Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

Al-Mohair, Hani K.; Mohamad–Saleh, Junita; Suandi, Shahrel Azmin

doi:10.1016/j.asoc.2015.04.046

Cited by 78 publications

(21 citation statements)

References 12 publications

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“…The average center of a cluster is recalculated each time a new object joins that cluster. 11 In the present paper, we chose the k samples that resulted in the greatest average distances between clusters as the initial cluster centers. This fixed initial cluster centers method is able to provide global convergence and to overcome local optima.…”

Section: Methodsmentioning

confidence: 99%

Three-Dimensional Measurement and Cluster Analysis for Determining the Size Ranges of Chinese Temporomandibular Joint Replacement Prosthesis

Zhang

Meng

et al. 2016

Medicine

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The aim of this study was to investigate the osseous characteristics of Chinese temporomandibular joint (TMJ) and detect the size clusters for total joint prostheses design.Computer tomography (CT) data from 448 Chinese adults (226 male and 222 female, aged from 20 to 83 years, mean age 39.3 years) with 896 normal TMJs were chosen from the Department of Radiology in the Shanghai 9th People's Hospital. Proplan CMF 1.4 software was used to reconstruct the skulls. Three-dimensional (3D) measurements of the TMJ fossa and condyle-ramus units with 13 parameters were performed. Size clusters for prostheses design were determined by hierarchical cluster analyses, nonhierarchical (K-means) cluster analysis, and discriminant analysis.The glenoid fossa was grouped into 3 clusters, and the condyle-ramus units were grouped into 4 clusters. Discriminant analyses were capable of correctly classifying 97.24% of the glenoid fossa and 94.98% of the condyle-ramus units. The means and standard deviations for the parameter values in each cluster were determined.Fossa depth and angles between the condyle and ramus were important parameters for Chinese TMJ prostheses design. 3D measurements and cluster analysis of the osseous morphology of the TMJ provided an anatomical reference and identified the dimensions of the minimum numbers of prosthesis sizes required for Chinese TMJ replacement.

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Section: Methodsmentioning

confidence: 99%

Three-Dimensional Measurement and Cluster Analysis for Determining the Size Ranges of Chinese Temporomandibular Joint Replacement Prosthesis

Zhang

Meng

et al. 2016

Medicine

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“…Next, the elliptical human skin color distribution model defined through pre-learning, as shown in Equation (2), is applied to robustly extract skin color distribution pixels except for other background regions from an input color image [25][26][27].…”

Section: Detection Of Skin Color Distribution Regionsmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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“…Kmeans terimi ilk defa James MacQueen tarafından 1967'de kullanılmıştır [22,23]. K-means basitliği sebebi ile veri madenciliği başta olmak üzere çok çeşitrli alanlarda uygulamaya sahiptir [24]. K-means kümelemede amaç kümeler arası uzaklığı maksimum, küme içi uzaklığı ise minimum yapmaktır.…”

Section: K-means Kümelemeunclassified

The classification of breast cancer with Machine Learning Techniques

Kolay

Erdoğmuş

2016

2016 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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ÖzetçeBu çalışmada UCI(University of California-Irvine) Machine Learning Laboratuvarından alınan göğüs kanseri dataları kullanılarak, makine öğrenmesi teknikleri ile göğüs kanserinin sınıflandırılmasına çalışılmıştır. Bu amaçla, göğüs kanseri dataları kullanılarak, K-means kümeleme uzaklık ölçümlerinin kümeleme başarısı Matlab©'da analiz edilmiştir. Daha sonra, hiçbir ön işleme yapmadan, Weka© Veri Madenciliği yazılımı kullanılarak, UCI'den alınan göğüs kanseri dataları, çeşitli makine öğrenme teknikleri ile WEKA'da sınıflandırılmıştır. Sonuç olarak kümeleme uzaklık ölçütlerinin başarıyı yaklaşık olarak %12 değiştirdiği ve sınıflandırma başarısının yöntemlere göre %45-%79 arasında değiştiği görülmüştür. AbstractIn this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab© has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software©. As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from %45 to %79, according to the methods.

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Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

Cited by 78 publications

References 12 publications

Three-Dimensional Measurement and Cluster Analysis for Determining the Size Ranges of Chinese Temporomandibular Joint Replacement Prosthesis

Three-Dimensional Measurement and Cluster Analysis for Determining the Size Ranges of Chinese Temporomandibular Joint Replacement Prosthesis

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

The classification of breast cancer with Machine Learning Techniques

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