A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection

Acoustic seabed sediment classification method is always important research contents in marine geology and marine acoustics because of its characters of low-cost and high efficiency. At present, there are mainly three types of acoustic seabed sediment classification methods:(1) the echo signal statistical characteristics classification; (2) image texture classification; (3) submarine acoustic parameter inversion method. The principles of anterior two classification methods are similar, which is based on statistics, unknown sediment type can be concluded according to the statistical characteristics of known sediment. There are many usable acoustic equipments and commercial classification software for the two kinds of methods. The third type method is based on suitable seabed sediment model. Seabed acoustic characteristic parameters are inversed and thus seabed sediment can be classified. At present, there are few usable acoustic equipment and commercial classification software for the third method, but it's more accurate than the anterior two classification methods.

Download Full-text

Compressed Wavelet Tensor Attention Capsule Network

Security and Communication Networks ◽

10.1155/2021/9949204 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xiushan Liu ◽

Chun Shan ◽

Qin Zhang ◽

Jun Cheng ◽

Peng Xu

Keyword(s):

Neural Network ◽

Frequency Domain ◽

Wavelet Decomposition ◽

State Of The Art ◽

Texture Classification ◽

Texture Features ◽

Edge Computing ◽

Spectral Features ◽

Classification Methods ◽

Classification Tasks

Texture classification plays an important role for various computer vision tasks. Depending upon the powerful feature extraction capability, convolutional neural network (CNN)-based texture classification methods have attracted extensive attention. However, there still exist many challenges, such as the extraction of multilevel texture features and the exploration of multidirectional relationships. To address the problem, this paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet), which integrates multiscale wavelet decomposition, tensor attention blocks, and quantization techniques into the framework of capsule neural network. Specifically, the multilevel wavelet decomposition is in charge of extracting multiscale spectral features in frequency domain; in addition, the tensor attention blocks explore the multidimensional dependencies of convolutional feature channels, and the quantization techniques make the computational storage complexities be suitable for edge computing requirements. The proposed CWTACapsNet provides an efficient way to explore spatial domain features, frequency domain features, and their dependencies which are useful for most texture classification tasks. Furthermore, CWTACapsNet benefits from quantization techniques and is suitable for edge computing applications. Experimental results on several texture datasets show that the proposed CWTACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.

Download Full-text

Pit Pattern Classification Using Multichannel Features and Multiclassification

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications ◽

10.4018/978-1-60566-314-2.ch022 ◽

2009 ◽

pp. 335-350 ◽

Cited By ~ 7

Author(s):

Michael Haefner ◽

Alfred Gangl ◽

Michael Liedlgruber ◽

A. Uhl ◽

Andreas Vecsei ◽

...

Keyword(s):

Pattern Classification ◽

Texture Classification ◽

Wavelet Packet ◽

Structural Features ◽

Spatial Domain ◽

Classification Methods ◽

Statistical Features ◽

Pit Pattern ◽

Color Channel ◽

Pit Pattern Classification

Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images according to the pit pattern classification scheme. Regarding the wavelet-based methods, statistical features based on the wavelet coefficients as well as structural features based on the wavelet packet decomposition structures of the images have been used. In the case of the Fourier-based method, statistical features based on the Fourier-coefficients in ring filter domains are computed. In the spatial domain, histogram-based techniques are used. After reviewing the various methods employed we start by extracting the feature vectors for the methods from one color channel only. To enhance the classification results the methods are then extended to utilize multichannel features obtained from all three color channels of the respective color model used. Finally, these methods are combined into one multiclassifier to stabilize classification results across the image classes.

Download Full-text