A parameterized statistical sonar image texture model

2008 ◽  
Author(s):  
J. Tory Cobb ◽  
K. Clint Slatton
Keyword(s):  
Image Texture ◽  
Sonar Image ◽  
Texture Model

Land Cover Classification Using the Proposed Texture Model and Fuzzy k-NN Classifier

2018 ◽  
pp. 226-261
Author(s):  
Jenicka S.
Keyword(s):  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Texture Feature ◽  
Image Texture ◽  
Kappa Statistics ◽  
Grey Level ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Texture Model ◽  
Proposed Model

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of current pixel but from the grey level intensity variations of current pixel with its neighbors. In this chapter, a new texture model called multivariate binary threshold pattern (MBTP) has been proposed with five discrete levels such as -9, -1, 0, 1, and 9 characterizing the grey level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. Texture-based classification has been performed with the proposed model using fuzzy k-nearest neighbor (fuzzy k-NN) algorithm on IRS-P6, LISS-IV data, and the results have been evaluated based on confusion matrix, classification accuracy, and Kappa statistics. From the experiments, it is found that the proposed model outperforms other chosen existing texture models.

Texture model regression for effective feature discrimination: Application to seismic facies visualization and interpretation

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 958-967 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
High Performance ◽  
Fresnel Zone ◽  
Maximum Amplitude ◽  
Seismic Facies ◽  
Image Texture ◽  
Computational Time ◽  
Gas Field ◽  
Reflection Seismic ◽  
Hydrocarbon Reservoir ◽  
Texture Model

The classical approach to feature discrimination requires extraction and classification of multiple attributes. Such an approach is expensive in terms of computational time and storage space, and the results are generally difficult to interpret. With increasing data size and dimensionality, along with demand for high performance and productivity, the effectiveness of a feature‐discrimination methodology has become a critically important issue in many areas of science. To address such an issue, I developed a texture model regression (TMR) methodology. Unlike classical attribute extraction and classification algorithms, the TMR methodology uses an interpreter‐defined texture model as a calibrating filter and regresses the model texture with the data texture at each sample location to create a regression‐gradient volume. The new approach not only dramatically reduces computational cycle time and space but also creates betters results than those obtained from classical techniques, resulting in improved feature discrimination, visualization, and interpretation. Application of the TMR concept to reflection seismic data demonstrates its value in seismic‐facies analysis. In order to characterize reflection seismic images composed of wiggle traces with variable amplitude, frequency, and phase, I introduced two simple seismic‐texture models in this application. The first model is defined by a full cycle of a cosine function whose amplitude and frequency are the maximum amplitude and dominant frequency of wiggle traces in the interval of interest. The second model is defined by a specific reflection pattern known to be associated with a geologic feature of interest, such as gas sand in a hydrocarbon reservoir. I applied both models to a submarine turbidite system offshore West Africa and to a gas field in the deep‐water Gulf of Mexico, respectively. Based on extensive experimentation and comparative analysis, I found that the TMR process with such simple texture models creates superior results, using minimal computational resources. The result is geologically intriguing, easily interpretable, and consistent with general depositional and reservoir‐facies concepts. Such a successful application may be attributable to the sensitivity of image texture to physical texture in the Fresnel zone at an acoustic interface and therefore to lithology, depositional facies, and hydrocarbonsaturation.

scholarly journals On the Information Advantage of Sidescan Sonar Three-Frequency Colour over Greyscale Imagery

2019 ◽  
Vol 7 (8) ◽  
pp. 276
Author(s):  
Duncan Tamsett ◽  
Jason McIlvenny ◽  
James Baxter ◽  
Paulo Gois ◽  
Benjamin Williamson
Keyword(s):  
Image Texture ◽  
Single Frequency ◽  
Sidescan Sonar ◽  
Prototype System ◽  
Black And White ◽  
Sonar Image ◽  
Colour Image ◽  
Underwater Mapping ◽  
Greyscale Image ◽  
Information Advantage

A prototype three-frequency (114, 256, and 410 kHz) colour sidescan sonar system, built by Kongsberg Underwater Mapping Ltd. (Great Yarmouth, UK), was previously described, and preliminary results presented, in Tamsett, McIlvenny, and Watts. The prototype system has subsequently been modified, and in 2017, new data were acquired in a resurvey of the Inner Sound of the Pentland Firth, North Scotland. An image texture characterisation and image classification exercise demonstrates considerably greater discrimination between different seabed classes in a three-frequency colour sonar image of the seabed, than in a multi-frequency colour image reduced to greyscale display, or in a single-frequency greyscale image, with readily twice the number of classes of seabed discriminated between, in the colour image. The information advantage of colour acoustic imagery over greyscale acoustic imagery is analogous to the information advantage of colour television images over black-and-white television images. A three-frequency colour sonar image contains a theoretical maximum of a factor of 3 times the information in a corresponding greyscale image, for independent seabed responses at the three frequencies. Estimates of the average information per pixel (information entropy) in the colour image, and in corresponding greyscale images, reveal an actual information advantage of colour sonar imagery over greyscale, to be in practice approximately a factor of 2.5, empirically confirming the greater information based utility of three-frequency colour sonar over greyscale sonar. Reference: Tamsett, D.; McIlvenny, J.; Watts, A. J. Mar. Sci. Eng. 2016, 4(26).

TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

2011 ◽  
Vol 25 (05) ◽  
pp. 741-762 ◽  
Author(s):  
E. M. SRINIVASAN ◽  
K. RAMAR ◽  
A. SURULIANDI
Keyword(s):  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Vital Role ◽  
Image Texture ◽  
Feature Extraction Method ◽  
Local Texture ◽  
Texture Model ◽  
Fuzzy Logic Approach

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

scholarly journals Image texture model based on energy features

2021 ◽  
Vol 1902 (1) ◽  
pp. 012120
Author(s):  
M M Lyasheva ◽  
S A Lyasheva ◽  
M P Shleymovich
Keyword(s):  
Image Texture ◽  
Model Based ◽  
Texture Model

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Sonar image denoising method based on NSCT circle sample

2009 ◽  
Vol 29 (1) ◽  
pp. 68-70
Author(s):  
Chun-rui TANG ◽  
Dan-dan LIU
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Sonar Image

scholarly journals Realistic Sonar Image Simulation Using Generative Adversarial Network

2019 ◽  
Vol 52 (21) ◽  
pp. 291-296 ◽  
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Juhwan Kim ◽  
Son-Cheol Yu
Keyword(s):  
Image Simulation ◽  
Generative Adversarial Network ◽  
Sonar Image ◽  
Adversarial Network

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

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