Adaptive Filter Based on Image Region Characteristics for Optimal Edge Detection

2008 ◽  
Author(s):  
E.T.P. Lussiana ◽  
Yuhilza Hanum ◽  
Sarifuddin Madenda
Keyword(s):  
Edge Detection ◽  
Adaptive Filter ◽  
Image Region

scholarly journals Identifikasi Karakteristik Citra Berdasarkan pada Nilai Entropi dan Kontras

2021 ◽  
Vol 2 (1) ◽  
pp. 18-23
Author(s):  
Bheta Agus Wardijono ◽  
Lussiana ETP ◽  
Rozi
Keyword(s):  
Edge Detection ◽  
Good Condition ◽  
The Other ◽  
Detection Methods ◽  
Test Results ◽  
Image Size ◽  
Image Region ◽  
Image Characteristics ◽  
Different Characteristics ◽  
Region Size

Abstract Determining the object boundaries in an image is a necessary process, to identify the boundaries of an object with other objects as well as to define an object in the image. The acquired image is not always in good condition, on the other hand there is a lot of noise and blur. Various edge detection methods have been developed by providing noise parameters to reduce noise, and adding a blur parameter but because these parameters apply to the entire image, but lossing some edges due to these parameters. This study aims to identify the characteristics of the image region, whether the region condition is noise, blurry or otherwise sharp (clear). The step is done by dividing the four regions from the image size, then calculating the entropy value and contrast value of each formed region. The test results show that changes in region size can produce different characteristics, this is indicated by entropy and contrast values ​​of each formed region. Thus it can be concluded that entropy and contrast can be used as a way to identify image characteristics, and dividing the image into regions provides more detailed image characteristics.  

scholarly journals A review on the wavelet methods for sonar image segmentation

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142093609
Author(s):  
Yuanyuan Tian ◽  
Luyu Lan ◽  
Haitao Guo
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Edge Detection ◽  
Wavelet Packet ◽  
Clustering Algorithms ◽  
Support Vector ◽  
Underwater Robots ◽  
Image Region ◽  
Sonar Image ◽  
Segmentation Methods

The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.

B-Spline Wavelets Vision Detection Technology Based on Adaptive Filter

2012 ◽  
Vol 198-199 ◽  
pp. 284-287
Author(s):  
Ya Lin Ye ◽  
Ning Shan ◽  
Qian Zhang ◽  
Ke Li Yang
Keyword(s):  
Computer Vision ◽  
Edge Detection ◽  
Adaptive Filter ◽  
B Spline ◽  
Spline Wavelets ◽  
Detection Technology ◽  
Vision Detection

Edge is the most important information for computer vision. Wavelets edge detection can reduce noise disturbing, and also loses weak edging. This paper presents a new algorithm for edge detection. Based on sharping imaging edging by adaptive filter algorithm, the algorithm can detect edge by B-spline wavelets. This new algorithm has more higher precision than those normal algorithms.

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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