentation is widely used to detect traffic light signals; however, the color in an image is easily affected by various illuminations and leads to incorrect recognition results. In order to overcome the problem, we propose the multiple exposure technique which enhances t...

In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descrip...