Quantitative Evaluation of Gray Level Difference and Successive Abandonment Methods Using Artificial Visualized Images.

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

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Performance Inspection of Gray-Level Difference Method Using Standard Images

Journal of the Visualization Society of Japan ◽

10.3154/jvs.18.supplement1_75 ◽

1998 ◽

Vol 18 (Supplement1) ◽

pp. 75-78

Author(s):

Akikazu KAGA ◽

In-Seop Lee ◽

Yoshio Inoue ◽

Katsuhito Yamaguchi ◽

Oh-Sung Kwon

Keyword(s):

Difference Method ◽

Gray Level ◽

Level Difference ◽

Performance Inspection

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The Combination of Local Fuzzy Entropy and Gray Level Difference-Based Thresholding Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.1288 ◽

2012 ◽

Vol 220-223 ◽

pp. 1288-1291

Author(s):

Tong Tong ◽

Yan Cai ◽

Da Wei Sun ◽

Wei Huang

Keyword(s):

Transition Region ◽

Extraction Methods ◽

Fuzzy Entropy ◽

Experimental Results ◽

Segmentation Result ◽

Gray Level ◽

Thresholding Method ◽

Level Difference ◽

Region Extraction

A novel transition region extraction and thresholding method based on both frequency and degree of gray level changes is proposed by analyzing properties of transition region. Frequent gray level based transition region extraction methods are greatly affected by noise. To eliminate the algorithm limitation, a modified descriptor taking both degree and frequency of gray level changes into account is developed. The proposed algorithm can accurately extract transition region of an image and get ideal segmentation result. The experimental results show its superiority and feasibility.

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Statistical Image Analysis of Psychological Projective Drawings

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2005.p0453 ◽

2005 ◽

Vol 9 (5) ◽

pp. 453-460 ◽

Cited By ~ 5

Author(s):

Kazuhisa Takemura ◽

◽

Iyuki Takasaki ◽

Yumi Iwamitsu ◽

Keyword(s):

Image Analysis ◽

Difference Method ◽

Gray Level ◽

Projective Drawings ◽

Standard Analysis ◽

Projective Drawing ◽

Statistical Image Analysis ◽

Level Difference ◽

Gray Level Histogram

We propose statistical image analysis for psychological projective drawings to facilitate assessing the reliability of the projective drawing making the determination of its validity difficult. Standard analysis involves (1) drawing a picture, (2) scanning the drawing, (3) dividing the drawing, (4) analyzing the gray level histogram moment (GLHM), (5) applying spatial gray level dependence method (SGLDM), (6) applying the gray level difference method (GLDM) for the drawing, and (7) interpreting the drawing. To demonstrate the proposed procedure, we used the tree test (Baum test). Three adults were presented with blank A4 paper and asked to draw a picture of a tree with fruit on it. Drawings were analyzed by statistical image analysis and results interpreted clinically.

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