scholarly journals Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Pengcheng Han ◽  
Junping Du
Keyword(s):  
A Priori ◽  
Contourlet Transform ◽  
Translation Invariant ◽  
Nonlocal Means ◽  
Directional Filter ◽  
Pyramid Structure ◽  
Image Edge ◽  
Edge Content ◽  
Image Details ◽  
Different Levels

Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimensional and translation-invariant image decomposition for spatial images. A nonsubsampled contourlet transform is used as the basis for an improved Bayesian nonlocal means (NLM) filter for different frequencies. The Bayesian model adds a sigma range in imagea priorioperations, which can be more effective in protecting image details. The NLM filter retains the image edge content and assigns greater weight to similarities for edge pixels. Experimental results both on standard images and spatial images confirm that the proposed algorithm yields significantly better performance than nonsubsampled wavelet transform, contourlet, and curvelet approaches.

The Visual Quality Recognition of Nonwovens Using a Novel Wavelet Based Contourlet Transform

2011 ◽  
Vol 267 ◽  
pp. 884-889
Author(s):  
Jian Li Liu ◽  
Bao Qi Zuo
Keyword(s):  
Texture Analysis ◽  
Comparative Method ◽  
Visual Quality ◽  
Contourlet Transform ◽  
Laplacian Pyramid ◽  
Directional Filter ◽  
Wavelet Texture Analysis ◽  
The One ◽  
Texture Extraction ◽  
Different Levels

In this paper, a novel wavelet based contourlet transform for texture extraction is presented. In the texture analysis section, we propose a novel wavelet based contourlet transform, which can be considered as a simplified but more sufficient for texture analysis for nonwoven image compared with version of the one introduced by Eslami in theory view. In experiment, nonwoven images of five different visual quality grades, 125 of each grade, are decomposed using wavelet based contourlet transform with ‘PKVA’ filter as the default filter of Laplacian Pyramid (LP) and Directional Filter Bank (DFB) at 3 levels and two energy-based features, norm-1 L1 and norm-2 L2, are calculated from the wavelet coefficients at the first level and contourlet coefficients of each high frequency subband at different levels and directions to train and test SVM. When the nonwoven images are decomposed at 3 levels and 16 L1s are extracted, with 500 samples to train the SVM, the average recognition accuracy of test set is 98.4%, which is superior to the comparative method using wavelet texture analysis.

scholarly journals Image Super Resolution Reconstruction Based on Improved Invertible Rescaling Net

2021 ◽  
Author(s):  
Taiping Mo ◽  
Dehong Chen
Keyword(s):  
Feature Extraction ◽  
High Frequency ◽  
Interpolation Method ◽  
Super Resolution ◽  
Frequency Information ◽  
Ill Posed ◽  
Image Edge ◽  
Image Super Resolution ◽  
Improved Model ◽  
Image Details

Abstract The Invertible Rescaling Net (IRN) is modeling image downscaling and upscaling as a unified task to alleviate the ill-posed problem in the super-resolution task. However, the ability of potential variables of the model embedded high-frequency information is general, which affects the performance of the reconstructed image. In order to improve the ability of embedding high-frequency information and further reduce the complexity of the model, the potential variables and feature extraction of key components of IRN are improved. Attention mechanism and dilated convolution are used to improve the feature extraction block, reduce the parameters of feature extraction block, and allocate more attention to the image details. The high frequency sub-band interpolation method of wavelet domain is used to improve the potential variables, process and save the image edge, and enhance the ability of embedding high frequency information. Experimental results show that compared with IRN model, improved model has less complexity and excellent performance.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

Improving Predictive Power of Conjoint Analysis by Constrained Parameter Estimation

1983 ◽  
Vol 20 (4) ◽  
pp. 433-438 ◽  
Author(s):  
V. Srinivasan ◽  
Arun K. Jain ◽  
Naresh K. Malhotra
Keyword(s):  
Parameter Estimation ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
Predictive Power ◽  
A Priori ◽  
A Priori Knowledge ◽  
Constrained Estimation ◽  
First Choice ◽  
Full Profile ◽  
Different Levels

The prediction of first choice preferences by the full-profile method of conjoint analysis can be improved significantly by imposing constraints on parameters based on a priori knowledge of the ordering of part worths for different levels of an attribute. Constrained estimation however, has little effect on the predictive validity of the tradeoff method because the preference judgments within rows (or columns) of tradeoff tables have largely the same role as the constraints.

Human–Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior Intelligence

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Matteo Lavit Nicora ◽  
Roberto Ambrosetti ◽  
Gloria J. Wiens ◽  
Irene Fassi
Keyword(s):  
Situation Awareness ◽  
Production Efficiency ◽  
A Priori ◽  
Open Loop ◽  
Human Robot Interaction ◽  
Smart Manufacturing ◽  
Seamless Integration ◽  
Robot Controller ◽  
Human Robot Collaboration ◽  
Different Levels

Abstract To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot’s decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human–robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI’s underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human–robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application.

scholarly journals Fossil crocodilian distributions, Upper Cretaceous to present: implications for paleoclimate

1992 ◽  
Vol 6 ◽  
pp. 198-198
Author(s):  
Paul Markwick
Keyword(s):  
Late Cretaceous ◽  
Middle Eocene ◽  
Global Climate ◽  
A Priori ◽  
Tropical Regions ◽  
Cooling Trend ◽  
Geologic Record ◽  
Continental Interior ◽  
Different Levels ◽  
Limited Scope

