Skin Color Segmentation in YCBCR Color Space with Adaptive Fuzzy Neural Network (Anfis)

Iraji, Mohammad Saber; Tosinia, Azam

doi:10.5815/ijigsp.2012.04.05

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…Using RGB color range, it is tough to decide whether the color is human skin color or not. But, YCbCr color space [10] can be adopted to recognize human skin color areas accurately.…”

Section: Related Workmentioning

confidence: 99%

Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Ranganatha¹,

Gowramma²

2018

IJITCS

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Due to the lack of particular algorithms for automatic detection and tracking of person face(s), we have developed a new algorithm to achieve detection and single/multiple face tracking in different background video sequence. To detect faces, skin sections are segmented from the frame by means of YCbCr color model; and facial features are used to agree whether these sections contain person face or not. This procedure is challenging, because face color is unique and some objects may have similar color. Further, color and Eigen features are extracted from detected faces. Based on the points detected in facial region, point tracker tracks the user specified number of faces throughout the video sequence. The developed algorithm was tested on challenging dataset videos; and measured for performance using standard metrics. Test results obtained ensure the efficiency of proposed algorithm at the end.

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“…Using RGB color range, it is tough to decide whether the color is human skin color or not. But, YCbCr color space [10] can be adopted to recognize human skin color areas accurately.…”

Section: Related Workmentioning

confidence: 99%

Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Ranganatha¹,

Gowramma²

2018

IJITCS

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“…Those features are extracted using feature extraction techniques. The feature extraction techniques used here are color moment descriptor for color features, GLCM method [12] for texture features, geometry features. The selected features are given as the input to NN classifier.…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

“…The color features are mean, skewness, and standard derivation. These features extract using Color Moment (CM) descriptor proposed in [11,12]. The common moments are mean, standard deviation and skewness; the corresponding calculation can be defined as follows respectively:…”

Section: Color Featuresmentioning

confidence: 99%

Early Detection and Classification of Melanoma Skin Cancer

Alasadi¹,

Alsafy²

2015

IJITCS

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Melanoma is a form of cancer that begins in melanocytes (cells that make the pigment melanin). It can affect the skin only, or it may spread to the organs and bones. It is less common, but more serious and aggressive than other types of skin cancer. Melanoma can be of benign or malignant. Malignant melanoma is the dangerous condition, while benign is not. In order to reduce the death rate due to malignant melanoma skin cancer, it is necessary to diagnose it at an early stage. In this paper, a detection system has been designed for diagnosing melanoma in early stages by using digital image processing techniques. The system consists of two phases: the first phase detects whether the pigmented skin lesion is malignant or benign; the second phase recognizes malignant melanoma skin cancer types. Both first and second phases have several stages. The experimental results are acceptable.

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“…Modifying the weights for all neurons in the network, changes the output. Once the architecture of the network is defined, weights are calculated so as to represent the desired output through a learning process where the ANN is trained to obtain the expected results [36].…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

Infrared and Microwave Image Fusion for Rainfall Detection over Northern Algeria

Ouallouche¹,

Lazri²,

Ameur³

et al. 2014

IJIGSP

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Rain areas delineation proposed in this paper is based on the image fusion from geostationary Meteosat Second Generation (MSG) satellite, with the low-earth orbiting passive Tropical Rainfall Measuring Mission (TRMM) satellite. The fusion technique described in this work used an artificial neural network (ANN). It's has been developed to detect instantaneous rainfall by using information from the IR images of MSG satellite and from TRMM Microwave Imager (TMI). The study is carried out over north of Algeria. Seven spectral parameters are used as input data of ANN to identify raining or non-raining pixels. Corresponding data of raining /non-raining pixels are taken from a PR (precipitation radar) issued from TRMM. Results from the developed scheme are compared with the results of SI method (Scattering Index) taken as reference method. The results show that the developed model performs very well and overcomes the deficiencies of use a single satellite.

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Skin Color Segmentation in YCBCR Color Space with Adaptive Fuzzy Neural Network (Anfis)

Cited by 16 publications

References 13 publications

Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Early Detection and Classification of Melanoma Skin Cancer

Infrared and Microwave Image Fusion for Rainfall Detection over Northern Algeria

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