Linear colour segmentation revisited

In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard’s DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.

[1]  Alain Trémeau,et al.  Regions adjacency graph applied to color image segmentation , 2000, IEEE Trans. Image Process..

[2]  Glenn Healey,et al.  Using color for geometry-insensitive segmentation , 1989 .

[3]  J. Morel,et al.  A multiscale algorithm for image segmentation by variational method , 1994 .

[4]  Dmitry P. Nikolaev,et al.  Vanishing points detection using combination of fast Hough transform and deep learning , 2018, International Conference on Machine Vision.

[5]  Shoji Tominaga,et al.  Dichromatic reflection models for a variety of materials , 1994 .

[6]  Huang Yumin,et al.  A PHYSICAL APPROACH TO COLOR IMAGE UNDERSTANDING , 1991 .

[7]  Chang-Yeong Kim,et al.  New image segmentation method using mode finding, multi-link clustering, and region graph analysis , 2004, IS&T/SPIE Electronic Imaging.

[8]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[9]  Steven A. Shafer,et al.  Physics-Based Segmentation of Complex Objects Using Multiple Hypotheses of Image Formation , 1997, Comput. Vis. Image Underst..

[10]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[11]  Matti Pietikäinen,et al.  Physics-based face database for color research , 2000, J. Electronic Imaging.

[12]  Brian V. Funt,et al.  A data set for color research , 2002 .

[13]  Sebastian Budzan,et al.  Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system , 2018, International Conference on Machine Vision.

[14]  S. Shafer,et al.  Method for estimating scene parameters from color histograms , 1994 .

[15]  José García Rodríguez,et al.  A Review on Deep Learning Techniques Applied to Semantic Segmentation , 2017, ArXiv.

[16]  Aggelos K. Katsaggelos,et al.  Hybrid image segmentation using watersheds and fast region merging , 1998, IEEE Trans. Image Process..

[17]  David J. Crisp,et al.  Fast Region Merging Algorithms for Image Segmentation , 2001 .

[18]  M H Brill,et al.  Image segmentation by object color: a unifying framework and connection to color constancy. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[19]  Penglang Shui,et al.  Fast SAR Image Segmentation via Merging Cost With Relative Common Boundary Length Penalty , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[21]  D. P. Nikolaev,et al.  Aerial images visual localization on a vector map using color-texture segmentation , 2018, International Conference on Machine Vision.

[22]  Joost van de Weijer,et al.  Computational Color Constancy: Survey and Experiments , 2011, IEEE Transactions on Image Processing.

[23]  Laure Tougne,et al.  Segmentation algorithm on smartphone dual camera: application to plant organs in the wild , 2018, International Conference on Machine Vision.

[24]  Takeo Kanade,et al.  The measurement of highlights in color images , 1988, International Journal of Computer Vision.

[25]  Min Bai,et al.  Deep Watershed Transform for Instance Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Hsien-Che Lee,et al.  Modeling Light Reflection for Computer Color Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Erik Cuevas,et al.  Color Segmentation Using LVQ Neural Networks , 2017 .

[28]  Jinpeng Zhang,et al.  Unsupervised color texture segmentation using active contour model and oscillating information , 2017, International Conference on Machine Vision.

[29]  Dmitry P. Nikolaev,et al.  Linear color segmentation and its implementation , 2004, Comput. Vis. Image Underst..

[30]  Robert B. Fisher,et al.  3D color homography model for photo-realistic color transfer re-coding , 2019, The Visual Computer.

[31]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..