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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.


Papers
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Patent
21 Apr 2016
TL;DR: In this article, a system for counting stacked items using image analysis is described, where an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location.
Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

54 citations

Journal ArticleDOI
TL;DR: In this article, a method based on Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) was proposed to classify moving objects in crowded traffic scenes.
Abstract: Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.

54 citations

Proceedings ArticleDOI
23 Jun 2014
TL;DR: A battery of 5 tests that measure the gap between human and machine performances in several dimensions and find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects.
Abstract: Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local/global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward.

52 citations

Journal ArticleDOI
TL;DR: The experimental results proved that the proposed Rider-CSA-DBN outperformed other existing methods with maximal accuracy, sensitivity, and specificity, respectively.
Abstract: Agriculture is the main source of wealth, and its contribution is essential to humans. However, several obstacles faced by the farmers are due to different kinds of plant diseases. The determination and anticipation of plant diseases are the major concerns and should be considered for maximizing productivity. This paper proposes an effective image processing method for plant disease identification. In this research, the input image is subjected to the pre-processing phase for removing the noise and artifacts present in the image. After obtaining the pre-processed image, it is subjected to the segmentation phase for obtaining the segments using piecewise fuzzy C-means clustering (piFCM). Each segment undergoes a feature extraction phase in which the texture features are extracted, which involves information gain, histogram of oriented gradients (HOG), and entropy. The obtained texture features are subjected to the classification phase, which uses the deep belief network (DBN). Here, the proposed Rider-CSA is employed for training the DBN. The proposed Rider-CSA is designed by integrating the rider optimization algorithm (ROA) and Cuckoo Search (CS). The experimental results proved that the proposed Rider-CSA-DBN outperformed other existing methods with maximal accuracy of 0.877, sensitivity of 0.862, and the specificity of 0.877, respectively.

52 citations

Proceedings ArticleDOI
25 Aug 2013
TL;DR: Experiments show that the Co-HOG based technique clearly outperforms state-of-the-art techniques that use HOG, Scale Invariant Feature Transform (SIFT), and Maximally Stable Extremal Regions (MSER).
Abstract: Scene text recognition is a fundamental step in End-to-End applications where traditional optical character recognition (OCR) systems often fail to produce satisfactory results. This paper proposes a technique that uses co-occurrence histogram of oriented gradients (Co-HOG) to recognize the text in scenes. Compared with histogram of oriented gradients (HOG), Co-HOG is a more powerful tool that captures spatial distribution of neighboring orientation pairs instead of just a single gradient orientation. At the same time, it is more efficient compared with HOG and therefore more suitable for real-time applications. The proposed scene text recognition technique is evaluated on ICDAR2003 character dataset and Street View Text (SVT) dataset. Experiments show that the Co-HOG based technique clearly outperforms state-of-the-art techniques that use HOG, Scale Invariant Feature Transform (SIFT), and Maximally Stable Extremal Regions (MSER).

52 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022181
2021116
2020189
2019179
2018240