Computer vision for automatic inspection of complex metal patterns on multichip modules (MCM-D)

1995 ◽  
Vol 18 (4) ◽  
pp. 675-684 ◽  
Author(s):  
M.E. Scaman ◽  
L. Economikos
Keyword(s):  
Computer Vision ◽  
Multichip Modules ◽  
Automatic Inspection ◽  
Complex Metal

A New Method for Railway Fastener Detection Using the Symmetrical Image and Its EA-HOG Feature

2019 ◽  
Vol 34 (02) ◽  
pp. 2055006
Author(s):  
Jiajia Liu ◽  
Jianying Yuan ◽  
Yongfang Jia
Keyword(s):  
Computer Vision ◽  
Sparse Representation ◽  
Efficient Method ◽  
Important Task ◽  
New Method ◽  
Automatic Inspection ◽  
Test Image ◽  
Recognition Result ◽  
The Status ◽  
Railway Operation

Railway fastener recognition and detection is an important task for railway operation safety. However, the current automatic inspection methods based on computer vision can effectively detect the intact or completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In our method, we exploit the EA-HOG feature fastener image, generate two symmetrical images of original test image and turn the detection of the original test image into the detection of two symmetrical images, then integrate the two recognition results of symmetrical image to reach exact recognition of original test image. The potential advantages of the proposed method are as follows: First, we propose a simple yet efficient method to extract the fastener edge, as well as the EA-HOG feature of the fastener image. Second, the symmetry images indeed reflect some possible appearance of the fastener image which are not shown in the original images, these changes are helpful for us to judge the status of the symmetry samples based on the improved sparse representation algorithm and then obtain an exact judgment of the original test image by combining the two corresponding judgments of its symmetry images. The experiment results show that the proposed approach achieves a rather high recognition result and meets the demand of railway fastener detection.

Computer Vision Based Technique for Surface Defect Detection of Apples

2014 ◽  
pp. 111-121
Author(s):  
C. J. Prabhakar ◽  
S. H. Mohana
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Quality Standards ◽  
Production Costs ◽  
Automatic Inspection ◽  
Segmentation Technique ◽  
Surface Defect Detection ◽  
Fruit Grading

The automatic inspection of quality in fruits is becoming of paramount importance in order to decrease production costs and increase quality standards. Computer vision techniques are used in fruit industry for fruit grading, sorting, and defect detection. In this chapter, we review recent approaches for automatic inspection of quality in fruits using computer vision techniques. Particularly, we focus on the review of advances in computer vision techniques for automatic inspection of quality of apples based on surface defects. Finally, we present our approach to estimate the defects on the surface of an apple using grow-cut and multi-threshold based segmentation technique. The experimental results show that our method effectively estimates the defects on the surface of apples significantly more effectively than color based segmentation technique.

scholarly journals SUBPIXEL IMAGE REGISTRATION USING CIRCULAR FIDUCIALS

1994 ◽  
Vol 04 (04) ◽  
pp. 403-422 ◽  
Author(s):  
ALON EFRAT ◽  
CRAIG GOTSMAN
Keyword(s):  
Computer Vision ◽  
Number Theory ◽  
Image Registration ◽  
Practical Importance ◽  
Efficient Algorithms ◽  
Automatic Inspection ◽  
Rotation Invariant ◽  
Registration Accuracy ◽  
Geometric Methods ◽  
Lattice Geometry

The design of fiducials for precise image registration is of major practical importance in computer vision, especially in automatic inspection applications. We analyze the sub-pixel registration accuracy that can, and cannot, be achieved by some rotation-invariant fiducials, and present and analyze efficient algorithms for the registration procedure. We rely on some old and new results from lattice geometry and number theory and efficient computational-geometric methods.

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