Reaching behind Specular Highlights by Registration of Two Images of Broiler Viscera

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

The manual postmortem inspection of broilers and their viscera is becoming a bottleneck as the slaughter rate increases. Computer vision can assist veterinarians during the inspection, but specular highlights can hide crucial details when inspecting for diseases on the viscera set. This study aims to restore details behind these specular highlights by capturing two images of the same viscera using shifting light positions. The dataset consists of images captured in-line at a poultry processing plant. The method achieves an average SSIM score of 0.96 over a test set of 100 image sets. The result is visually pleasing images with correct textural information instead of specular highlights.
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Details

The manual postmortem inspection of broilers and their viscera is becoming a bottleneck as the slaughter rate increases. Computer vision can assist veterinarians during the inspection, but specular highlights can hide crucial details when inspecting for diseases on the viscera set. This study aims to restore details behind these specular highlights by capturing two images of the same viscera using shifting light positions. The dataset consists of images captured in-line at a poultry processing plant. The method achieves an average SSIM score of 0.96 over a test set of 100 image sets. The result is visually pleasing images with correct textural information instead of specular highlights.
Original languageEnglish
Title of host publicationProceedings of the 14th Asian Conference on Computer Vision
Publication date2019
Publication statusAccepted/In press - 2019
Publication categoryResearch
Peer-reviewedYes
ID: 292793194