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SEGMENTATION OF BEEF JOINT IMAGES USING HISTOGRAM THRESHOLDING
Author(s) -
ZHENG CHAOXIN,
SUN DAWEN,
ZHENG LIYUN
Publication year - 2006
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.2006.00083.x
Subject(s) - balanced histogram thresholding , histogram , thresholding , artificial intelligence , histogram matching , adaptive histogram equalization , pattern recognition (psychology) , segmentation , image histogram , computer science , computer vision , region growing , pixel , image segmentation , entropy (arrow of time) , mathematics , histogram equalization , scale space segmentation , image texture , image (mathematics) , physics , quantum mechanics
Four histogram‐based thresholding methods, i.e., one‐dimensional (1‐D) histogram variance, 1‐D histogram entropy, two‐dimensional (2‐D) histogram variance and 2‐D histogram entropy, were proposed to segment the images of beef joints (raw, cooked and cooled) automatically from the background. The 2‐D histogram‐based methods incorporate a fast algorithm to reduce the calculation time, thus increasing the speed greatly. All the four methods were applied to 15 beef joint images captured from a video camera, and the methods including pixel classification, object overlap and object contrast for the evaluation of segmentation results were then employed to compare the performances or the abilities of the four different segmenting methods. Results indicate that the 2‐D histogram variance thresholding method can accomplish the segmentation task with the most satisfactory performance.