Pedestrian Presence Detection System Based on Image Processing

2019 ◽  
Author(s):  
Toncho G. Balbuzanov ◽  
Boris I. Evstatiev
Keyword(s):  
Image Processing ◽  
Detection System

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

Automatic lung nodule detection system using image processing techniques in computed tomography

2020 ◽  
Vol 56 ◽  
pp. 101659 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chang-Chiun Huang ◽  
Jing-Jhong Siao ◽  
Chia-Wen Hsieh ◽  
Vu Quang Huy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Detection System ◽  
Lung Nodule ◽  
Image Processing Techniques ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Processing Techniques

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

scholarly journals PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER

2018 ◽  
Vol 1 (3) ◽  
pp. 92
Author(s):  
Kadek Oki Sanjaya ◽  
Gede Indrawan ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
Image Processing ◽  
Detection System ◽  
High Accuracy ◽  
Special Study ◽  
Cascade Classifier ◽  
Integral Image ◽  
Main Method ◽  
Average Accuracy ◽  
Haar Features ◽  
Haar Cascade

Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object

scholarly journals A Wide-Ranging Review on Diabetic Retinotherapy

2020 ◽  
Vol 18 (02) ◽  
pp. 169-187
Author(s):  
Toufique Ahmed Soomro ◽  
Ahsin Murtaza Bughio ◽  
Shahid Hussain Siyal ◽  
Ali Anwar Panwar ◽  
Nasreen Nizamani
Keyword(s):  
Image Processing ◽  
Blood Vessels ◽  
Detection System ◽  
Eye Diseases ◽  
Fundus Images ◽  
The Past ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Computerized System ◽  
Automatic Algorithm

Diabetic Retinopathy (DR) is one of the major eye diseases that causes damage to retina of the human eye ball due to the rupture of tiny blood vessels. DR is identified by the ophthalmologists on the basis of various specifications i.e., textures, blood vessels and pathologies. The ophthalmologists are recently considering software for eye diseases detection based on image processing designed by the computing techniques and bio-medical images. In the analysis of medical imaging, traditional techniques of image processing and computer vision have played an important role in the field of ophthalmology. From the past two decades, there is a tremendous advancement in the development of computerized system for DR detection. This paper comprises the five parts of analysis on image based retinal detection DR, named as review of low varying contrast techniques of the retinal fundus Images (RFI), review of noise effect in the fundus images, review of pathology detection method from the retinal fundus images, review of blood vessels extraction from the RFI, and review of automatic algorithm for the DR detection. This paper presents a comprehensive detail to each problem in the retinal images. The procedures that are currently utilized to analyze the contrast issue and noise issues are discussed in detail. The paper also explains the techniques used for segmentation. In the end, the recent automated detection system of related eye diseases or DR is described.

scholarly journals Image Detection System Using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 5356-5362
Author(s):  
Mohini Gawande
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Estimation Method ◽  
Scale Invariant ◽  
Matrix Estimation ◽  
Speeded Up Robust Features ◽  
Invariant Features ◽  
Marketing Analysis ◽  
New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

Sign in / Sign up

Export Citation Format

Share Document