0000000001130377
AUTHOR
Soufiane Rital
A segmentation algorithm for noisy images
International audience; This paper presents a segmentation algorithm for gray-level images and addresses issues related to its performance on noisy images. It formulates an image segmentation problem as a partition of a weighted image neighborhood hypergraph. To overcome the computational difficulty of directly solving this problem, a multilevel hypergraph partitioning has been used. To evaluate the algorithm, we have studied how noise affects the performance of the algorithm. The alpha-stable noise is considered and its effects on the algorithm are studied. Key words : graph, hypergraph, neighborhood hypergraph, multilevel hypergraph partitioning, image segmentation and noise removal.
K-Way Hypergraph Partitioning And Color Image Segmentation
International audience
Color Image Segmentation: The Hypergraph Framework
International audience; Color Image Segmentation: The Hypergraph Framework
Weighted Adaptive Neighborhood HypergraphPartitioning for Image Segmentation
International audience; The aim of this paper is to present an improvement of a previously published algorithm. The proposed approach is performed in two steps. In the first step, we generate the Weighted Adaptive Neighborhood Hypergraph (WAINH) of the given gray-scale image. In the second step, we partition the WAINH using a multilevel hypergraph partitioning technique. To evaluate the algorithm performances, experiments were carried out on medical and natural images. The results show that the proposed segmentation approach is more accurate than the graph based segmentation algorithm using normalized cut criteria.Key words hypergraph, neighborhood hypergraph, hypergraph partitioning, image…
Application of Adaptive Hypergraph Model to Impulsive Noise Detection
In this paper, using hypergraph theory, we introduce an image model called Adaptive Image Neighborhood Hypergraph (AINH). From this model we propose a combinatorial definition of noisy data. A detection procedure is used to classify the hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses an estimation procedure to remove the effects of the noise. Extensive simulations show that the proposed scheme consistently works well in suppressing of impulsive noise.
A Combinatorial Color Edge Detector
In this paper, we present an edge detection approach in color image using neighborhood hypergraph. The edge structure is detected by a structural model. The Color Image Neighborhood Hypergraph (CINH) representation is first computed, then the hyperedges of CINH are classified into noise or edge based on hypergraph properties. To evaluate the algorithm performance, experiments were carried out on synthetic and real color images corrupted by alpha-stable noise. The results show that the proposed edge detector finds the edges properly from color images.
Neighborhood Hypergraph Partitioning for Image Segmentation
International audience; The aim of this paper is to introduce a multilevel neighborhoodhypergraph partitioning for image segmentation. Our proposedapproach uses the image neighborhood hypergraph model introduced inour last works and the algorithm of multilevel hypergraphpartitioning introduced by George Karypis. To evaluate the algorithmperformance, experiments were carried out on a group of gray scaleimages. The results show that the proposed segmentation approachfind the region properly from images as compared to imagesegmentation algorithm using normalized cut criteria.Key words :Graph, Hypergraph, Neighborhood hypergraph, multilevel hypergraph partitioning, image segmentation, edge dete…
Spatiocolorimetric neighborhood hypergraph and Image Processing Applications : Noise Removal and Edge Detection.
In this document, we are interested in image modeling by the means of the hypergraph theory. Our contribution is essentially centered on the determination of the properties resulting from this theory and on the analysis from their adequacy with image problems, particularly edge and noise detection.First, we study the image spatiocolorimetric neighborhood hypergraph representation. Three representations are respectively presented incorporating global properties, local properties and similarity functions. Then, we use the hypergraph properties generated by the representation in order to define the structural models of noise and edge. This enables us to deduce the algorithms of noise suppressi…