Open AccessA Fast Otsu Thresholding Method Based on an Improved 2D Histogram
Author(s) -
Yanli Tan,
Yue Zhao
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.102
Subject(s) - balanced histogram thresholding , thresholding , histogram , artificial intelligence , image segmentation , pixel , otsu's method , image histogram , histogram matching , computer vision , region growing , computer science , pattern recognition (psychology) , computation , segmentation , adaptive histogram equalization , computational complexity theory , mathematics , image (mathematics) , scale space segmentation , histogram equalization , algorithm , image texture
The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the optimal threshold, thus reducing the amount of computation. The simulation results demonstrate that the proposed algorithm has better performance in image segmentation, with the increased computational speed and improved real-time capability.