Fast and robust Segmentation of natural color scenes

Rehrmann, Volker; Priese, Lutz

doi:10.1007/3-540-63930-6_172

Cited by 47 publications

(40 citation statements)

References 6 publications

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“…We have compared the performance of RCRM with that of the Color Structure Code CSC [8]), the Edge Detection and Image SegmentatiON EDISON [9], and the Recursive Hierarchical Image Segmentation RHSEG [10]. The results are summarized in Table 1.…”

Section: Fig 2 Test Imagesmentioning

confidence: 99%

Image Segmentation Based on Representative Colors Detection and Region Merging

Ramella

Baja

2013

Lecture Notes in Computer Science

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Abstract. We present a color image segmentation algorithm, RCRM, based on the detection of Representative Colors and on Region Merging. The 3D color histogram of the RGB input image is built. Colors are processed in decreasing frequency order and a grouping process is accomplished to gather in the same cluster all colors that are close enough to the current color. Colormapping is done to originate a preliminary image segmentation. Segmentation regions having small size undergo a merging process. Merging is actually accomplished only for adjacent regions whose colors do not significantly differ. The parameters involved by the algorithm are set automatically by taking into account color distribution in the input image and geometrical features of the regions into which the image is partitioned. The algorithm has been tested on a large number of RGB color images originating satisfactory results.

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Section: Fig 2 Test Imagesmentioning

confidence: 99%

Image Segmentation Based on Representative Colors Detection and Region Merging

Ramella

Baja

2013

Lecture Notes in Computer Science

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“…The advantage of the AGS is that it can find groupings in spaces with a concept of a neighborhood but without a topology. The AGS follows ideas of the CSC (Color Structure Code) segmentation technique of Priese and Rehrmann [5] and works as follows:…”

Section: Cluster Analysis With the Intersection Point Neighborhoodmentioning

confidence: 99%

Vanishing Point Detection with an Intersection Point Neighborhood

Schmitt

Priese

2009

Discrete Geometry for Computer Imagery

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“…In order to understand this hierarchy we first introduce the hierarchical hexagonal topology (see Figure 1(a)) of the 2D-CSC (Rehrmann 1998). The topology is formed by so called islands in different levels.…”

Section: D Cell Structure Codementioning

confidence: 99%

Improved Segmentation of MR Brain Images Including Bias Field Correction Based on 3d-CSC

Wang

Sturm

Schmitt

et al. 2006

Proceedings of the First International Conference on Computer Vision Theory and Applications

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Abstract:The 3D Cell Structure Code (3D-CSC) is a fast region growing technique. However, directly adapted for segmentation of magnetic resonance (MR) brain images it has some limitations due to the variability of brain anatomical structure and the degradation of MR images by intensity inhomogeneities and noise. In this paper an improved approach is proposed. It starts with a preprocessing step which contains a 3D Kuwahara filter to reduce noise and a bias correction method to compensate intensity inhomogeneities. Next the 3D-CSC is applied, where a required similarity threshold is chosen automatically. In order to recognize gray and white matter, a histogram-based classification is applied. Morphological operations are used to break small bridges connecting gray value similar non-brain tissues with the gray matter. 8 real and 10 simulated T1-weighted MR images were evaluated to validate the performance of our method.

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Fast and robust Segmentation of natural color scenes

Cited by 47 publications

References 6 publications

Image Segmentation Based on Representative Colors Detection and Region Merging

Image Segmentation Based on Representative Colors Detection and Region Merging

Vanishing Point Detection with an Intersection Point Neighborhood

Improved Segmentation of MR Brain Images Including Bias Field Correction Based on 3d-CSC

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