Saliency-Based Candidate Inspection Region Extraction in Tape Automated Bonding

Dümcke, Martina; Takahashi, Hiroki

doi:10.1007/978-3-642-14400-4_15

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Accordingly, this selective attention mechanism can enable us to efficiently capture the defect among the fabric images without any prior knowledge. Therefore, defect detection based on visual saliency has become a popular research topic in recent years (Dümcke and Takahashi, 2010;Zhao et al, 2012;Ahn and Kim, 2010;Guan and Gao, 2014;Zhang et al, 2013). Dümcke and Takahashi (2010) adopted visual saliency to detect probable error regions in a tape automated bonding pattern.…”

Section: Ijcst 275mentioning

confidence: 99%

“…Therefore, defect detection based on visual saliency has become a popular research topic in recent years (Dümcke and Takahashi, 2010;Zhao et al, 2012;Ahn and Kim, 2010;Guan and Gao, 2014;Zhang et al, 2013). Dümcke and Takahashi (2010) adopted visual saliency to detect probable error regions in a tape automated bonding pattern. Zhao et al (2012) extracted the rough saliency region and pixel saliency region by improved frequency-tuned saliency method and gabor filter saliency method, respectively, then maximum entropy was used to segment the defect region.…”

Section: Ijcst 275mentioning

confidence: 99%

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A fabric defect detection algorithm via context-based local texture saliency analysis

Liu

Zhao

et al. 2015

International Journal of Clothing Science and Technology

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Purpose -Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach -In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings -The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value -In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

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