Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Integration of Image Segmentation Maps using Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
IEEE Transactions on Software Engineering
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Region growing: a new approach
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Effective defect classification for flat display panel film images
Proceedings of the 2009 International Conference on Hybrid Information Technology
Expert Systems with Applications: An International Journal
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In this paper, we present an effective defect inspection system that identifies film defects and determines their types in order to produce polarized films for TFT-LCD (thin film transistor - liquid crystal display). The proposed system is designed and implemented to find defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects using template matching techniques. We extract features of the defects such as shape and texture, and compare them to the features of referential defect images stored in a template database. Experimental results using the proposed system show that it identifies defects of test images effectively (Recall=1.00, Precision=0.95) and efficiently (Average response time=0.64s), and also achieves a high correctness in determining the types (Recall=0.95, Precision=0.96) for five classes of defects. In addition the experiment shows that our system is fairly robust with respect to the rotational transformation, achieving the desirable property of the rotation invariance.