Color image segmentation using a self-organizing map algorithm

Chen, Yung‐Sheng

doi:10.1117/1.1455007

Cited by 18 publications

(2 citation statements)

References 31 publications

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“…Huang et al 22 developed a color image segmentation methodology that employed a two-stage SOM-based ANN. The algorithm is initiated by an RGB to HVC (hue-valuechroma) color conversion of the input image, which is employed by an SOM to identify a large initial set of Fig.…”

Section: Spatially Blind Approachesmentioning

confidence: 99%

Survey of contemporary trends in color image segmentation

Vantaram

Saber

2012

J. Electron. Imaging

108

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In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

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Section: Spatially Blind Approachesmentioning

confidence: 99%

Survey of contemporary trends in color image segmentation

Vantaram

Saber

2012

J. Electron. Imaging

108

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“…The richness of a dataset can be enhanced with these advanced analyses and interpretations. Incorporating color information can be used for texture mapping, while intensity data can provide information about surface or material properties [ 15 ]. Researchers have employed low-cost spherical cameras, commonly used in projects such as virtual reality and street view mapping, for three-dimensional reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping

Rezaei,

Maier,

Arefi

2024

Sensors

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Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.

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Image Dominant Colors Estimation and Color Reduction Via a New Self-growing and Self-organized Neural Gas

Atsalakis

Papamarkos

Andreadis

2005

Lecture Notes in Computer Science

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A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the development of a new neural network classifier that combines the advantages of the Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (SOFM) neural networks. We call the new neural network: Self-Growing and Self-Organized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. Besides, a new method is proposed for the Estimation of the Most Important of already created Classes (EMIC). The combination of SGONG and EMIC in color images results in retaining the isolated and significant colors with the minimum number of color classes. The above techniques are able to be fed by both color and spatial features. For this reason a similarity function is used for vector comparison. To speed up the entire algorithm and to reduce memory requirements, a fractal scanning sub-sampling technique is used. The method is applicable to any type of color images and it can accommodate any type of color space.

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Color image segmentation using a self-organizing map algorithm

Cited by 18 publications

References 31 publications

Survey of contemporary trends in color image segmentation

Survey of contemporary trends in color image segmentation

Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping

Image Dominant Colors Estimation and Color Reduction Via a New Self-growing and Self-organized Neural Gas

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