The present day distribution of crocodilians appears to be climatically controlled, at least in part, with the group restricted to tropical through sub-tropical regions. Studies have shown that although crocodiles may be able to withstand sub-zero temperatures they can do so for only limited periods. By analogy the presence of fossil crocodilians in the geologic record has been interpretated as indicating warmth. However previous studies have generally been of limited scope. This study uses global paleodistributions of the crocodilians to map gross global climate for the last 100 million years.A comprehensive database of published occurrences of fossil crocodilians from the late Cretaceous to the Present has been constructed. Taphonomic and collection biases have been addressed using ‘control groups', these are respectively the Testudines and the vertebrates in general. Problems of taxonomic inconsistency have been dealt with by ‘accepting’ a standard published taxonomic scheme (Carroll, 1988). Geographic and temporal uncertainties and imprecisions are coded on the database to facilitate sorting; this allows the analyses to be run at different levels of precision and provides an opportunity to understand the way biogeographic and hence paleoclimatic interpretations may be influenced by both the nature of the geologic record itself and by a priori decisions made by the worker. The database also includes lithologic, stratigraphic and environmental information on some 3300 localities and includes specimen information for the taxa entered (>14000 separate entries assembled from 1000 references).Preliminary analyses of paleolatitudinally reconstructed localities reveals the following trends: an overall equatorward movement of the poleward limit of the crocodiles from the late Cretaceous to the present; this is punctuated by an abrupt equatorward excursion of almost 10° during the Oligocene and another of similar magnitude at the end of the Miocene, with an apparent Miocene ‘recovery’ in between (this trend is shown most clearly by the families Alligatoridae and Crocodylidae). At the suborder level the Mesosuchians (excluding the Sebecidae) show a distinct equatorial shift from the Campanian through to the middle Eocene when they disappear; inclusion of the Sebecidae in the Mesosuchia gives rise to a sudden poleward expansion in the middle Eocene of some 20° paleolatitude. Map reconstructions, especially for North America, reveal an eastward shift of crocodilian localities as the Tertiary progresses, perhaps due in part to a taphonomic artifact, viz., the migration of the locus of sedimentation. With the late Miocene the crocodilians disappeared completely from the continental interior record, a transition which seems tied to increased aridity (as indicated by the development of caliches in many areas) and increased seasonality of temperature. This pattern is also seen in the southern ‘U.S.S.R’.The distributions of the Crocodylia through time therefore reflect and support established views concerning late Cretaceous through Tertiary climate with a general cooling trend from the late Cretaceous to the present punctuated by abrupt coolings in the Oligocene and around the Miocene-Pliocene boundary.

Medical Ultrasound Image Denoising Algorithm Based on Nosubsampled Contourlet Transform

2014 ◽  
Vol 644-650 ◽  
pp. 3999-4004
Author(s):  
Min Fen Shen ◽  
Fei Huang ◽  
Zhi Fei Su ◽  
Li Sha Sun
Keyword(s):  
Clinical Medicine ◽  
Ultrasound Image ◽  
Contourlet Transform ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Wavelet Thresholding ◽  
Scale Analysis ◽  
Multi Scale ◽  
Image Edge

Currently,the ultrasound image has been widely used in diagnosis and treatment of clinical medicine,the results obtained by the diagnostic accuracy and reliability of the image is directly related to the effects of diagnosis and treatment.Because ultrasound images in the imaging process inevitably contaminated noise,thus the research of inhibiting ultrasound image noise is one of the important issues in domestic and international ultrasound imaging techniques.This paper studies the multi-scale analysis and wavelet thresholding two theories,put forwarda denoising algorithm about combining the Nonsubsampling contourlet transform and a new threshold function,experiments show that the new algorithm can not only good at suppressing the noise of ultrasound images,and can better retain image edge and texture details.

Hybrid modelling of yeast production processes - combination of a priori knowledge on different levels of sophistication

1994 ◽  
Vol 17 (1) ◽  
pp. 10-20 ◽  
Author(s):  
Jörg Schubert ◽  
Rimvydas Simutis ◽  
Michael Dors ◽  
Ivo Havlík ◽  
Andreas Lübbert
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Hybrid Modelling ◽  
Production Processes ◽  
Yeast Production ◽  
Different Levels ◽  
Priori Knowledge

scholarly journals An Intelligent cluster-based segmentation using Wavelet Features:Application to Medical Images

2010 ◽  
Author(s):  
Thavavel V ◽  
JafferBasha J
Keyword(s):  
Medical Images ◽  
Automatic Segmentation ◽  
A Priori ◽  
User Interaction ◽  
Region Of Interest ◽  
Feature Space ◽  
Discrete Wavelet ◽  
Wavelet Frames ◽  
Translation Invariant ◽  
Spherical Coordinate

Segmentation forms the onset for image analysis especially for medical images, making any abnormalities in tissues distinctly visible. Possible application includes the detection of tumor boundary in SPECT, MRI or electron MRI (EMRI). Nevertheless, tumors being heterogeneous pose a great problem when automatic segmentation is attempted to accurately detect the region of interest (ROI). Consequently, it is a challenging task to design an automatic segmentation algorithm without the incorporation of ‘a priori’ knowledge of an organ being imaged. To meet this challenge, here we propose an intelligence-based approach integrating evolutionary k-means algorithm within multi-resolution framework for feature segmentation with higher accuracy and lower user interaction cost. The approach provides several advantages. First, spherical coordinate transform (SCT) is applied on original RGB data for the identification of variegated coloring as well as for significant computational overhead reduction. Second the translation invariant property of the discrete wavelet frames (DWF) is exploited to define the features, color and texture using chromaticity of LL band and luminance of LH and HL band respectively. Finally, the genetic algorithm based K-means (GKA), which has the ability to learn intelligently the distribution of different tissue types without any prior knowledge, is adopted to cluster the feature space with optimized cluster centers. Experimental results of proposed algorithm using multi-modality images such as MRI, SPECT, and EMRI are presented and analyzed in terms of error measures to verify its effectiveness and feasibility for medical applications.

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