RESEARCH ON SEPARATION OF ARTIFICIAL AND NATURAL IMAGES

人工图像与自然图像分离的研究

• 批准号: 06555049
• 负责人: FUKUDA Shuichi
• 金额: $ 4.48万
• 依托单位: TOKYO METROPOLITAN INSTITUTE OF TECHNOLOGY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (A)
• 财政年份: 1994
• 资助国家: 日本
• 起止时间: 1994 至 1996
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-06555049/
• 关键词: Image Separation; Natural Images and Artificial Images; Fractal Dimension; Integer Dimension and Non-integer Dimension; Binary Images; Color Images; ASIC; イメージプロセッシング; 画像弁別; 人工物画像; 自然物画像; プリプロセッシング

If we can separate artificial and natural images, a robot can easily navigate even if the scene may change from season to season because of the falling leaves, etc. , since those images which change from season to season are natural ones and those which do not are artificial ones. And we can easily detect cracks in a very complex structure without any prior knowledge because cracks are natural in nature.In this work, we noticed that natural images have non-integer Fractal dimensions and artificial ones have integer dimensions and developed the methodology and conceptual design for ASIC.What we have done and achieved in this project are(1) We calculate Fractal dimension using the box counting method.(2) The following two methods are developed for binary images.[Static separation method]Divide the target image into meshes. The Fractal dimension is calculated segment by segment, which is a group of meshes. These local segments are grouped together by the value of Fractal dimension.[Dynamic separation method]The Fractal dimension is calculated moving dx in the x direction and dy in the y direction sequentially. By the size of the local segment and the size of dx and dy, overlap sometimes occurs. If such overlap occurs, Fractal dimension is calculated in terms of pixels and the average value of these valuse will be used.(3) We extended the above method to color images.We calculate the histogram for HSL (Hue, Saturation and Lightness) of the color image.Then it is divided by peak based on the histogram. The same procedure as the binary image is applied for each color segments.(4) We developed conceptual design for ASICThe above methodology can also be applied to separation problems between artificial and natural sounds.

如果我们可以将人工图像和自然图像分开，机器人可以很容易地导航，即使由于落叶等原因导致场景随季节而变化，因为那些随季节变化的图像是自然图像，而不是自然图像的是人工图像。在这项工作中，我们注意到自然图像具有非整数分形维，而人工图像具有整数维，并提出了ASIC的方法和概念设计。本课题所做的工作和取得的成果包括：(1)使用盒法计算分维。(2)针对二值图像，提出了以下两种方法。分形维是一段一段地计算的，这是一组网格。这些局部段通过分维的值组合在一起。[动态分离法]计算分维的方法是在x方向上移动dx，在y方向上依次移动dy。根据局部段的大小以及dx和dy的大小，有时会发生重叠。将上述方法推广到彩色图像，计算彩色图像HSL(色调、饱和度和亮度)的直方图，然后根据直方图划分峰值。对每个颜色段采用与二值图像相同的方法。(4)提出了ASIC的概念设计方法，该方法也适用于人工声音和自然声音的分离问题。

项目成果

福田収一: "フラクタルを用いた音の識別" 日本機械学会第5回設計工学・システム部門講演会講演論文集. 95-24. 181 (1995)

Shuichi Fukuda：“使用分形进行声音识别”日本机械工程师学会第五届设计工程和系统分会会议记录 95-24 (1995)。

Shuichi Fukuda: "Image Separation Between Natural and Artificial Objects" First Asian Control Conference. 2 of 3. 761-764 (1994)

Shuichi Fukuda：“自然与人造物体之间的图像分离”第一届亚洲控制会议。

Shuichi Fukuda: ""Image Separation between Natural and Artificial Objects"" First Asian Control Conference. Vol. 2. 761-764 (1994)

Shuichi Fukuda：““自然与人造物体之间的图像分离””第一届亚洲控制会议。

福田収一: "フラクタルを利用した人工物と自然画像の識別" 画像ラボ. 5・4. 44-47 (1994)

福田修一：“使用分形识别人造物体和自然图像”图像实验室 5・4（1994）。

Shuichi Fukuda: "Image Separation Between Natural and Artificial Objects Using Fractal Dimension" MVA'94 IAPR Workshop on Machine Vision Applications. 368-370 (1994)

Shuichi Fukuda：“使用分形维数在自然和人造物体之间进行图像分离”MVA94 IAPR 机器视觉应用研讨会。