Rule based concave curvature segmentation for touching rice grains in binary digital images

Hobson, D.M.; Carter, R M; Yan, Yong

doi:10.1109/imtc.2009.5168727

Cited by 8 publications

(1 citation statement)

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“…Thus, we obtained a distance vector (Hobson et al 2009) with m elements D=(D 1 ,D 2 ,...,D m ). After computing first derivative of the distance vector D, the velocity vector is obtained v=D′.…”

Section: Image Acquisition and Pattern Recognitionmentioning

confidence: 99%

Identification of Broken Rice Kernels Using Image Analysis Techniques Combined with Velocity Representation Method

Lin

Chen

2010

Food Bioprocess Technol

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The percentage of whole kernels remaining after milling is one of the most important physical characteristics of rice quality. A method based on flatbed scanning and image analysis was developed for the identification of broken rice kernels. Velocity representation method was developed for pattern recognition based on the contour characteristics of the rice kernels. The similarity of the boundary features of the image of rice kernel was measured by similarity coefficient, which was used to identify the broken rice kernel by comparing with threshold. High recognition rates for three rice varieties were reached by this method with 96.7% for Thailand rice, 98.73% for Pearl rice, and 97.14% for Changlixiang rice, respectively, and the recognition rate could be improved by the adjustment of the similarity coefficient threshold. Because the comprehensive boundary features were the basis for the classification, this method could be more accurate compared to other methods using the single dimension feature.

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Section: Image Acquisition and Pattern Recognitionmentioning

confidence: 99%