A New Similarity Measure for Random Signatures: Perceptually Modified Hausdorff Distance

2006 ◽  
pp. 990-1001 ◽  
Author(s):  
Bo Gun Park ◽  
Kyoung Mu Lee ◽  
Sang Uk Lee
Keyword(s):  
Similarity Measure ◽  
Hausdorff Distance ◽  
Modified Hausdorff Distance

scholarly journals Hausdorff distance-based multiresolution maps applied to image similarity measure

2007 ◽  
Vol 55 (3) ◽  
pp. 164-174 ◽  
Author(s):  
E Baudrier ◽  
G Millon ◽  
F Nicolier ◽  
R Seulin ◽  
S Ruan
Keyword(s):  
Similarity Measure ◽  
Hausdorff Distance ◽  
Image Similarity

Binary Image Recognition Utilizing Computer Generated Templates

2017 ◽  
Author(s):  
Dustin Bielecki ◽  
Prakhar Jaiswal ◽  
Rahul Rai
Keyword(s):  
Image Recognition ◽  
Hausdorff Distance ◽  
Binary Image ◽  
Sorting Technique ◽  
Milling Operation ◽  
Modified Hausdorff Distance ◽  
Image Templates

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.

Similarity Measure for Image Retrieval Based on Hausdorff Distance

2014 ◽  
Vol 635-637 ◽  
pp. 1039-1044 ◽  
Author(s):  
He Qun Qiang ◽  
Chun Hua Qian ◽  
Sheng Rong Gong
Keyword(s):  
Image Retrieval ◽  
Pattern Matching ◽  
Similarity Measure ◽  
Hausdorff Measure ◽  
Hausdorff Distance ◽  
Experimental Result ◽  
Feature Vectors ◽  
Image Query ◽  
Feature Values ◽  
Object Similarity

In general, it is difficult to segment accurately image regions or boundary contours and represent them by feature vectors for shape-based image query. Therefore, the object similarity is often computed by their boundaries. Hausdorff distance is nonlinear for computing distance, it can be used to measure the similarity between two patterns of points of edge images. Classical Hausdorff measure need to express image as a feature matrix firstly, then calculate feature values or feature vectors, so it is time-consuming. Otherwise, it is difficult for part pattern matching when shadow and noise existed. In this paper, an algorithm that use Hausdorff distance on the image boundaries to measure similarity is proposed. Experimental result has showed that the proposed algorithm is robust.

scholarly journals Fully automatic segmentation of bee wing images

2020 ◽  
Vol 12 (2) ◽  
pp. 37-45
Author(s):  
João Marcos Garcia Fagundes ◽  
Allan Rodrigues Rebelo ◽  
Luciano Antonio Digiampietri ◽  
Helton Hideraldo Bíscaro
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Feature Extraction ◽  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Food Crops ◽  
Artificial Intelligence Techniques ◽  
Fully Automatic ◽  
Modified Hausdorff Distance ◽  
F Measure

Bee preservation is important because approximately 70% of all pollination of food crops is made by them and this service costs more than $ 65 billion annually. In order to help this preservation, the identification of the bee species is necessary, and since this is a costly and time-consuming process, techniques that automate and facilitate this identification become relevant. Images of bees' wings in conjunction with computer vision and artificial intelligence techniques can be used to automate this process. This paper presents an approach to do segmentation of bees' wing images and feature extraction. Our approach was evaluated using the modified Hausdorff distance and F measure. The results were, at least, 24% more precise than the related approaches and the proposed approach was able to deal with noisy images.

Corrigendum to “A modified Hausdorff distance between fuzzy sets”

2002 ◽  
Vol 148 (1-4) ◽  
pp. 233-234 ◽  
Author(s):  
Kiran R. Bhutani ◽  
B.B. Chaudhuri ◽  
Azriel Rosenfeld
Keyword(s):  
Fuzzy Sets ◽  
Hausdorff Distance ◽  
Modified Hausdorff Distance
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