Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Computational Statistics & Data Analysis
Graphical Models and Image Processing
Multivariate Statistical Analysis of Audit Trails for Host-Based Intrusion Detection
IEEE Transactions on Computers
Texture segmentation using wavelet transform
Pattern Recognition Letters
Signature analysis and defect detection in layered manufacturing of ceramic sensors and actuators
Machine Vision and Applications
Pattern Recognition Letters
Wavelet based methods on patterned fabric defect detection
Pattern Recognition
Automated vision system for localizing structural defects in textile fabrics
Pattern Recognition Letters
Fast curvilinear structure extraction and delineation using density estimation
Computer Vision and Image Understanding
Electric contacts inspection using machine vision
Image and Vision Computing
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This paper presents a wavelet characteristic based approach for the automated visual inspection of ripple defects in the surface barrier layer (SBL) chips of ceramic capacitors. Difficulties exist in automatically inspecting ripple defects because of their semi-opaque and unstructured appearances, the gradual changes of their intensity levels, and the low intensity contrast between their surfaces and the rough exterior of a SBL chip. To overcome these difficulties, we first utilize wavelet transform to decompose an image and use wavelet characteristics as texture features to describe surface texture properties. Then, we apply multivariate statistics of Hotelling T^2, Mahalanobis distance D^2, and Chi-square X^2, respectively, to integrate the multiple texture features and judge the existence of defects. Finally, we compare the defect detection performance of the three wavelet-based multivariate statistical models. Experimental results show that the proposed approach (Hotelling T^2) achieves a 93.75% probability of accurately detecting the existence of ripple defects and an approximate 90% probability of correctly segmenting their regions.