Computer Vision Application to Automatic Number Plate Recognition

1994 ◽  
Vol 27 (12) ◽  
pp. 169-173 ◽  
Author(s):  
Maged M.M. Fahmy
Author(s):  
Bappaditya Debnath ◽  
Mary O’Brien ◽  
Motonori Yamaguchi ◽  
Ardhendu Behera

AbstractThe computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered.


2021 ◽  
Author(s):  
Razvan Andrei Gheorghiu ◽  
Valentin Iordache ◽  
Valentin Alexandru Stan

2017 ◽  
Vol 79 (5-2) ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Mohd Safirin Karis ◽  
Siti Azura Ahmad Tarusan ◽  
Gao-Jie Wong ◽  
Mohd Shahrieel Mohd Aras ◽  
...  

The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.


Author(s):  
YUNG-SHENG CHEN ◽  
KUN-LI LIN

Perception of content displayed on the screen of a computer display using computer vision is a challenging topic if the treated target is changed from physical world to digital world. Screen area from the given computer display image should be segmented and corrected primarily before perceiving the content displayed on the screen. An automatic approach is proposed to the segmentation and deformation correction of screen area for a computer display image. Due to some inherent characteristics existing on ordinary computer displays, the segmentation can be performed by contour tracing. After contouring the screen area, its four corner locations can be readily identified. By approximating the obtained corners to the closest normal screen region, the deformed screen image can be further restored with affine transformation. As a computer vision application on the "look at" screen image, the effectively segmented screen region can be fixed after a little time. The experiments demonstrate that about 70% cases can be fixed under 33 processed frames, others under 51 processed frames, and thus confirm the feasibility of the proposed approach.


This paper proposes a way to construct a financially cheap and fast object tracking using Raspberry Pi3. Multiple object detection is an important step in any computer vision application. Since the number of cameras included is more these gadgets are compelled by expense per hub, control utilization and handling power. We propose a tracking system with low power consumption. The framework is completely designed with python and OpenCV. The tracking quality and accuracy is measured using publicly available datasets.


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