Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Toward color image segmentation in analog VLSI: algorithm and hardware
International Journal of Computer Vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Low Latency Color Segmentation on Embedded Real-Time Systems
DIPES '02 Proceedings of the IFIP 17th World Computer Congress - TC10 Stream on Distributed and Parallel Embedded Systems: Design and Analysis of Distributed Embedded Systems
A Color Segmentation Algorithm for Real-Time Object Localization on Small Embedded Systems
RobVis '01 Proceedings of the International Workshop on Robot Vision
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
A new graph cut-based multiple active contour algorithm without initial contours and seed points
Machine Vision and Applications
Automatic image segmentation by dynamic region growth and multiresolution merging
IEEE Transactions on Image Processing
3-D brain MRI tissue classification on FPGAs
IEEE Transactions on Image Processing
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Pattern Recognition Letters
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Image segmentation is one of the first important and difficult steps of image analysis and computer vision and it is considered as one of the oldest problems in machine vision. Lately, several segmentation algorithms have been developed with features related to thresholding, edge location and region growing to offer an opportunity for the development of faster image/video analysis and recognition systems. In addition, fuzzy-based segmentation algorithms have essentially contributed to synthesis of regions for better representation of objects. These algorithms have minor differences in their performance and they all perform well. Thus, the selection of one algorithm vs. another will be based on subjective criteria, or, driven by the application itself. Here, a low-cost embedded reconfigurable architecture for the Fuzzy-like reasoning segmentation (FRS) method is presented. The FRS method has three stages (smoothing, edge detection and the actual segmentation). The initial smoothing operation is intended to remove noise. The smoother and edge detector algorithms are also included in this processing step. The segmentation algorithm uses edge information and the smoothed image to find segments present within the image. In this work the FRS segmentation algorithm was selected due to its proven good performance on a variety of applications (face detection, motion detection, Automatic Target Recognition (ATR)) and has been developed in a low-cost, reconfigurable computing platform, aiming at low cost applications. In particular, this paper presents the implementation of the smoothing, edge detection and color segmentation algorithms using Stretch S5000 processors and compares them with a software implementation using the Matlab. The new architecture is presented in detail in this work, together with results from standard benchmarks and comparisons to alternative technologies. This is the first such implementation that we know of, having at the same time high throughput, excellent performance (at least in standard benchmarks) and low cost.