Selecting appropriate difference operators for digital images by local feature detection

1997 ◽  
Vol 6 (4) ◽  
pp. 415
Author(s):  
Peter Veelaert
Keyword(s):  
Feature Detection ◽  
Digital Images ◽  
Local Feature ◽  
Difference Operators

scholarly journals α-shapes for local feature detection

2016 ◽  
Vol 50 ◽  
pp. 56-73 ◽  
Author(s):  
Christos Varytimidis ◽  
Konstantinos Rapantzikos ◽  
Yannis Avrithis ◽  
Stefanos Kollias
Keyword(s):  
Feature Detection ◽  
Local Feature

Local entropy edge detection in digital images

1988 ◽  
Vol 46 ◽  
pp. 840-841
Author(s):  
David C. Martin
Keyword(s):  
Edge Detection ◽  
Feature Recognition ◽  
Digital Images ◽  
Original Data ◽  
Rate Of Change ◽  
Difference Operators ◽  
Conditional Probabilities ◽  
Local Entropy ◽  
Definition Of ◽  
Detection Schemes

Detection of edges in digital images is an important task for feature recognition and interpretation. Typically, edge detection schemes involve difference operators such as the gradient or Laplacian which depend on the rate of change of brightness in an image. Recently, Shiozaki has shown that a local entropy operator can be useful for edge detection in digital images. This method is simple, rapid, and the resulting image can be interpreted as a measure of the local “information” content of the original data. Here, we investigate the applicability of this approach to digital electron micrographs.The definition of the entropy for a probability distribution pi is S=-Σ pi in Pi. For an image, the conditional probabilities pi are defined as the fraction of total flux which is in a given pixel. If the intensity of the image at a pixel i is fi, then pi=fi/Σfi. The entropy has a maximum when all the Pi's are equal, corresponding to the case of least configurational information.

scholarly journals An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Luan Xidao ◽  
Xie Yuxiang ◽  
Zhang Lili ◽  
Zhang Xin ◽  
Li Chen ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Feature Detection ◽  
Feature Vector ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Local Feature ◽  
Image Similarity ◽  
Sift Feature ◽  
Similarity Detection ◽  
Overcomplete Dictionary

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.

scholarly journals Dithering-based Sampling and Weighted α-shapes for Local Feature Detection

2015 ◽  
Vol 7 (0) ◽  
pp. 189-200 ◽  
Author(s):  
Christos Varytimidis ◽  
Konstantinos Rapantzikos ◽  
Yannis Avrithis ◽  
Stefanos Kollias
Keyword(s):  
Feature Detection ◽  
Local Feature
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