Comparing Threshold-Selection Methods for Image Segmentation: Application to Defect Detection in Automated Visual Inspection Systems

López-Leyva, Rafael; Rojas-Domínguez, Alfonso; Flores-Mendoza, Juan Pablo; Casillas, Miguel Ángel; Santiago-Montero, Raúl

doi:10.1007/978-3-319-39393-3_4

Cited by 1 publication

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“…Otherwise, all the pixels that compose the scene could be assigned to a single class. In industrial applications usually the illumination and other scene properties can be easily controlled [39]. When processing the image, the TT-Otsu is used to calculate the optimal thresholds T and T , and the image is binarized to facilitate subsequent morphological operations.…”

Section: ) Test Methods and Processmentioning

confidence: 99%

Research on Double Edge Detection Method of Midsole Based on Improved Otsu Method

Fang

Chen

2020

IEEE Access

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The midsole is an important part of the shoe, but in its industrial production, the surface quality of the midsole currently relies on manual testing. It cost high and cannot meet the needs of industrial online real-time detection. To realize online surface defect detection, extracting the double edges of the midsole resulting from its special structure becomes an indispensable pre-work. This paper proposes a twostep Otsu method (TT-Otsu) to extract double edges of products. This method adopts the improved Otsu method to process the midsole image in two steps. It respectively combines with the Weighted Object Variance method (WOV) and the Neighborhood Valley-Emphasis method (NVE) to calculate the optimal threshold. Then the image is segmented to extract the edges and the misclassification error (ME) is 0.0007. To ensure the accuracy of the edge detection, the neighborhood gradient extreme value discrimination method is used to realize the local self-checking and make appropriate adjustments to the deviated edge. The false positive rate (FPR) and the false negative rate (FNR) of TT-Otsu are approximately equal to 5%. This method can effectively and clearly extract the double edges of the midsole. The precision rate is 95.61%, the average running time is 1.8s. The experiment demonstrates that the proposed method in this paper has good detection performance and good applicability.

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Section: ) Test Methods and Processmentioning

confidence: 99%