EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function
We propose a two-part local image descriptor EL (Edges and Lines), based on the strongest image responses to the first- and second-order partial derivatives of the two-dimensional Gaussian function. Using the steering theorems, the proposed method finds the filter orientations giving the strongest i...
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2019-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2019/1247925 |
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doaj-8d94d5bdd4494bc0b25518c2b72555eb2020-11-24T22:07:22ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/12479251247925EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian FunctionJasna Maver0Danijel Skočaj1Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, SloveniaFaculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, SloveniaWe propose a two-part local image descriptor EL (Edges and Lines), based on the strongest image responses to the first- and second-order partial derivatives of the two-dimensional Gaussian function. Using the steering theorems, the proposed method finds the filter orientations giving the strongest image responses. The orientations are quantized, and the magnitudes of the image responses are histogrammed. Iterative adaptive thresholding of histogram values is then applied to normalize the histogram, thereby making the descriptor robust to nonlinear illumination changes. The two-part descriptor is empirically evaluated on the HPatches benchmark for three different tasks, namely, patch verification, image matching, and patch retrieval. The proposed EL descriptor outperforms the traditional descriptors such as SIFT and RootSIFT on all three evaluation tasks and the deep-learning-based descriptors DeepCompare, DeepDesc, and TFeat on the tasks of image matching and patch retrieval.http://dx.doi.org/10.1155/2019/1247925 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jasna Maver Danijel Skočaj |
spellingShingle |
Jasna Maver Danijel Skočaj EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function Mathematical Problems in Engineering |
author_facet |
Jasna Maver Danijel Skočaj |
author_sort |
Jasna Maver |
title |
EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function |
title_short |
EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function |
title_full |
EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function |
title_fullStr |
EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function |
title_full_unstemmed |
EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function |
title_sort |
el: local image descriptor based on extreme responses to partial derivatives of 2d gaussian function |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2019-01-01 |
description |
We propose a two-part local image descriptor EL (Edges and Lines), based on the strongest image responses to the first- and second-order partial derivatives of the two-dimensional Gaussian function. Using the steering theorems, the proposed method finds the filter orientations giving the strongest image responses. The orientations are quantized, and the magnitudes of the image responses are histogrammed. Iterative adaptive thresholding of histogram values is then applied to normalize the histogram, thereby making the descriptor robust to nonlinear illumination changes. The two-part descriptor is empirically evaluated on the HPatches benchmark for three different tasks, namely, patch verification, image matching, and patch retrieval. The proposed EL descriptor outperforms the traditional descriptors such as SIFT and RootSIFT on all three evaluation tasks and the deep-learning-based descriptors DeepCompare, DeepDesc, and TFeat on the tasks of image matching and patch retrieval. |
url |
http://dx.doi.org/10.1155/2019/1247925 |
work_keys_str_mv |
AT jasnamaver ellocalimagedescriptorbasedonextremeresponsestopartialderivativesof2dgaussianfunction AT danijelskocaj ellocalimagedescriptorbasedonextremeresponsestopartialderivativesof2dgaussianfunction |
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1725820774133006336 |