Multiscale texture classification using dual-tree complex wavelet transform

Çelik, Turgay; Tjahjadi, Tardi

doi:10.1016/j.patrec.2008.10.006

Cited by 67 publications

(71 citation statements)

References 23 publications

(26 reference statements)

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“…However, the parallel wavelet filter bank trees are designed to calculate thee coefficient of the complex wavelet. In order to use decomposition coefficients of the trees, filters within a tree may give delays [31,32].…”

Section: Related Workmentioning

confidence: 99%

Oil exploration oriented multi-sensor image fusion algorithm

2017

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Abstract:In order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT) in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multisensor image fusion, especially for images of oil exploration.

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Section: Related Workmentioning

confidence: 99%

Oil exploration oriented multi-sensor image fusion algorithm

2017

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“…D. Experiment IV In this experiment, MIT VIS-Tex database is used and the proposed method is compared with DT-CWT based classification of ref. [3] at level 2. Twelve images containing similar textural patterns from the MIT VIS-Tex database are used.…”

Section: B Experiments IImentioning

confidence: 99%

“…The Table IV shows the comparison between the proposed method and the DT-CWT technique of ref. [3]. E. Experiment V In this experiment 69 textures of size 512 × 512 from Meastex database are used and the proposed method is compared with the DLBP approach for texture classification of ref.…”

Section: B Experiments IImentioning

confidence: 99%

Texture Classification Using Cosine-modulated Wavelets

Mushrif¹,

Dubey²

2012

IJCEE

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Abstract-This paper proposes a technique for image texture classification based on cosine-modulated wavelet transform. Better discriminability and low implementation cost of the cosine-modulated wavelets has been effectively utilized to yield better features and more accurate classification results. Experimental results demonstrate the effectiveness of this approach on different datasets in three experiments. The proposed approach improves classification rates compared to the traditional Gabor wavelet based approach, rotated wavelet filters based approach, DT-CWT approach and the DLBP approach. The computational cost of the proposed method is less as compared to the other two methods.

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“…Unlike in [13] where the variance and entropy are defined in terms of only the magnitude of the DT CWT subbands, in this paper we define a variance M 1 (r) and an entropy M 2 (r) that incorporate both the magnitude and phase responses as the features for r, r ∈ {α…”

Section: Feature Extractionmentioning

confidence: 99%

“…In order to perform a supervised texture classification using DT CWT, the following learning stage for the texture classifier based on that in [13] but modified to include phase information and the newly defined feature vectors for each texture class t: (ii) Generate feature vector F…”

Section: Texture Learning and Classificationmentioning

confidence: 99%

Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands

Çelik

Tjahjadi

2011

Computers & Electrical Engineering

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This paper proposes a multiscale texture classifier which uses features extracted from both magnitude and phase responses of subbands at different resolutions of the dual-tree complex wavelet transform decomposition of a texture image. The mean and entropy in the transform domain are used to form a feature vector. The proposed method can achieve a high texture classification rate even for small number of samples used in training stage. This makes it suitable for applications where the number of texture samples used in training is very limited. The superior performance and robustness of the proposed classifier is shown for classifying and retrieving texture images from image databases.

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Multiscale texture classification using dual-tree complex wavelet transform

Cited by 67 publications

References 23 publications

Oil exploration oriented multi-sensor image fusion algorithm

Oil exploration oriented multi-sensor image fusion algorithm

Texture Classification Using Cosine-modulated Wavelets

Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands

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