scholarly journals A Case Study on FPGA Implementation of Parts Counting Orientation Recognition Method for Industrial Vision System

2021 ◽  
Vol 14 (0) ◽  
pp. 21-23
Author(s):  
Qiaochu Zhao ◽  
Ittetsu Taniguchi ◽  
Takao Onoye
Keyword(s):  
Vision System ◽  
Fpga Implementation ◽  
Recognition Method

scholarly journals Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 791
Author(s):  
Sufei Zhang ◽  
Ying Guo
Keyword(s):  
Computer Vision ◽  
Regression Model ◽  
Vision System ◽  
Expert Group ◽  
Group Test ◽  
System A ◽  
Real Objects ◽  
Practical Tool ◽  
Almost All

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

Implementation of the visual data processing algorithms for onboard computing units

2021 ◽  
Vol 9 (2) ◽  
pp. 106-111
Author(s):  
Sergey Sokolov ◽  
Andrey Boguslavsky ◽  
Sergei Romanenko
Keyword(s):  
Vision System ◽  
Fpga Implementation ◽  
Visual Data ◽  
Dense Optical Flow ◽  
Data Capturing ◽  
Computation Algorithm ◽  
Modern Experience ◽  
Computing Module ◽  
Xilinx Fpga

According to the short analysis of modern experience of hardware and software for autonomous mobile robots a role of computer vision systems in the structure of those robots is considered. A number of configurations of onboard computers and implementation of algorithms for visual data capturing and processing are described. In original configuration space the «algorithms-hardware» plane is considered. For software designing the realtime vision system framework is used. Experiments with the computing module based on the Intel/Altera Cyclone IV FPGA (implementation of the histogram computation algorithm and the Canny's algorithm), with the computing module based on the Xilinx FPGA (implementation of a sparse and dense optical flow algorithms) are described. Also implementation of algorithm of graph segmentation of grayscale images is considered and analyzed. Results of the first experiments are presented.

A novel bulk density-based recognition method for kitchen and dry waste: A case study in Beijing, China

2020 ◽  
Vol 114 ◽  
pp. 89-95 ◽  
Author(s):  
Zhonglei Li ◽  
Qingwei Wang ◽  
Tao Zhang ◽  
Hongtao Wang ◽  
Tan Chen
Keyword(s):  
Bulk Density ◽  
Recognition Method ◽  
Beijing China

Playing Domino: A Case Study for an Active Vision System

1999 ◽  
pp. 392-411 ◽  
Author(s):  
Maik Bollmann ◽  
Rainer Hoischen ◽  
Michael Jesikiewicz ◽  
Christoph Justkowski ◽  
Bärbel Mertsching
Keyword(s):  
Vision System ◽  
Active Vision

Target Recognition and Features ExtractionBased on ROI and SIFT Descriptors

2012 ◽  
Vol 220-223 ◽  
pp. 1153-1157 ◽  
Author(s):  
Jing Ma ◽  
Yuan Li ◽  
Qing Lin Wang
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Region Of Interest ◽  
Recognition Method ◽  
Sift Algorithm ◽  
Computation Cost ◽  
Feedback Information ◽  
Gaussian Pyramid ◽  
Pyramid Structure ◽  
Sift Features

This paper presents a target recognition method based on SIFT features matching and image ROI technology for vision system of assembly robots. In order to accelerate target recognition method, color histogram is used to detect the region of interest in images, then SIFT algorithm is employed in the ROI to extract the features. Thereby, the image Gaussian pyramid structure is simplified and the computation cost is reduced significantly. The experimental results show that this method can quickly recognize the objects without the loss of robustness and accuracy, and can provide necessary visual feedback information for assembly robots in approaching and operating task.

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