Fuzzy region-growing segmentation of natural images using local fractal dimension

Maeda, Junji; Novianto, Sonny; Miyashita, A.; Saga, Sato; Suzuki, Yusuke

doi:10.1109/icpr.1998.711855

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…Cette dernière est calculée pour chaque pixel de l'image ou pour des petites fenêtres locales [107]. La méthode a permis de segmenter différentes textures avec succès.…”

Section: Segmentationunclassified

La géométrie fractale pour l’analyse de signaux médicaux : état de l’art

Lopes

Dubois

Bhouri

et al. 2010

IRBM

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“…Cette dernière est calculée pour chaque pixel de l'image ou pour des petites fenêtres locales [107]. La méthode a permis de segmenter différentes textures avec succès.…”

Section: Segmentationunclassified

La géométrie fractale pour l’analyse de signaux médicaux : état de l’art

Lopes

Dubois

Bhouri

et al. 2010

IRBM

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“…Even though there are many restrictions for these, any useful techniques for image segmentation are still being focused in the fields of image processing and pattern recognition. Traditionally, some well-known algorithms concerned with image segmentation are: gray level thresholding (Taxt et al, 1989), relaxation procedures (Hansen and Higgins, 1997), region growing and merging (Moghaddamzadeh and Bourbakis, 1997, Tremeau and Borel, 1997, Maeda, et al, 1998, fuzzy clustering (Bezdek, 1981, Cannon et al, 1986, Chen et al, 1995a, neural networks , Campadelli et al, 1997, Cinque et al, 1998, and multiresolution processing using the quadtree construction method (Liu and Yang, 1994), for example. A general review of image segmentation techniques can be found in (Pal and Pal, 1993).…”

Section: Introductionmentioning

confidence: 99%

Primary‐view perception on a gray image: Region segmentation and association

Huang

Chen

Hsu

2001

Journal of the Chinese Institute of Engineers

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The segmentation of scenes into perceptually meaningful partitions possessing a useful relationship among them is of great importance in image understanding. In this paper, an effective approach performing image segmentation and association among the segmented regions for a gray image is presented. An unsupervised segmentation process using a self-organization map (SOM) and spatial-distance computation is presented for the segmentation of clusters in our understanding system. An association process is developed for the construction of spatial relationships among the attended regions, which are obtained from the segmented clusters using fuzzy relations. The algorithms for each process are presented and exemplified with a series of illustrations. This approach is applied to simulating the primary-view perception on a natural gray image. Experiments show that the proposed approach is feasible. A possible linguistic presentation, based on the attended regions and their relationships, is given for every test image.

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“…The existing target detection and image segmentation methods include fractal coding 2 , local fractal dimension 3,4 , lacunatity feature 5 , directional fractal dimension 6,7 , extended fractal analysis 8 , multifractal analysis 9,10 and others such as combining with wavelet and neural network etc. The fractal dimension of the man-made object is usually lower than the background's because mostly it is smoother than natural background.…”

Section: Introductionmentioning

confidence: 99%

Fractal dimension computation with the Parzon window and its application in target detection

Wang

Shi

Huang

2005

Visual Communications and Image Processing 2005

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The automatic target detection under natural backgrounds is an important topic in the field of automatic target recognition. Fractal dimension is generally related to the roughness of the surface. The fractal dimension of the man-made object is usually lower than the background's because mostly it is smoother than natural background. This feature can be used to detect the target automatically. In the computing of the fractal dimension, the irregular values often appear at the boundary of the different textures in the image. This phenomenon can be called 'edge effect'. It may result in the difficulty in the followed image processing such as thresholding and cluster segmentation. The main reason of the edge effect is the same contribution of the every pixel in the neighborhood of the pixel where the fractal dimension being calculated. In this paper, in order to weaken the 'edge effect' in the fractal dimension computation, a 2-D Parzon window is designed. The accuracy of fractal dimension calculated after multiplied by the Parzon window is discussed, and a new algorithm is proposed to apply in the automatic target detection. The proposed automatic target detection algorithm is adopted in the experiments in images under complex land or sea backgrounds. The correctly detection rate is above 95%. The robust of this algorithm is represented in the cases of the variety of the light, rotation, size changing and occlusion of the target.

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Fuzzy region-growing segmentation of natural images using local fractal dimension

Cited by 12 publications

References 6 publications

La géométrie fractale pour l’analyse de signaux médicaux : état de l’art

La géométrie fractale pour l’analyse de signaux médicaux : état de l’art

Primary‐view perception on a gray image: Region segmentation and association

Fractal dimension computation with the Parzon window and its application in target detection

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