Topic
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.
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TL;DR: In this paper, a combination of grayscale-based gradient and color-invariant gradient is proposed to replace the original gradient definition, which achieves a 30% reduction in miss rate.
Abstract: The histogram of oriented gradients has been proven to be a successful method of object detection, especially for pedestrian detection in images and videos. However, the question of how to make maximal use of color information for gradient calculation has not been thoroughly investigated. We propose a simple yet effective adaption that uses a combination of grayscale-based gradient and color-invariant-based gradients (after Geusebroek et al.) to replace the original gradient definition. Our experiments show that such a combination achieves a 30% reduction in miss rate, using the same experiment setting and the same evaluation criteria as Dalal et al. We have also measured the trade-off between the performance and computational cost by using a more sophisticated quadratic kernel instead of a linear kernel. While it can reduce the miss rate further by 10% to 20%, using a quadratic kernel can take as much as 70 times more running time for the original (Dalal et al. 2006) dataset.
11 citations
TL;DR: The results indicate that excellent diagnostic predictions can be produced using the proposed framework, which is a novel framework for volumetric medical image classification founded on homogeneous decomposition and dictionary learning.
11 citations
01 Apr 2017
TL;DR: A novel initiative towards the digital image processing technique by the application of histogram of oriented gradients (HOG) feature descriptor using the OpenCV library coded with the High-level programming language Python, booted with the help of Raspberry Pi microcontroller fitted with a RaspiCam to capture moving images of objects passing under it.
Abstract: A novel initiative towards the digital image processing technique by the application of histogram of oriented gradients (HOG) feature descriptor using the OpenCV library coded with the High-level programming language Python, booted with the help of Raspberry Pi microcontroller fitted with a RaspiCam to capture moving images of objects passing under it has been presented in this paper. The project utilizes image samples in top-view which are used to set predefined models containing a great number of motion variations for identification of humans entering a room through a door or gate. Pair of Passive Infra-Red (PIR) sensors has been used to instruct the system to capture images of incoming or outgoing objects that cross it. This method of image detection combined with a sensor feedback has been used along with an ability to send data via bluetooth to local servers for security or record purposes.
11 citations
18 Apr 2017
TL;DR: A new methodology based on a two-stage classification framework that combines multiple trabecular bone regions of interest (ROIs) for osteoporosis prescreening is constructed and the results suggest that the proposed method with the HOG (histogram of oriented gradients) feature achieves the best overall accuracy.
Abstract: A panoramic radiography image provides not only details of teeth but also rich information about trabecular bone. Recent studies have addressed the correlation between trabecular bone structure and osteoporosis. In this paper, we collect a dataset containing 40 images from 40 different subjects, and construct a new methodology based on a two-stage classification framework that combines multiple trabecular bone regions of interest (ROIs) for osteoporosis prescreening. In the first stage, different support vector machines (SVMs) are adopted to describe different information of different ROIs. In the second stage, the output probabilities of the first stage are effectively combined by using an additional linear SVM model to make a final prediction. Based on our two stage model, we test the performance of different image features by using leave-one-out cross-valuation and analysis of variance rules. The results suggest that the proposed method with the HOG (histogram of oriented gradients) feature achieves the best overall accuracy.
11 citations
TL;DR: In this article, a sliding window approach based on Histogram of Oriented Gradients (HOG) features is used for Brazilian license plate detection, which consists in scanning the whole image in a multiscale fashion such that the license plate is located precisely.
Abstract: Due to the increasingly need for automatic traffic monitoring, vehicle license plate detection is of high interest to perform automatic toll collection, traffic law enforcement, parking lot access control, among others. In this paper, a sliding window approach based on Histogram of Oriented Gradients (HOG) features is used for Brazilian license plate detection. This approach consists in scanning the whole image in a multiscale fashion such that the license plate is located precisely. The main contribution of this work consists in a deep study of the best setup for HOG descriptors on the detection of Brazilian license plates, in which HOG have never been applied before. We also demonstrate the reliability of this method ensured by a recall higher than 98% (with a precision higher than 78%) in a publicly available data set.
11 citations