Machine Vision Based Image Processing Techniques for Surface Finish and Defect Inspection in a Grinding Process

2018 ◽  
Vol 5 (5) ◽  
pp. 12792-12802 ◽  
Author(s):  
R. Manish ◽  
Adigopula Venkatesh ◽  
S. Denis Ashok
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Surface Finish ◽  
Grinding Process ◽  
Defect Inspection ◽  
Image Processing Techniques ◽  
Processing Techniques

Certain Aspects of Machine Vision in the Arts

2011 ◽  
pp. 236-256
Author(s):  
Marc Bohlen
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Digital Imagery ◽  
Art Practice ◽  
Short Introduction ◽  
Computational Approaches ◽  
Image Processing Techniques ◽  
The Arts ◽  
Processing Techniques

This chapter attempts to consider the consequences of machine vision technologies for the role of the image in the visual arts. After a short introduction, the text gives a practical overview of image processing techniques that are relevant in surveillance, installation, and information art practice. Example work by practitioners in the field contextualizes these more technical descriptions and shows how computational approaches to digital imagery can radically expand the use of the image in the arts. A final note on possible future areas of investigation is included.

A New Measurement System of Yarn Appearance Based on Machine Vision

2014 ◽  
Vol 945-949 ◽  
pp. 1810-1814
Author(s):  
Jun Juan Li ◽  
Chen Wang ◽  
Wen Xiao Tu ◽  
Bao Qi Zuo
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Measurement System ◽  
Coefficient Of Variation ◽  
Suitable Method ◽  
Main Body ◽  
Image Processing Techniques ◽  
Image Acquisition System ◽  
Processing Techniques ◽  
Morphological Opening

In this paper, a new yarn appearance measurement system based on machine vision is introduced. The yarn images are continuously captured by image acquisition system. To extract the main body of the yarn accurately, the yarn images are processed sequentially with threshold segmentation and morphological opening operation. Then the coefficient of variation (CV value) of diameter is calculated to characterize the yarn evenness. The measurement process achieves result (CV value) which can be compared with USTER evenness tester by image processing techniques. By comparing different methods which use different algorithms, a suitable method is chosen to be used in this new measurement system. Then a more accurate, more efficient and faster measurement system will meet requirements in the future.

Automatic Inspection of Typical Microstructure Defects Based on Image Processing Techniques

2010 ◽  
Vol 44-47 ◽  
pp. 2622-2626
Author(s):  
Xiao Hui Chen ◽  
Xiao Jun Liu
Keyword(s):  
Image Processing ◽  
Mass Production ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Structural Defects ◽  
Automatic Inspection ◽  
Mems Devices ◽  
Defect Inspection ◽  
Image Processing Techniques ◽  
Processing Techniques

Micro-structural defects influence the performance of MEMS devices. For effective quality monitoring and assurance in mass-production of MEMS devices, it is necessary to conduct automatic defect inspection. In this paper, an approach based on image processing for automatic microstructure defect inspection is presented. In the approach, an Influence Region Template is built based on a reference microstructure image while it preserves the framework and restrains the tiny distortion of the reference, then the test microstructure image is compared with the Influencing Region Template for quality evaluation. By the approach, typical defects such as blocks in trenches or channels, transfixions between trenches or channels and missing of some part and so on, are detected and classified automatically, while some distortions which do not affect the function of the microstructures are ignored. Testing results show that the approach can conduct a high quality assurance while it can endure a given distortion tolerance of microstructures.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Rice Panicle Blast Detection and Grading Based on Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 165-168 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Swaraj Kumar Sahu ◽  
Nalini Kanta Barpanda ◽  
Amiya Kumar Rath
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Panicle Blast ◽  
Processing Techniques

Identification of Cucumber Leaf Disease using Image Processing Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 1493-1499
Author(s):  
Shrutika.C.Rampure . ◽  
Dr. Vindhya .P. Malagi ◽  
Dr. Ramesh Babu D.R
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Leaf Disease ◽  
Processing Techniques
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