Size and Defect Detection of Hami Big Jujubes Based on Computer Vision

2012 ◽  
Vol 562-564 ◽  
pp. 750-754 ◽  
Author(s):  
Ben Xue Ma ◽  
Xiang Xiang Qi ◽  
Li Li Wang ◽  
Rong Guang Zhu ◽  
Qin Gang Chen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Data ◽  
Ccd Camera ◽  
Transverse Diameter ◽  
Testing Time ◽  
Hardware System ◽  
Space Transformation ◽  
Visual Basic6.0 ◽  
Mean And Variance

In order to realize the rapid nondestructive testing for Hami Big Jujubes’ quality detection, a detecting system based on computer vision was established to detect Hami Big Jujubes’ size and defect. The image grabbing card and CCD camera were consisted of the hardware system, which was used to collect image data. Visual Basic6.0 and image processing toolbox of Mil9.0 constituted the software system. The function of MIL9.0 was called in the Visual Basic6.0 to realize the detection. During image processing, the threshold was all chose (0.1,0.7).Many methods were used to identify the features rapidly and get the H value’s mean and variance, such as colour space transformation, mathematical morphology processing and mask etc. Experimental results showed that the correlation coefficient between the projective areas and weights was 0.945.The correlation between projective areas, transverse diameter and vertical diameter was 0.951.The defects grading models were built by BP neural network .The discriminating rate was as high as 99.16％ in training set，and 91.43％ in prediction set. The average testing time was 80 milliseconds, which can satisfy the detection system’s requirements of time．

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

Research on the High-Speed Object Shadowgraph Image Processing Method Based on Adaptive Threshold Segmentation

2013 ◽  
Vol 325-326 ◽  
pp. 1571-1575
Author(s):  
Fang Wang ◽  
Zong Wei Yang ◽  
De Ren Kong ◽  
Yun Fei Jia
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Center Of Mass ◽  
Image Data ◽  
Ccd Camera ◽  
Adaptive Threshold ◽  
Processing Method ◽  
Laser Source ◽  
Feature Points ◽  
Threshold Segmentation

Shadowgraph is an important method to obtain the flight characteristics of high-speed object, such as attitude and speed etc. To get the contour information of objects and coordinates of feature points from shadowgraph are the precondition of characteristics analysis. Current digital shadowgraph system composed of CCD camera and pulsed laser source is widely used, but still lack of the corresponding method in image processing. Therefore, the selection of an effective processing method in order to ensure high effectiveness and accuracy of image data interpretation is an urgent need to be solved. According to the features of shadowgraph, a processing method to realize the contour extraction of high-speed object by adaptive threshold segmentation is proposed based on median filtering in this paper, and verified with the OpenCV in VC environment, the identification process of the feature points are recognized. The result indicates that by using this method, contours of high-speed objects can be detected nicely, to combine relevant algorithm, the pixel coordinates of feature points such as the center of mass can be recognized accurately.

Image Capture Board for The PC

1995 ◽  
Vol 32 (3) ◽  
pp. 235-255
Author(s):  
T. David Binnie ◽  
I. Reading
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Personal Computer ◽  
Low Cost ◽  
Random Access ◽  
Image Data ◽  
Image Capture ◽  
Design And Implementation

Image capture board for the PC We report the design and implementation of a low cost, image capture board for an IBM type personal computer. The board is particularly suited to computer vision education. The board provides: image capture at video rate, random access to xy addressable image data, and options for on-board image processing hardware.

scholarly journals An Image Detection Method Based on Parameter Optimization of Support Vector Machine

2021 ◽  
Vol 15 ◽  
pp. 306-314
Author(s):  
Zhanshen Feng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Parameter Optimization ◽  
Visual Processing ◽  
Image Data ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Image Detection

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

scholarly journals PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

2017 ◽  
Author(s):  
Malia A Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C Berry ◽  
Steven T Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

2017 ◽  
Author(s):  
Malia A Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C Berry ◽  
Steven T Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

Automatic Inspection System for Grain Size Distribution Using a Commercial Grind Gauge

2008 ◽  
Vol 381-382 ◽  
pp. 323-324
Author(s):  
M. Yoshida ◽  
Kazuhisa Yanagi ◽  
M.H. Hafiz ◽  
M. Hara
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Ccd Camera ◽  
Inspection System ◽  
Automatic Inspection ◽  
Image Processing Algorithm ◽  
Measurement Results ◽  
Optical Configuration ◽  
Processing Techniques ◽  
Measuring Tool

Grind gauge is a measuring tool for size of grains or particles included in paint or ink. Its geometrical specifications and operational procedure are regulated to some extent and partly standardized in both ISO and JIS. However, only skilled technician can manage to handle it properly and to obtain correct measurement results. The objective of this study is to develop an automatic inspection system for the grain or particle size by use of artificial lighting and CCD camera with image processing techniques. A telecentric lens system was constructed and high resolution CCD camera was attached to it. Advantages of coaxial illumination and oblique illumination methods were revealed and their applicability was examined respectively. The optical configuration to cover the scale and the whole groove width of grind gauge was devised so that the captured image data could contain both grain/particle distribution and height location. A proper software program followed by image processing algorithm was established to reveal particle mark and liner mark.

A Soldering Defect Inspection System of a Special Integrated Circuit Board Based on Computer Vision

2013 ◽  
Vol 650 ◽  
pp. 543-547
Author(s):  
Cong Ling Zhou ◽  
Jun Qiang Wu ◽  
Yong Qiang Wang ◽  
Zeng Pu Xu
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Integrated Circuit ◽  
Visual Inspection ◽  
Ccd Camera ◽  
Structure Design ◽  
Circuit Board ◽  
Inspection System ◽  
Defect Inspection ◽  
Hardware System

This paper introduces a soldering defect inspection system for a special integrated circuit board aided by the computer vision. Space occluder is fixed on this special integrated circuit board, which makes the light blocked from the CCD camera to the chip pins to be inspected. This system can inspect the light blocked soldering defects of the chip pins through the structure design of hardware system and the software system. It is a cheap but automatic soldering defect inspecting system, and can do the soldering defect detection instead of manual visual inspection, and improve the detection speed and stability.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Automated electron tomography: from data collection to image processing

1995 ◽  
Vol 53 ◽  
pp. 26-27
Author(s):  
Weiping Liu ◽  
Jennifer Fung ◽  
W.J. de Ruijter ◽  
Hans Chen ◽  
John W. Sedat ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Collection ◽  
Structural Information ◽  
Imaging Technique ◽  
Electron Tomography ◽  
Ccd Camera ◽  
Automated Data Collection ◽  
Full Control ◽  
Transmission Electron ◽  
Amorphous Structures

Electron tomography is a technique where many projections of an object are collected from the transmission electron microscope (TEM), and are then used to reconstruct the object in its entirety, allowing internal structure to be viewed. As vital as is the 3-D structural information and with no other 3-D imaging technique to compete in its resolution range, electron tomography of amorphous structures has been exercised only sporadically over the last ten years. Its general lack of popularity can be attributed to the tediousness of the entire process starting from the data collection, image processing for reconstruction, and extending to the 3-D image analysis. We have been investing effort to automate all aspects of electron tomography. Our systems of data collection and tomographic image processing will be briefly described.To date, we have developed a second generation automated data collection system based on an SGI workstation (Fig. 1) (The previous version used a micro VAX). The computer takes full control of the microscope operations with its graphical menu driven environment. This is made possible by the direct digital recording of images using the CCD camera.

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