A new technique for color image segmentation

Amoroso, C.; Ardizzone, Edoardo; Morreale, Vito; Storniolo, Pietro

doi:10.1109/iciap.1999.797620

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…In the cone, the 360º circle denotes hue, radius from 0 to 1 denotes saturation; purity being the most at 0 and least at 1, and the cone's vertical axis represents the dark and the bright sides of the model. Hue is a property that relates to the dominant wavelength in the light spectrum [26]. Hue represents the type of colour that a human can understand.…”

Section: A Colour Modelsmentioning

confidence: 99%

Robot soccer vision: An overview for new learner

Zabawi

Omar

2011

2011 International Conference on Pattern Analysis and Intelligence Robotics

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Vision is a part of a robot soccer system. It completes the system by providing the 'eye' to see what occurs inside the pitch during the game. Similarly as in humans, the sight is then sent to the 'brain'. The control system needs to be processed before making any decision to be sent back to robots on the field to react. The process will be repeated over and over again. This paper discusses the robot soccer vision system which deals with surface theory and some examples of applications used. Suggestions for future improvement are included at the end of this paper.

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Section: A Colour Modelsmentioning

confidence: 99%

Robot soccer vision: An overview for new learner

Zabawi

Omar

2011

2011 International Conference on Pattern Analysis and Intelligence Robotics

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“…The feature band image at 685 nm was used to make a mask due to its significant contrast between the loin-eye area and the background. The intensity histogram valley method (Amoroso, et al, 1999) was used to segment the lean meat against fat and background. With multiple processes of erosion and filling-holes, the loin-eye area and the other small lean area around the loin were extracted (Figure 2).…”

Section: Calculation Of the Average Intensity Of The Feature Band Imagesmentioning

confidence: 99%

Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network

Qiao¹,

Ngadi²,

Wang³

et al. 2007

International Journal of Food Engineering

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Pork quality is usually determined subjectively as PSE, PFN, RFN, RSE and DFD based on color, texture and exudation of the meat. In this study, a hyperspectral-imaging-based technique was developed to achieve rapid, accurate and objective assessment of pork quality. The principal component analysis (PCA) and stepwise operation methods were used to select feature waveband from the entire spectral wavelengths (430 to 980 nm). Then the feature waveband images were extracted at the selected feature wavebands from raw hyperspectral images, and the average reflectance (R) was calculated within the whole loin-eye area. Artificial neural network was used to classify these groups. Results showed that PCA analysis had a better performance than that of stepwise operation for feature waveband images selection. The 1st derivative data gave a better result than that of mean reflectance spectra data. The best classified result was 87.5% correction. The error frequency showed that RSE samples were easier to classify. The PFN and PSE samples were difficult to separate from each other.

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“…Many approaches to color segmentation have been developed, including fuzzy c-means ͑FCM͒, 2-4 region growing and merging, 5-7 region growing and edge information, [8][9][10][11] the Markov random field ͑MRF͒, 12-15 the neural network, 16 -18 histogram-based methods, [19][20][21][22][23][24] and the physically based method. 25 For a more detailed review of gray and color image segmentation methods, the reader is referred to a general review given in Ref.…”

Section: Introductionmentioning

confidence: 99%

Color image segmentation using a self-organizing map algorithm

Chen

2002

J. Electron. Imaging

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A color image segmentation methodology based on a self-organizing map (SOM) is proposed. The method developed takes into account the color similarity and spatial relationship of objects within an image. According to the features of color similarity, an image is first segmented into coarse cluster regions. The resulting regions are then treated by computing the spatial distance between any two cluster regions, and the SOM with a labeling process is applied. In this paper, the selection of the parameters for the SOM algorithm was also investigated experimentally. The experimental results show that the proposed system is feasible, and that the segmented object regions are similar to those perceived by human vision.

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A new technique for color image segmentation

Cited by 5 publications

References 11 publications

Robot soccer vision: An overview for new learner

Robot soccer vision: An overview for new learner

Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network

Color image segmentation using a self-organizing map algorithm

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