Hair Detection in Mushroom Based on Image Processing

2015 ◽  
Vol 713-715 ◽  
pp. 402-405
Author(s):  
Zhan Si Deng ◽  
Tong Qiang Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
High Speed ◽  
Detection System ◽  
Target Identification ◽  
Accurate Method ◽  
Inspection System ◽  
Extraction Techniques ◽  
On Line

Nowadays,artificial recognition is widely used in the mushroom inspection system, however, it depends on subjective judgment of inspectors.Therefore,the testing personnel's experience, technology and other factors will affect the objectivity and accuracy of test results.Commodity inspection system need a high-speed, objective and accurate method for the on-line hair detection in the mushroom.On the basis of summary of domestic and foreign research, this paper studies the target identification and feature extraction techniques based on computer vision, conducts a feasibility study for the real-time hair detection system.

Design and Implementation of the Cold-Rolled Aluminum Plate Surface On-Line Defect Detection System

2013 ◽  
Vol 712-715 ◽  
pp. 2323-2326
Author(s):  
Xing Guang Qi ◽  
Hai Lun Zhang ◽  
Xiao Ting Li
Keyword(s):  
High Speed ◽  
Surface Defects ◽  
Detection System ◽  
Aluminum Plate ◽  
Gigabit Ethernet ◽  
Accurate Detection ◽  
High Processing Speed ◽  
Rolled Aluminum ◽  
On Line ◽  
Cold Rolled

This paper presents an on-line surface defects detection system based on machine vision, which has high speed architecture and can perform high accurate detection for cold-rolled aluminum plate. The system consists of high speed camera and industrial personal computer (IPC) array which connected through Gigabit Ethernet, achieved seamless detection by redundant control. In order to acquire high processing speed, single IPC as processor receives from and deals with only one or two cameras' image. Experimental results show that the system with high accurate detection capability can satisfy the requirement of real time detection and find out the defects on the production line effectively.

scholarly journals Image Detection System Using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 5356-5362
Author(s):  
Mohini Gawande
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Estimation Method ◽  
Scale Invariant ◽  
Matrix Estimation ◽  
Speeded Up Robust Features ◽  
Invariant Features ◽  
Marketing Analysis ◽  
New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

scholarly journals Fashion Product Classification through Deep Learning and Computer Vision

2019 ◽  
Vol 9 (7) ◽  
pp. 1385 ◽  
Author(s):  
Luca Donati ◽  
Eleonora Iotti ◽  
Giulio Mordonini ◽  
Andrea Prati
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Template Matching ◽  
Learning Approaches ◽  
Visual Classification ◽  
Product Classification ◽  
Processing Techniques

Visual classification of commercial products is a branch of the wider fields of object detection and feature extraction in computer vision, and, in particular, it is an important step in the creative workflow in fashion industries. Automatically classifying garment features makes both designers and data experts aware of their overall production, which is fundamental in order to organize marketing campaigns, avoid duplicates, categorize apparel products for e-commerce purposes, and so on. There are many different techniques for visual classification, ranging from standard image processing to machine learning approaches: this work, made by using and testing the aforementioned approaches in collaboration with Adidas AG™, describes a real-world study aimed at automatically recognizing and classifying logos, stripes, colors, and other features of clothing, solely from final rendering images of their products. Specifically, both deep learning and image processing techniques, such as template matching, were used. The result is a novel system for image recognition and feature extraction that has a high classification accuracy and which is reliable and robust enough to be used by a company like Adidas. This paper shows the main problems and proposed solutions in the development of this system, and the experimental results on the Adidas AG™ dataset.

scholarly journals Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877394 ◽  
Author(s):  
Ye Han ◽  
Zhigang Liu ◽  
DJ Lee ◽  
Wenqiang Liu ◽  
Junwen Chen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
High Speed ◽  
Power Transmission ◽  
Estimation Algorithm ◽  
Inspection System ◽  
High Speed Railway ◽  
Inspection Method ◽  
Railway Systems ◽  
Catenary System

Maintenance of catenary system is a crucial task for the safe operation of high-speed railway systems. Catenary system malfunction could interrupt railway service and threaten public safety. This article presents a computer vision algorithm that is developed to automatically detect the defective rod-insulators in a catenary system to ensure reliable power transmission. Two key challenges in building such a robust inspection system are addressed in this work, the detection of the insulators in the catenary image and the detection of possible defects. A two-step insulator detection method is implemented to detect insulators with different inclination angles in the image. The sub-images containing cantilevers and rods are first extracted from the catenary image. Then, the insulators are detected in the sub-image using deformable part models. A local intensity period estimation algorithm is designed specifically for insulator defect detection. Experimental results show that the proposed method is able to automatically and reliably detect insulator defects including the breakage of the ceramic discs and the foreign objects clamped between two ceramic discs. The performance of this visual inspection method meets the strict requirements for catenary system maintenance.

Design of Computer Vision and Image Processing Technology Based Car Keys Tooth Identification Detection System

2014 ◽  
Vol 1079-1080 ◽  
pp. 1061-1063 ◽  
Author(s):  
Hong Ying Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Processing Technology ◽  
Direct Impact ◽  
Assembly Process ◽  
Accurate Identification ◽  
Detection Technology ◽  
Key Aspects ◽  
The Cost

This paper can be used as acar key toothed recognition and detection technology and computer vision, imageprocessing technology combined with interdisciplinary applications. Car lockassembly complicated procedures, identification and car keys tooth detection isone of the key aspects of automotive lock assembly, lock a direct impact on theefficiency of the assembly process. The system can effectively improve theexisting car key tooth detection technology to reduce the cost of car keystooth detection recognition, while also rapid and accurate identification, sothat the entire lock assembly process much more efficient.

Development of Cable Inspection System Based on Image Processing

2014 ◽  
Vol 602-605 ◽  
pp. 2199-2204
Author(s):  
Huan Liu ◽  
Chao Tao Liu
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Host Computer ◽  
Inspection System ◽  
Energy Distributions ◽  
Single Motor ◽  
Image Acquisition System

A stayed cable inspection system was developed which consists of robot, host computer, cameras and image acquisition system. The robot was driven with single motor and could climb cables of various and variable diameters. Pictures of the cables’ were taken by the robot, and the defects and mars were identified automatically with image recognition. The steps of image recognition includes image de-noising, image enhancement, image segmentation, feature extraction, and recognition with the features of the images’ histogram grayscale distributions and energy distributions.

scholarly journals Hand Gesture Recognition using Image Processing and Feature Extraction Techniques

2020 ◽  
Vol 173 ◽  
pp. 181-190
Author(s):  
Ashish Sharma ◽  
Anmol Mittal ◽  
Savitoj Singh ◽  
Vasudev Awatramani
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Extraction Techniques
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