PERFORMANCE ANALYSIS OF DSP AND ARM PROCESSORS FOR IMAGE PROCESSING APPLICATIONS

2016 ◽  
Vol 16 (1-2) ◽  
pp. 39-48 ◽  
Author(s):  
Vijayalaxmi
Keyword(s):  
Image Processing ◽  
Performance Analysis

scholarly journals A standard data set for performance analysis of advanced IR image processing techniques

2012 ◽  
Author(s):  
A. Robert Weiß ◽  
Uwe Adomeit ◽  
Philippe Chevalier ◽  
Stéphane Landeau ◽  
Piet Bijl ◽  
...  
Keyword(s):  
Image Processing ◽  
Performance Analysis ◽  
Data Set ◽  
Image Processing Techniques ◽  
Standard Data ◽  
Processing Techniques

scholarly journals Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Lidia Forlenza ◽  
Giancarmine Fasano ◽  
Domenico Accardo ◽  
Antonio Moccia
Keyword(s):  
Image Processing ◽  
Performance Analysis ◽  
Data Rate ◽  
Obstacle Detection ◽  
Image Processing Technique ◽  
Identification System ◽  
Flight Performance ◽  
Image Processing Algorithm ◽  
Position Measurement ◽  
Detection Range

This paper is focused on the development and the flight performance analysis of an image-processing technique aimed at detecting flying obstacles in airborne panchromatic images. It was developed within the framework of a research project which aims at realizing a prototypical obstacle detection and identification System, characterized by a hierarchical multisensor configuration. This configuration comprises a radar, that is, the main sensor, and four electro-optical cameras. Cameras are used as auxiliary sensors to the radar, in order to increase intruder aircraft position measurement, in terms of accuracy and data rate. The paper thoroughly describes the selection and customization of the developed image-processing techniques in order to guarantee the best results in terms of detection range, missed detection rate, and false-alarm rate. Performance is evaluated on the basis of a large amount of images gathered during flight tests with an intruder aircraft. The improvement in terms of accuracy and data rate, compared with radar-only tracking, is quantitatively demonstrated.

scholarly journals Ball Classification through Object Detection using Deep Learning for Handball

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Arzielah Ashiqin Alwi ◽  
Ahmad Najmuddin Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Performance Analysis ◽  
Object Detection ◽  
High Speed ◽  
Detection Accuracy ◽  
Narrow Angle ◽  
Target Locations ◽  
Ball Detection

Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL)  models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions.

scholarly journals The Design of Educational Simulator for Noise and Filter Types in Image Processing

2018 ◽  
Vol 1 (1) ◽  
pp. 25-30
Author(s):  
Fahri Vatansever ◽  
Nedim Aktan Yalcin
Keyword(s):  
Image Processing ◽  
Performance Analysis ◽  
Theoretical Knowledge ◽  
Comparative Performance ◽  
Signal And Image Processing ◽  
Practical Applications ◽  
Interactive Applications

Noise and filtering noise are basic topics in the field of signal and image processing. In this study, an interactive simulator is designed for comprehension of related topics and to be reinforced theoretical knowledge with practical applications by students which have educated in these fields. Knowledge about noise and filtering in the field of image processing is easily, quickly and effectively gained to students with the simulator which includes many noise (Gaussian, Gamma, Rayleigh etc.) and filter (mean, median, min, max etc.) types, presents theoretical topics (explanations, mathematical statements, examples, etc.) about them visually, allows realizing of interactive applications (parameter and type changes etc.) and creates singular and comparative performance analysis.

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