A high level approach to design and implementation of real time low-level image processing operators

2002 ◽  
Author(s):  
M.A. de Barros
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Low Level ◽  
Design And Implementation ◽  
High Level

AN EFFICIENT FPGA OVERLAY FOR COLOR TRANSFORMATION FUNCTION USING HIGH LEVEL SYNTHESIS

2021 ◽  
Vol 9 (1) ◽  
pp. 280-287
Author(s):  
Minal Deshmukh, Prasad Khandekar, Nishikant Sadafale
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Transformation Function ◽  
High Level Synthesis ◽  
Smart Devices ◽  
Real Time Image Processing ◽  
Real Time Image ◽  
Color Transformation ◽  
High Level ◽  
Time Image

Image Processing is a significantly desirable in commercial, industrial, and medical applications. Processor based architectures are inappropriate for real time applications as Image processing algorithms are quite intensive in terms of computations. To reduce latency and limitation in performance due to limited amount of memory and fixed clock frequency for synthesis in processor-based architecture, FPGA can be used in smart devices for implementing real time image processing applications. To increase speed of real time image processing custom overlays (Hardware Library of programmable logic circuit) can be designed to run on FPGA fabric. The IP core generated by the HLS (High Level Synthesis) can be implemented on a reconfigurable platform which allows effective utilization of channel bandwidth and storage. In this paper we have presented FPGA overlay design for color transformation function using Xilinx’s python productivity board PYNQ-Z2 to get benefit in performance over a traditional processor. Performance comparison of custom overlay on FPGA and Processor based platform shows FPGA execution yields minimum computation time.

Image Enhancement Techniques Using Particle Swarm Optimization Technique

2015 ◽  
pp. 327-347 ◽  
Author(s):  
V. Santhi ◽  
B. K. Tripathy
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Quality Enhancement ◽  
Swarm Optimization ◽  
Image Processing Techniques ◽  
Real Time Applications ◽  
High Level ◽  
Processing Techniques

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

scholarly journals A Study on Image Segmentation Techniques

2019 ◽  
Vol 8 (S2) ◽  
pp. 75-78
Author(s):  
S. Abdul Saleem ◽  
G. Vinitha
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Video Frame ◽  
Challenging Problem ◽  
Digital Form ◽  
Low Level ◽  
Grey Scale ◽  
High Level

Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.

scholarly journals A new method for vehicles detection and tracking using information and image processing

2021 ◽  
Vol 11 (6) ◽  
pp. 4942
Author(s):  
Mazouzi Amine ◽  
Kerfa Djoudi ◽  
Ismail Rakip Karas
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
New Method ◽  
Video Sequences ◽  
Detection Level ◽  
Low Level ◽  
Detection And Tracking ◽  
High Level

<span lang="EN-US">In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this article. It helps to reduce some artefacts that occur at the detection level. The main contribution of this article lies in the possibility of merging information of low level (detection) and high level (tracking). In other words, it is shown that many artefacts resulting from image processing (low level) can be detected, and eliminated thanks to the information contained in the labeling (high level). The proposed method has been tested on many video sequences and examples are given illustrating the merits of our approach.</span>

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