Color image edge detection using cluster analysis

Tao, Hai; Huang, Tao

doi:10.1109/icip.1997.648093

Cited by 23 publications

(6 citation statements)

References 5 publications

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“…Pietikainen and Harwood Pietikainen & Harwood (1986) used histograms of vector differences. Yang and Tsai Yang & Tsai (1996) and Tao and Huang Tao & Huang (1997) used vector projections. Trahanias and Venetsanopoulos Trahanias & Venetsanopoulos (1996) used the median of a set of vectors.…”

Section: Vector Methodsmentioning

confidence: 99%

New Robust Obstacle Detection System Using Color Stereo Vision

Cabani¹,

Toulminet²,

Bensrhair³

2011

Advances in Theory and Applications of Stereo Vision

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Section: Vector Methodsmentioning

confidence: 99%

New Robust Obstacle Detection System Using Color Stereo Vision

Cabani¹,

Toulminet²,

Bensrhair³

2011

Advances in Theory and Applications of Stereo Vision

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“…Methods to find feature point extraction in colour images are broadly classified into Vector methods [8][9], Multi-Dimensional Gradient methods [10], and Output Fusion methods [11][12]. In order to extract stable corners in a colour image, we should take into consideration the chrominance information along with luminance.…”

Section: Corner Detection In Color Imagesmentioning

confidence: 99%

Edge and corner detection in grayscale and color images using first Fourier basis

Prakash

Jagannadan

Sharma

et al. 2005

Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision

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In this paper we present an algorithm for edge and corner detection in the greyscale and colour images using Fourier Amplitude Measure. We use the magnitude of the first Fourier basis to detect changes in the image intensity profile. We show the mathematical equivalence of the first Fourier co-efficient to that of the first order partial derivatives. Thus we interpret these coefficients as an estimate of the derivative measure and call them as Fourier Derivative Estimate. We then modify the first order derivative based method used for edge and corner detection using the Fourier Derivative Estimates (FDE) to produce robust edges and corner points. We compare the repeatability rate between FDE method and first order derivative methods in grayscale images. We then show examples where in, the generalization of this method to color images is found to be stable under illumination, blur, color contrast and affine variations, suggesting the utility of this method for image registration and mosaicing purposes.

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“…However, by this approach we cannot find a change in the color of image which does not involve a change in the intensity level. It implies that channels have to be combined in a non-trivial way: added with different signs or be fused non-linearly [1,9]. But these approaches return again a number of gray valued images.…”

Section: Introductionmentioning

confidence: 99%

“…Like in [13] where authors proposed to use the "change point" theory for edge detection in gray valued images. The methods employing clustering to extract new channels which are more suitable for the task of edge detection [1,9] can also be considered as such methods, but not completely. They still need to combine the results obtained for different channels.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches

Verzakov

Pacĺık

Duin

2006

Lecture Notes in Computer Science

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Abstract. Edge detection is well developed area of image analysis. Many various kinds of techniques were designed for one-channel images. Also, a considerable attention was paid to edge detection in color, multispectral, and hyperspectral images. However, there are still many open issues in edge detection in multichannel images. For example, even the definition of multichannel edge is rather empirical and is not well established. In this paper statistical pattern recognition methodology is used to approach the problem of edge detection by considering image pixels as points in a multidimensional feature space. Appropriate multivariate techniques are used to retrieve information which can be useful for edge detection. The proposed approaches were tested on the real-world data.

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Color image edge detection using cluster analysis

Cited by 23 publications

References 5 publications

New Robust Obstacle Detection System Using Color Stereo Vision

New Robust Obstacle Detection System Using Color Stereo Vision

Edge and corner detection in grayscale and color images using first Fourier basis

Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches

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