scholarly journals PATTERN RECOGNITION IN THE VISION SYSTEM

2017 ◽  
pp. 4-7
Author(s):  
Pelevin E.E. ◽  
Balyasny S.V.
Keyword(s):  
Pattern Recognition ◽  
Vision System

Diagnostics of Product by Vision System

2013 ◽  
Vol 308 ◽  
pp. 33-38 ◽  
Author(s):  
Kamil Židek ◽  
Eva Rigasová
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Vision System ◽  
Industrial Robot ◽  
Current Status ◽  
Basic Algorithm ◽  
Red Color ◽  
Plastic Parts ◽  
Modular Education ◽  
Algorithm Implementation

This article describes the vision system, which is designed for diagnostics of defects in casted products. In the first part an overview about image processing, edge and pattern recognition algorithms and current status in available free and commercial vision libraries is found. For the described task we selected open source Aforge .NET library. The next part describes common defects in casted products. Modular education system MPS 500 from Festo with conveyor and palette with plastic parts is used for simulation of production system. This system contains an industrial robot which can be used for sorting defective parts. The selected vision library is used for two level diagnostics of algorithm implementation. The first level algorithm detects position of part, its dimensions and edge disturbances. The second algorithm detects any defects inside of a part. The basic algorithm is presented only for circular shape with red color texture, but can be easily extended to other basic shapes by shape detector.

scholarly journals Pattern Recognition & Image Understanding based on Fuzzy Technology

1997 ◽  
Vol 1 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Kaoru Hirota ◽  
◽  
Yoshinori Arai ◽  
Yukiko Nakagawa ◽  
Keyword(s):  
Pattern Recognition ◽  
Time Use ◽  
Additional Data ◽  
Fuzzy Inference ◽  
Vision System ◽  
Image Understanding ◽  
Robot Arm ◽  
Remote Sensing Images ◽  
Dynamic Image ◽  
Hierarchical Pattern

Four image recognition and understanding techniques based on fuzzy technology developed by the authors group have been surveyed. First topics is a fuzzy clustering with additional data applied to the remote sensing images. It is modified version of the well known FCM. A robot arm and vision system on assembling line is presented using fuzzy discriminant tree for a real time use. The repetition method is introduced into the construction of discriminant tree. Third is the pattern recognition for a models of cars which is applied a fuzzy hierarchical pattern recognition based on fixation feedback. Finally, a fuzzy dynamic image understanding system is presented using fuzzy knowledge base and fuzzy inference method to understand dynamic image understanding on general roads in Japan. These techniques are mentioned the algorithms, and some of them are with experimental results.

Technical Vision Model of the Visual Systems for Industry Application

2021 ◽  
pp. 288-310
Author(s):  
Oleg Sytnik ◽  
Vladimir Kartashov
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Visual System ◽  
Mathematical Models ◽  
Human Visual System ◽  
Vision System ◽  
Vision Systems ◽  
Industrial Systems ◽  
Visual Systems ◽  
Technical Vision

The problems of highlighting the main informational aspects of images and creating their adequate models are discussed in the chapter. Vision systems can receive information about an object in different frequency ranges and in a form that is not accessible to the human visual system. Vision systems distort the information contained in the image. Therefore, to create effective image processing and transmission systems, it is necessary to formulate mathematical models of signals and interference. The chapter discusses the features of perception by the human visual system and the issues of harmonizing the technical characteristics of industrial systems for receiving and transmitting images. Methods and algorithms of pattern recognition are discussed. The problem of conjugation of the characteristics of the technical vision system with the consumer of information is considered.

A FPGA CORE GENERATOR FOR EMBEDDED CLASSIFICATION SYSTEMS

2011 ◽  
Vol 20 (02) ◽  
pp. 263-282 ◽  
Author(s):  
DAVIDE ANGUITA ◽  
LUCA CARLINO ◽  
ALESSANDRO GHIO ◽  
SANDRO RIDELLA
Keyword(s):  
Pattern Recognition ◽  
Vision System ◽  
Classification Systems ◽  
Support Vector ◽  
Digital Architecture ◽  
Current State ◽  
Field Programmable ◽  
Hardware Description ◽  
High Level ◽  
Core Description

We describe in this work a Core Generator for Pattern Recognition tasks. This tool is able to generate, according to user requirements, the hardware description of a digital architecture, which implements a Support Vector Machine, one of the current state-of-the-art algorithms for Pattern Recognition. The output of the Core Generator consists of a high-level language hardware core description, suitable to be mapped on a reconfigurable device, like a Field Programmable Gate Array (FPGA). As an example of the use of our tool, we compare different solutions, by targeting several reconfigurable devices, and implement the recognition part of a machine vision system for automotive applications.

scholarly journals Computer Analysis of Architecture Using Automatic Image Understanding

2019 ◽  
Vol 2018 ◽  
Author(s):  
Fan Wei ◽  
Yuan Li ◽  
Lior Shamir
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Machine Vision ◽  
Computer Analysis ◽  
Vision System ◽  
Cognitive Task ◽  
Image Understanding ◽  
New Paradigm ◽  
Complex Cognitive Task ◽  
Architectural Styles

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.

Pattern recognition with moment invariants on a machine vision system

1989 ◽  
Vol 9 (3) ◽  
pp. 175-180 ◽  
Author(s):  
Zhu Mingfa ◽  
Santosh Hasani ◽  
Surendra Bhattarai ◽  
Harpreet Singh
Keyword(s):  
Pattern Recognition ◽  
Machine Vision ◽  
Vision System ◽  
Moment Invariants ◽  
Machine Vision System

Algorithm and Design of an Intelligent Digital Integrated Circuit for a Watermelon Harvesting Robot

1999 ◽  
Vol 11 (3) ◽  
pp. 173-182 ◽  
Author(s):  
Yusuke Tokunaga ◽  
◽  
Toshihide Hakukawa ◽  
Takahiro Inoue ◽  
Keyword(s):  
Pattern Recognition ◽  
Integrated Circuit ◽  
Template Matching ◽  
Vision System ◽  
Logic Synthesis ◽  
Location Parameter ◽  
Functional Verification ◽  
Circular Pattern ◽  
Harvesting Robot ◽  
Digital Integrated Circuit

We propose an algorithm and design of an intelligent digital integrated circuit for recognition of circular patterns in a binary image based on template matching using a modified matching degree. The proposed system consists of a preprocessor #1 (noise reduction, coarsening, and edge detection), a preprocessor #2 (noise filtering and location parameter detection), and a circular pattern recognition block. The proposed system is implementable onto field programmable gate arrays (FPGAs) and forming part of the vision system for a watermelon harvesting robot. Functional verification, logic synthesis, and implementation are detailed for the FPGA circular pattern recognition block.

Sign in / Sign up

Export Citation Format

Share Document