Rotation-invariant features based on directional coding for texture classification

Farida Ouslimani; Achour Ouslimani; Zohra Ameur

doi:10.1007/s00521-018-3462-9

Rotation-invariant features based on directional coding for texture classification

Neural Computing and Applications ◽

10.1007/s00521-018-3462-9 ◽

2018 ◽

Vol 31 (10) ◽

pp. 6393-6400 ◽

Cited By ~ 2

Author(s):

Farida Ouslimani ◽

Achour Ouslimani ◽

Zohra Ameur

Keyword(s):

Texture Classification ◽

Rotation Invariant ◽

Invariant Features ◽

Directional Coding

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Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2020.102931 ◽

2020 ◽

Vol 194 ◽

pp. 102931 ◽

Cited By ~ 1

Author(s):

Yasseen Almakady ◽

Sasan Mahmoodi ◽

Joy Conway ◽

Michael Bennett

Keyword(s):

Random Fields ◽

Markov Random Fields ◽

Texture Classification ◽

Three Dimensional ◽

Rotation Invariant ◽

Gaussian Markov Random Fields ◽

Invariant Features ◽

Markov Random ◽

Volumetric Texture

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Rotation invariant features for color texture classification and retrieval under varying illumination

Pattern Analysis and Applications ◽

10.1007/s10044-011-0207-0 ◽

2011 ◽

Vol 16 (1) ◽

pp. 69-81

Author(s):

B. Sathyabama ◽

M. Anitha ◽

S. Raju ◽

V. Abhaikumar

Keyword(s):

Texture Classification ◽

Rotation Invariant ◽

Invariant Features ◽

Color Texture

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Continuous rotation invariant features for gradient-based texture classification

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2014.10.004 ◽

2015 ◽

Vol 132 ◽

pp. 87-101 ◽

Cited By ~ 16

Author(s):

Kazim Hanbay ◽

Nuh Alpaslan ◽

Muhammed Fatih Talu ◽

Davut Hanbay ◽

Ali Karci ◽

...

Keyword(s):

Texture Classification ◽

Rotation Invariant ◽

Invariant Features ◽

Continuous Rotation ◽

Gradient Based

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Texture classification using Gabor wavelets based rotation invariant features

Pattern Recognition Letters ◽

10.1016/j.patrec.2006.05.008 ◽

2006 ◽

Vol 27 (16) ◽

pp. 1976-1982 ◽

Cited By ~ 158

Author(s):

S. Arivazhagan ◽

L. Ganesan ◽

S. Padam Priyal

Keyword(s):

Texture Classification ◽

Gabor Wavelets ◽

Rotation Invariant ◽

Invariant Features

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Noise robust rotation invariant features for texture classification

Pattern Recognition ◽

10.1016/j.patcog.2013.01.014 ◽

2013 ◽

Vol 46 (8) ◽

pp. 2103-2116 ◽

Cited By ~ 37

Author(s):

Rouzbeh Maani ◽

Sanjay Kalra ◽

Yee-Hong Yang

Keyword(s):

Texture Classification ◽

Rotation Invariant ◽

Invariant Features ◽

Noise Robust

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Development of Sorting System for Fishes by Feed-forward Neural Networks Using Rotation Invariant Features

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.404 ◽

2011 ◽

Vol 131 (2) ◽

pp. 404-410 ◽

Cited By ~ 1

Author(s):

Yuhki Shiraishi ◽

Fumiaki Takeda

Keyword(s):

Neural Networks ◽

Rotation Invariant ◽

Feed Forward ◽

Invariant Features ◽

Feed Forward Neural Networks ◽

Sorting System

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SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2016.02.005 ◽

2016 ◽

Vol 52 ◽

pp. 96-107 ◽

Cited By ~ 10

Author(s):

Bae-Muu Chang ◽

Hung-Hsu Tsai ◽

Chih-Yuan Yen

Keyword(s):

Texture Classification ◽

Image Texture ◽

Rotation Invariant

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Rotation-invariant texture classification using a two-stage wavelet packet feature approach

IEE Proceedings - Vision Image and Signal Processing ◽

10.1049/ip-vis:20010705 ◽

2001 ◽

Vol 148 (6) ◽

pp. 422 ◽

Cited By ~ 8

Author(s):

C.-M. Pun ◽

M.-C. Lee

Keyword(s):

Texture Classification ◽

Wavelet Packet ◽

Rotation Invariant ◽

Two Stage

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Rotation invariant texture classification using multichannel filtering

10.1117/12.441651 ◽

2001 ◽

Cited By ~ 1

Author(s):

Ramchandra Manthalkar ◽

Kumar P. Biswas

Keyword(s):

Texture Classification ◽

Rotation Invariant ◽

Multichannel Filtering

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Analysis of the Repeatability of SIFT and SURF Descriptors Techniques for Underwater Image Preprocessing

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-v4-i3-004 ◽

2021 ◽

pp. 24-31

Author(s):

Shubhangi N. Ghate ◽

Dr. Mangesh Nikose

Keyword(s):

Prior Knowledge ◽

State Of The Art ◽

Rotation Invariant ◽

Image Preprocessing ◽

Retrieval Time ◽

Retrieval Accuracy ◽

Invariant Features ◽

Underwater Image ◽

Geometrical Constraints ◽

Time Required

To improve the repeatability of SIFT and SURF descriptors, we conducted research to find two methods: first, a method for pre-processing underwater images that does not require prior knowledge of the scene, and second, a method for computing distances that is less expensive in terms of execution time for finding corresponding points. SIFTs (Scale and Rotation Invariant Features) are new features that have been developed. SIFTs (Scale and Rotation Invariant Features) are newly developed features that are based on geometrical constraints between pairs of nearby points around a key point. SIFT is contrasted with cutting-edge local features. SIFT outperforms the state-of-the-art in terms of retrieval time and retrieval accuracy. We have discussed the time required to extract key point features of SIFT and SURF Descriptor.

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