scholarly journals Image Feature Descriptor using Co-occurrence Histograms of Oriented Gradients for Human Detection

2017 ◽  
Vol 71 (1) ◽  
pp. J28-J34 ◽  
Author(s):  
Tomoki Watanabe ◽  
Satoshi Ito ◽  
Kentaro Yokoi
Keyword(s):  
Human Detection ◽  
Image Feature ◽  
Feature Descriptor ◽  
Histograms Of Oriented Gradients

scholarly journals A New Retrieval Algorithm Based on Pulse Coupled Neural Network for Biomedical Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 9-13
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rapid Expansion ◽  
Image Feature ◽  
Experimental Result ◽  
Feature Descriptor ◽  
Resonance Imaging ◽  
Biomedical Images ◽  
Pulse Coupled Neural Network

The rapid expansion and improvement in medical science and technology lead to the generation of more image data in its regular activity such as computed tomography (CT), X-ray, magnetic resonance imaging (MRI) etc. To manage the medical images properly for clinical decision making, content-based medical image retrieval (CBMIR) system emerged. In this paper, Pulse Coupled Neural Network (PCNN) based feature descriptor is proposed for retrieval of biomedical images. Time series is used as an image feature which contains the entire information of the feature, based on which the similar biomedical images are retrieved in our work. Here, the physician can point out the disorder present in the patient report by retrieving the most similar report from related reference reports. Open Access Series of Imaging Studies (OASIS) magnetic resonance imaging dataset is used for the evaluation of the proposed approach. The experimental result of the proposed system shows that the retrieval efficiency is better than the other existing systems.

scholarly journals Human Detection by Boosting-Based Co-occurrence of HOG and Color Feature

1970 ◽  
Vol 5 (2) ◽  
pp. 47-51
Author(s):  
Ryo Matsumura ◽  
Akitoshi Hanazawa
Keyword(s):  
Human Detection ◽  
Color Feature ◽  
Histograms Of Oriented Gradients ◽  
Color Features ◽  
Local Areas

In this paper, we propose a method for human detection using co-occurrence of Histograms of Oriented Gradients (HOG) features and color features. This method expresses the co-occurrence between HOG and color features by Adaboost and generates the combination of the features effective for the identification automatically. Color features were calculated by making histograms that quantized hue and saturation in local areas. We show the effectiveness of the proposed method by identification experiments for human and non-human images.

Towards Intelligent Human Behavior Detection for Video Surveillance

2018 ◽  
pp. 34-73 ◽  
Author(s):  
Swati Nigam ◽  
Rajiv Singh ◽  
A. K. Misra
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Behavior ◽  
Human Activity Recognition ◽  
Human Detection ◽  
Video Sequences ◽  
Human Tracking ◽  
Feature Descriptor ◽  
Behavior Detection ◽  
Human Behavior Analysis

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.

Image feature descriptor based on shape salience points

2013 ◽  
Vol 120 ◽  
pp. 156-163 ◽  
Author(s):  
Glauco V. Pedrosa ◽  
Marcos A. Batista ◽  
Celia A.Z. Barcelos
Keyword(s):  
Image Feature ◽  
Feature Descriptor

A Novel Hybrid Discrete Cosine Transform Speeded Up Robust Feature-Based Secure Medical Image Watermarking Algorithm

2020 ◽  
Vol 10 (11) ◽  
pp. 2588-2599
Author(s):  
Saqib Ali Nawaz ◽  
Jingbing Li ◽  
Uzair Aslam Bhatti ◽  
Anum Mehmood ◽  
Raza Ahmed ◽  
...  
Keyword(s):  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Image Feature ◽  
Feature Descriptor ◽  
Cosine Transform ◽  
Speeded Up Robust Features ◽  
Feature Based ◽  
Dct Coefficients

With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. We design and implement a meaningful binary watermark embedding and extraction algorithm based on the SURF feature descriptor and discrete-cosine transform domain digital image watermarking algorithm. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Correlation values are more than 90% in most of the attacks. It provides better results against different noise attacks and also better performance against rotation. Transparency and high computational efficiency, coupled with dual functions of copyright protection and content authentication, is the advantage of the proposed algorithm.

scholarly journals Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Mi ◽  
Xin He ◽  
Haiwei Liu ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Training Sample ◽  
Detection Algorithm ◽  
Human Detection ◽  
Security Requirements ◽  
Support Vector ◽  
Svm Classifier ◽  
Histograms Of Oriented Gradients ◽  
Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

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