A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Image processing by simulated annealing
IBM Journal of Research and Development - High-density magnetic recording
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Real Time Object Segmentation and Tracking Algorithm and its Parallel Hardware Architecture
Journal of VLSI Signal Processing Systems
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Fast connected-component labeling
Pattern Recognition
Hi-index | 0.00 |
Efficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algorithm finds the equilibrium states of a Potts model in the superparamagnetic phase of the system. Our method maps perfectly onto the Graphics Processing Unit (GPU) architecture and has been implemented using the framework NVIDIA Compute Unified Device Architecture (CUDA). For images of 256 × 320 pixels we obtained a frame rate of 30 Hz that demonstrates the applicability of the algorithm to video-processing tasks in real-time.