Multi-direction Fuzzy Morphology Algorithm for Image Edge Detection

Wang, Hainan

doi:10.4304/jnw.6.6.895-898

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…(5) The proposed method utilize a radial basis function to construct a kernel matrix by computing the distance of two different vectors, which are computed by the parameter of 2-norm exponential. (6) In the proposed scheme, the sliding median filter is utilized to deal with signals which could contaminate with outliers, and the relationship one dimension sliding median filter between input and output in this window of the sliding median filter is also considered.…”

Section: Experimental Results Analyzingmentioning

confidence: 99%

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Human Face Recognition based on Improved PCA Algorithm

Xu¹,

Li²

2013

JMM

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This paper aims to effectively recognize human faces from images, which is an important problem in the multimedia information process. After analyzing the related research works, the framework of the face recognition system is illustrated as first, which contains the training process and the testing process. Particularly, the improved PCA algorithm is use in the feature extraction module. The main innovations of this paper lie in that, in the improved PCA, we utilize a radial basis function to construct a kernel matrix by computing the distance of two different vectors, which are calculated by the parameter of 2-norm exponential. Afterwards, human faces can be recognized by computing the distance of test image and the training images by the nearest neighbor classifier, of which the cosine distance is utilized. Finally, experiments are conducted to make performance evaluation. Compared with the existing face recognition methods, the proposed scheme is more effective in recognizing human faces with high efficiency

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Section: Experimental Results Analyzingmentioning

confidence: 99%

“…Face detection and feature extraction can be achieved simultaneously. Depending on the nature of the application, for example, the sizes of the training and testing databases, clutter and variability of the background, noise, occlusion, and speed requirements, some of the sub-tasks can be very challenging [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Human Face Recognition based on Improved PCA Algorithm

Xu¹,

Li²

2013

JMM

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“…Morphology is based on the association theory in mathematics [9] [15]. In general, morphological method can be used as an image segmentation method.…”

Section: Basic Morphological Methodsmentioning

confidence: 99%

“…In the digital image, collection of pixel which is similar (geometric feature with the same shape and size) is known as "foreground" (pixel structure has value one (1)) [8] and collection of pixel which is the complement is known as background (pixel structure has value 0) [8]. According to Basic Morphological or Morphology, there are some basic arithmetic which can be used for image segmentation [9] [16]. …”

Section: Basic Morphological Methodsmentioning

confidence: 99%

Hybrid Method and Similarity to Recognize Javanese Keris

Santoso¹,

Hadjon²

2015

ijacsa

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Abstract-This paper describes Hybrid method and Similarity for recoginizing Javanese Keris. Javanese Keris is one of traditional javanese weapon. It is one of the Indonesia Cultural Heritage. Keris is famous for its distinctive wavy blade. But some of the keris has straight blades. There are many kinds of Keris and every Keris has its own unique pattern. The algorithm to recognize several types of Javanese Keris is made by using Edge Detection method with Image Segmentation and combine with Similarity. By using these three methods, it will bring more exact results.The method combines Canny Algorithm and Basic Morphological for image segmentation. The result of edge detection and image segmentation are compared with the sample pictures using similarity. Ten (10) images of traditional javanese keris are being used as samples. The final result for this study is recognizing the kind of keris. These techniques is carried out with an experiment by using MATLAB 6.0.1 software.

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