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FWLBP: A Scale Invariant Descriptor for Texture Classification
In this paper we propose a novel texture recognition feature called Frac...
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Radial Line Fourier Descriptor for Segmentation-free Handwritten Word Spotting
Automatic recognition of historical handwritten manuscripts is a dauntin...
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Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network
This paper presents a novel approach to exploit the distinctive invarian...
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Performance Evaluation of SIFT Descriptor against Common Image Deformations on Iban Plaited Mat Motifs
Borneo indigenous communities are blessed with rich craft heritage. One ...
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HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning
Local feature extraction remains an active research area due to the adva...
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Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images
Fast and robust image matching is a very important task with various app...
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Application of Threshold Techniques for Readability Improvement of Jawi Historical Manuscript Images
Historical documents such as old books and manuscripts have a high aesth...
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A proposition of a robust system for historical document images indexation
Characterizing noisy or ancient documents is a challenging problem up to now. Many techniques have been done in order to effectuate feature extraction and image indexation for such documents. Global approaches are in general less robust and exact than local approaches. That's why, we propose in this paper, a hybrid system based on global approach(fractal dimension), and a local one based on SIFT descriptor. The Scale Invariant Feature Transform seems to do well with our application since it's rotation invariant and relatively robust to changing illumination.In the first step the calculation of fractal dimension is applied to images in order to eliminate images which have distant features than image request characteristics. Next, the SIFT is applied to show which images match well the request. However the average matching time using the hybrid approach is better than "fractal dimension" and "SIFT descriptor" if they are used alone.
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