2014/01/24 by Yu‐Kun Lai, Paul L. Rosin · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Artificial intelligence #Balanced histogram thresholding #Computer science #Computer vision #Feature (linguistics) #Histogram #Histogram matching #Image (mathematics) #Image Enhancement Techniques #Image histogram #Image segmentation #Image texture #Otsu's method #Pattern recognition (psychology) #Segmentation #Thresholding
paper · doi:10.1109/tip.2013.2297014
openalex publication_date 2014/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
Otsu's algorithm for thresholding images is widely used, and the computational complexity of determining the threshold from the histogram is O(N) where N is the number of histogram bins. When the algorithm is adapted to circular rather than linear histograms then two thresholds are required for binary thresholding. We show that, surprisingly, it is still possible to determine the optimal threshold in O(N) time. The efficient optimal algorithm is over 300 times faster than traditional approaches for typical histograms and is thus particularly suitable for real-time applications. We further demonstrate the usefulness of circular thresholding using the adapted Otsu criterion for various applications, including analysis of optical flow data, indoor/outdoor image classification, and non-photorealistic rendering. In particular, by combining circular Otsu feature with other colour/texture features, a 96.9% correct rate is obtained for indoor/outdoor classification on the well known IITM-SCID2 data set, outperforming the state-of-the-art result by 4.3%.