Multiscale Blob Features for Gray Scale, Rotation and Spatial Scale Invariant Texture Classification

2006 ◽  
Author(s):  
Qi Xu ◽  
Yan Qiu Chen
Keyword(s):  
Spatial Scale ◽  
Texture Classification ◽  
Gray Scale ◽  
Scale Invariant

Logo Matching and Recognition Based on Context

2018 ◽  
pp. 164-176
Author(s):  
Tapan Kumar Das
Keyword(s):  
Real World ◽  
Scale Invariant Feature Transform ◽  
Gray Scale ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Key Points ◽  
Gray Scale Image ◽  
Feature Transform ◽  
Graphic Elements ◽  
Scale Invariant Feature

Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.

scholarly journals A Multiresolution Gray-Scale and Rotation Invariant Descriptor for Texture Classification

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 30691-30701 ◽  
Author(s):  
Qiqi Kou ◽  
Deqiang Cheng ◽  
Liangliang Chen ◽  
Kai Zhao
Keyword(s):  
Texture Classification ◽  
Gray Scale ◽  
Rotation Invariant

Texture Classification Based on Empirical Wavelet Transform Using LBP Features

2017 ◽  
Vol 8 (3) ◽  
pp. 623
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Automatic Inspection ◽  
Gray Scale ◽  
Novel Approach ◽  
Empirical Wavelet Transform ◽  
Inspection Systems

<p>Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.</p>

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