Train wheel edge detection and image object region segmentation

2016 ◽  
Author(s):  
Guo Nan ◽  
Shengfang Lu ◽  
Junen Yao
Keyword(s):  
Edge Detection ◽  
Region Segmentation ◽  
Object Region ◽  
Image Object

scholarly journals RANCANG BANGUN APLIKASI DETEKSI TEPI CITRA DIGITAL MENGGUNAKAN ALGORITMA PREWITT

2021 ◽  
Vol 4 (1) ◽  
pp. 83-87
Author(s):  
Ardiansyah Putra ◽  
◽  
Volvo Sihombing ◽  
Mustafha Haris Munandar ◽  
◽  
...  
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Human Vision ◽  
Processing Technology ◽  
Image Information ◽  
Human Eye ◽  
Image Object ◽  
Boundary Lines

Edge detection is one of the algorithms used in Digital Image Processing. This algorithm serves to identify the line/edge of the image object to highlight the boundary lines of the image information. Edge is a set of connected pixels (connected pixels) that restricts the objects contained in the image. Senses the eye is one that is used by humans to see.However, the human eye has its limitations in capturing the electromagnetic signals.Therefore, created a computer or imaging machine that can capture almost the entire signal elektromagnetic. Imaging machines can work with imagery from sources that do not fit, do not fit or can not be captured by human vision. This is why digital image processing have very wide usefulness. Image processing technology can fit into a variety of fields such as medicine, geology, marine , industrial and others.Keywords:Image,Prewitt,Edge,Java,Netbeans

Quadrilateral Extraction Method and its Application on Parking Cell Detection

2012 ◽  
Vol 263-266 ◽  
pp. 2534-2537
Author(s):  
Mei Wang ◽  
Zuo Peng Wang ◽  
Yong Ling Chu
Keyword(s):  
Extraction Method ◽  
Detection Accuracy ◽  
Cell Detection ◽  
Segmentation Method ◽  
Region Segmentation ◽  
Straight Line ◽  
Actual Color ◽  
Object Area ◽  
Image Object ◽  
Template Extraction

This paper presents a quadrilate extraction method based on four vertices of the image object to solve the problrm of object area inaccuracy depending on conventional rectangular segmentation method. Firstly, four vertices are given by the mouse-click depending on quadilateral object spatial feature; and then the quadrilateral object shape feature is estimated by each side of the quadriangle computing by straight-line formulation of two adjacent vertices of object in order to acquare the quadrialeratal object inernal pixel. At last, the pixels of quadrialeratel external region are cleaned. Arbitrary quadrilateral region segmentation is achieved accurately. The method can be used for any quadrilateral region segmentation based on image processing, The experiments on actual color parking images show any parking cell template extraction accuracy reached 100% and parking cell state detection accuracy is 99.8%. The result verifies the algorithm effectiveness and robustnes

Difference mode EXELFS with parallel-detection EELS

1992 ◽  
Vol 50 (2) ◽  
pp. 1262-1263
Author(s):  
Michael K. Kundmann ◽  
Ondrej L. Krivanek
Keyword(s):  
Edge Detection ◽  
Important Distinction ◽  
Edge Structure ◽  
Parallel Detection ◽  
Gain Variation ◽  
Ideal Solutions ◽  
Detector Cell ◽  
The Difference ◽  
Elemental Detection ◽  
Partial Correction

Parallel detection has greatly improved the elemental detection sensitivities attainable with EELS. An important element of this advance has been the development of differencing techniques which circumvent limitations imposed by the channel-to-channel gain variation of parallel detectors. The gain variation problem is particularly severe for detection of the subtle post-threshold structure comprising the EXELFS signal. Although correction techniques such as gain averaging or normalization can yield useful EXELFS signals, these are not ideal solutions. The former is a partial throwback to serial detection and the latter can only achieve partial correction because of detector cell inhomogeneities. We consider here the feasibility of using the difference method to efficiently and accurately measure the EXELFS signal.An important distinction between the edge-detection and EXELFS cases lies in the energy-space periodicities which comprise the two signals. Edge detection involves the near-edge structure and its well-defined, shortperiod (5-10 eV) oscillations. On the other hand, EXELFS has continuously changing long-period oscillations (∼10-100 eV).

A Dividing Ratio Changeable Digital PLL Based on Phase State Memory and Double Clock-Edge Detection

2008 ◽  
Vol 128 (7) ◽  
pp. 1185-1190 ◽  
Author(s):  
Kuniaki Fujimoto ◽  
Hirofumi Sasaki ◽  
Mitsutoshi Yahara
Keyword(s):  
Edge Detection ◽  
Phase State
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