scholarly journals Automated selection of corresponding point candidates for image registration with artificial neuralnet using rotation invariant moments as the input data

2003 ◽  
Vol 42 (3) ◽  
pp. 17-28
Author(s):  
Hiroshi OKUMURA ◽  
Katsuyuki WATANABE ◽  
Masashi SUEZAKI ◽  
Koji KAJIWARA ◽  
Xi ZHANG ◽  
...  
Keyword(s):  
Image Registration ◽  
Input Data ◽  
Rotation Invariant ◽  
Invariant Moments ◽  
Selection Of

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

scholarly journals Sensitivity of Food-Based Recommendations Developed Using Linear Programming to Model Input Data in Young Kenyan Children

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3485
Author(s):  
Karin J. Borgonjen-van den Berg ◽  
Jeanne H. M. de Vries ◽  
Prosper Chopera ◽  
Edith J. M. Feskens ◽  
Inge D. Brouwer
Keyword(s):  
Linear Programming ◽  
Dietary Intake ◽  
Selection Criteria ◽  
Input Data ◽  
Reference Scenario ◽  
Food Groups ◽  
Nutrient Requirement ◽  
Model Input ◽  
Alternative Scenarios ◽  
Selection Of

Food-based recommendations (FBR) developed using linear programming generally use dietary intake and energy and nutrient requirement data. It is still unknown to what extent the availability and selection of these data affect the developed FBR and identified problem nutrients. We used 24 h dietary recalls of 62 Kenyan children (4–6 years of age) to analyse the sensitivity of the FBR and problem nutrients to (1) dietary intake data, (2) selection criteria applied to these data and (3) energy and nutrient requirement data, using linear programming (Optifood©), by comparing a reference scenario with eight alternative scenarios. Replacing reported by estimated consumption frequencies increased the recommended frequencies in the FBR for most food groups while folate was no longer identified as a problem nutrient. Using the 10–90th instead of the 5–95th percentile of distribution to define minimum and maximum frequencies/week decreased the recommended frequencies in the FBR and doubled the number of problem nutrients. Other alternative scenarios negligibly affected the FBR and identified problem nutrients. Our study shows the importance of consumption frequencies for developing FBR and identifying problem nutrients by linear programming. We recommend that reported consumption frequencies and the 5–95th percentiles of distribution of reported frequencies be used to define the minimum and maximum frequencies.

scholarly journals A New Image Registration Algorithm Based on Evidential Reasoning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1091 ◽  
Author(s):  
Zhe Zhang ◽  
Deqiang Han ◽  
Jean Dezert ◽  
Yi Yang
Keyword(s):  
Image Registration ◽  
Fundamental Problem ◽  
Similarity Measures ◽  
Detection Algorithms ◽  
Image Registration Algorithm ◽  
Theory Of Belief Functions ◽  
Pros And Cons ◽  
Registration Result ◽  
Different Levels ◽  
Selection Of

Image registration is a crucial and fundamental problem in image processing and computer vision, which aims to align two or more images of the same scene acquired from different views or at different times. In image registration, since different keypoints (e.g., corners) or similarity measures might lead to different registration results, the selection of keypoint detection algorithms or similarity measures would bring uncertainty. These different keypoint detectors or similarity measures have their own pros and cons and can be jointly used to expect a better registration result. In this paper, the uncertainty caused by the selection of keypoint detector or similarity measure is addressed using the theory of belief functions, and image information at different levels are jointly used to achieve a more accurate image registration. Experimental results and related analyses show that our proposed algorithm can achieve more precise image registration results compared to several prevailing algorithms.

scholarly journals miRID: Multi-Modal Image Registration Using Modality-Independent and Rotation-Invariant Descriptor

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2078
Author(s):  
Thuvanan Borvornvitchotikarn ◽  
Werasak Kurutach
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Similarity Measures ◽  
Fundamental Property ◽  
Transformation Process ◽  
Experimental Result ◽  
Absolute Difference ◽  
Rotation Invariant ◽  
The Mean ◽  
Image Registrations

Axiomatically, symmetry is a fundamental property of mathematical functions defining similarity measures, where similarity measures are important tools in many areas of computer science, including machine learning and image processing. In this paper, we investigate a new technique to measure the similarity between two images, a fixed image and a moving image, in multi-modal image registration (MIR). MIR in medical image processing is essential and useful in diagnosis and therapy guidance, but still a very challenging task due to the lack of robustness against the rotational variance in the image transformation process. Our investigation leads to a novel, local self-similarity descriptor, called the modality-independent and rotation-invariant descriptor (miRID). By relying on the mean of the intensity values, an miRID is simply computable and can effectively handle the complicated intensity relationship between multi-modal images. Moreover, it can also overcome the problem of rotational variance by sorting the numerical values, each of which is the absolute difference between each pixel’s intensity and the mean of all pixel intensities within a patch of the image. The experimental result shows that our method outperforms others in both multi-modal rigid and non-rigid image registrations.

scholarly journals Uncertainty and Sensitivity Analysis of Void Reactivity Feedback for 3D BWR Assembly Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Andrius Slavickas ◽  
Raimondas Pabarčius ◽  
Aurimas Tonkūnas ◽  
Eugenijus Ušpuras
Keyword(s):  
Sensitivity Analysis ◽  
Cross Section ◽  
Input Data ◽  
Statistical Approach ◽  
Assembly Model ◽  
Uncertainty And Sensitivity Analysis ◽  
Geometrical Dimensions ◽  
Basic Input ◽  
Propagation Of Uncertainties ◽  
Selection Of

Uncertainty and sensitivity analysis of void reactivity feedback for 3D BWR fuel assembly model is presented in this paper. Uncertainties in basic input data, such as the selection of different cross section library, manufacturing uncertainties in material compositions, and geometrical dimensions, as well as operating data are considered. An extensive modelling of different input data realizations associated with their uncertainties was performed during sensitivity analysis. The propagation of uncertainties was analyzed using the statistical approach. The results revealed that important information on the code predictions can be obtained by analyzing and comparing the codes estimations and their associated uncertainties.

scholarly journals Versatile Harmonic Tidal Analysis: Improvements and Applications

2009 ◽  
Vol 26 (4) ◽  
pp. 806-817 ◽  
Author(s):  
M. G. G. Foreman ◽  
J. Y. Cherniawsky ◽  
V. A. Ballantyne
Keyword(s):  
Time Series ◽  
Input Data ◽  
Computer Software ◽  
Temperature Time Series ◽  
Correlation Matrices ◽  
Ocean Sciences ◽  
Harmonic Tidal Analysis ◽  
Direct Incorporation ◽  
Temperature Time ◽  
Selection Of

Abstract New computer software that permits more versatility in the harmonic analysis of tidal time series is described and tested. Specific improvements to traditional methods include the analysis of randomly sampled and/or multiyear data; more accurate nodal correction, inference, and astronomical argument adjustments through direct incorporation in the least squares matrix; multiconstituent inferences from a single reference constituent; correlation matrices and error estimates that facilitate decisions on the selection of constituents for the analysis; and a single program that analyzes one- or two-dimensional time series. This new methodology is evaluated through comparisons with results from old techniques and then applied to two problems that could not have been accurately solved with older software. They are (i) the analysis of ocean station temperature time series spanning 25 yr, and (ii) the analysis of satellite altimetry from a ground track whose proximity to land has led to significant data dropout. This new software is free as part of the Institute of Ocean Sciences (IOS) Tidal Package and can be downloaded, along with sample input data and an explanatory readme file.

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