Tuning the smoothness of the recursive median filter

Burian, A.; Kuosmanen, Pauli

doi:10.1109/tsp.2002.1011204

Cited by 34 publications

(19 citation statements)

References 20 publications

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“…Skin candidate region which is less than one percent of the largest skin candidate region is then rejected. A median filter provides a powerful method for smoothing signals and images by repeating the median filtering the root pixel, which is invariant to further filtering is found [11]. This convergence property of repeated median filtering has been used in some signal processing applications, such as speech processing [12] and image coding [13].…”

Section: ) Skin Color Filteringmentioning

confidence: 99%

Face detection based on skin color modeling and modified Hausdorff distance

Alajel

Wang

Leis

2011

2011 IEEE Consumer Communications and Networking Conference (CCNC)

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Abstract-This paper presents a new face detection approach which is capable of detecting human faces from complex backgrounds. A new skin color modeling process is applied to the face segmentation process. Image enhancement is then used to improve the features of face candidates before feeding to the face object classifier which is based on a modified Hausdorff distance. The overall performance of the face detection system is evaluated and achieved a success rate of 87.5 %.

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Section: ) Skin Color Filteringmentioning

confidence: 99%

Face detection based on skin color modeling and modified Hausdorff distance

Alajel

Wang

Leis

2011

2011 IEEE Consumer Communications and Networking Conference (CCNC)

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“…Table II summarizes the total number of operations for the most important solutions designed within the SWVF framework. Note that the use of non-recursive or recursive processing mode keeps the computational complexity of the operators unchanged (Burian and Kuosmanen, 2002). The cost of the implementation of the Since the cDNA image processing is commonly performed using a PC, the efficiency of the SWVF operators can also be measured, in terms of the execution time in a typical computing platform.…”

Section: B Examination Of the Performance Using Phantommentioning

confidence: 99%

“…The root signal of the cDNA microarray image is an image obtained from the input by repeatedly filtering it until no more changes occur (Burian and Kuosmanen, 2002). Equivalently, root signals can be defined as signals invariant to further processing by the same filtering operator (Astola et al, 1987).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, C (r,s) ¼ {y (g,h) , (i,j) [ } consists of the input cDNA vectors x (i,j) and vectors y (g,h) outputted by the processing filter in the previous centers of the sliding window. Recursive filters have usually provided better smoothing compared to non-recursive filters at the expense of increased distortion (Burian and Kuosmanen, 2002). To keep the subsequent discussion simple, the conventional, non-recursive filtering formulation is adopted in the paper.…”

Section: Introductionmentioning

confidence: 99%

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cDNA microarray image segmentation using root signals

Lukáč

Plataniotis

2006

Int. J. Imaging Syst. Technol.

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A vector processing based framework suitable for cDNA microarray image segmentation is introduced and analyzed in this paper. By using nonlinear, generalized selection vector filters the framework proposed here classifies the cDNA image data as either microarray spots or image background. The solution converges to a root signal that represents the segmented cDNA microarray image with the regular spots ideally separated from the background and with their coloration uniquely described by dominant color vectors. It will be demonstrated that the framework readily unifies image denoising, enhancement, data normalization, irregular spot rejection, and spot segmentation in one processing step delivering excellent performance at reasonable computational cost.

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“…Assuming that y n (r,s) is a vector in the image y n obtained after filtering n times the input cDNA image x, the convergence to a root signal can be expressed as a function of the difference between two successive filtering results [2], [17]:…”

Section: Root Signalsmentioning

confidence: 99%

Vector Median Root Signals Determination for cDNA Microarray Image Segmentation

Lukáč

Plataniotis

2005

Lecture Notes in Computer Science

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Abstract. This paper presents a new cDNA microarray image segmentation framework. The framework uses robust vector median filtering to generate a root sigLnal which is an image obtained from the input by repeatedly filtering it until no more changes occur. During the convergence to the root signal, the framework classifies the cDNA image data as either microarray spots or image background, and ideally separates the regular spots from the background. Thus, the obtained root signal represents the segmented microarray image. In addition, the framework excellently removes noise present in the cDNA microarray images and normalizes spots' intensities.

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Tuning the smoothness of the recursive median filter

Cited by 34 publications

References 20 publications

Face detection based on skin color modeling and modified Hausdorff distance

Face detection based on skin color modeling and modified Hausdorff distance

cDNA microarray image segmentation using root signals

Vector Median Root Signals Determination for cDNA Microarray Image Segmentation

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