EXEMPLAR-BASED INPAINTING: TECHNICAL REVIEW AND NEW HEURISTICS FOR BETTER GEOMETRIC RECONSTRUCTIONS

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Engineering

EXEMPLAR-BASED INPAINTING: TECHNICAL REVIEW AND NEW HEURISTICS
FOR BETTER GEOMETRIC RECONSTRUCTIONS
ABSTRACT
This paper proposes a technical review of exemplarbased inpainting approaches with a
particular focus on greedy methods. Several comparative and illustrative experiments are
provided to deeply explore and enlighten these methods, and to have a better understanding on
the state-of-the-art improvements of these approaches. From this analysis, three improvements
over Criminisi et al. algorithm are then presented and detailed: 1) a tensor-based data term for a
better selection of pixel candidates to fill in; 2) a fast patch lookup strategy to ensure a better
global coherence of the reconstruction; and 3) a novel fast anisotropic spatial blending algorithm
that reduces typical block artifacts using tensor models. Relevant comparisons with the state-of-
the-art inpainting methods are provided that exhibit the effectiveness of our contributions

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This document summarizes a paper on finite element simulation of wave propagation in nonlinear soils. The paper presents a finite element solution that uses an enriched Ramberg-Osgood constitutive model consistent with extended Masing behavior to account for hysteretic behavior of soils under cyclic loading conditions. It applies absorbing boundary conditions and calculates effective nodal forces to model wave propagation to infinite domains. Comparisons are made between implicit time integration schemes. Applications ranging from 1D to 3D wave propagation simulations using simple wavelets and seismic records are demonstrated. Best practices for successful simulation are summarized.

1. The document proposes a semantic octree framework that unifies recognition, reconstruction, and representation of 3D scenes. 2. It combines an associative higher-order random fields model for semantic segmentation with an octree representation used in reconstruction to label voxels with semantic classes. 3. Inference is performed over the octree structure to semantically label the reconstructed 3D scene in a computationally efficient manner by exploiting the hierarchical grouping of space provided by the octree.

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This document describes two GIS transit planning tools created by Carly S. Queen for her advanced GIS course: a Transit Score model and a Transit Propensity model. The Transit Score model calculates densities for population, employment, and poverty data, normalizes the data, applies weights, and calculates a transit score on a scale of 0-100. The Transit Propensity model uses coefficients for various density layers in a negative binomial regression to estimate transit usage, which is then compared to actual usage data to evaluate the model. The tools were developed to help with transit planning efforts by estimating area-level and stop-level transit demand.

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This document discusses various GIS analysis techniques including buffering, overlay, and distance measurement. Buffering creates zones around features, which can be used for planning purposes like restricting certain land uses near schools. Overlay combines data layers by joining features that occupy the same space. Feature overlay splits features where they overlap, while raster overlay combines characteristics across layers. Overlay is useful for queries and modeling. Distance measurement finds straight-line distances between features for analysis and pattern detection.

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The document discusses two numerical integration methods for approximating the area under a curve: 1) The trapezoidal rule approximates the area as a sum of trapezoidal areas under strips of equal width. It is applied to an example where it estimates the area as 625 square meters. 2) Simpson's rule assumes the boundary is composed of parabolic arcs rather than straight lines, making it more accurate. It is applied to the same example and estimates the area as 584 square meters.

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This document discusses automating the administrative boundary design process using hierarchical spatial reasoning theory and GIS. It outlines how hierarchical spatial reasoning can be applied to administrative polygons design in GIS by developing algorithms to transform flat boundary structures into hierarchical ones according to specific rules and constraints. The expected advantages of this approach include enhanced data integration, modifiable area units, boundary flexibility over time. The document concludes that it is possible to align administrative boundaries according to hierarchical spatial reasoning theory and the implemented algorithm offers a solution to automated boundary delineation.

Can graph convolution network learn spatial relations ?

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This document discusses using graph convolutional networks to learn spatial relations from geographic data represented as graphs. It presents experiments on (1) detecting alignments between linear geographic features and (2) selecting roads from a generalized network that should be kept or erased at a target map scale. For alignment detection, proximity criteria were used to modify the graph and convolutional networks with attribute features predicted alignments with 72% accuracy. For road selection, features like length and importance were used and the method classified 72% of sections correctly with 73% precision and 83% recall. The document concludes graph convolutional networks show promise for learning spatial relations from graph-represented geographic data.

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Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.

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This document describes a new recursive Monte Carlo simulation algorithm called the Sampled Path Set Algorithm (SPSA) for modeling complex k-out-of-n reliability systems. The SPSA uses a graph representation of a reliability block diagram and recursively searches the graph to determine system response based on the system state vector at each simulation iteration, allowing modeling of systems with general component failure and repair distributions and large numbers of components. Existing methods for analyzing such systems using tie/cut sets have limitations as the number of sets grows non-linearly with increased system complexity. The SPSA provides a more efficient alternative with linear growth in processing and memory requirements.

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Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Nagar, Chennai-83. Landmark: Next to Kotak Mahendra Bank/Bharath Scans Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry - 9 Landmark: Opp. to Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690.

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Cukoo srch

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The document proposes a particle swarm inspired cuckoo search algorithm to improve the balance of exploration and exploitation in the standard cuckoo search algorithm. It adds neighborhood information from the global best solutions to increase diversity and uses two new search strategies with a random probability rule to balance exploration and exploitation. The algorithm is tested on 30 benchmark functions and two real-world problems, and shows better performance than the standard cuckoo search, particle swarm optimization, and other state-of-the-art algorithms.

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This document presents a new method for creating proxy models called Simulated Annealing Programming (SAP). SAP uses simulated annealing optimization on a tree structure to find the equation that best predicts outputs with minimum error. The authors apply SAP to create a model for predicting gas compressor torque based on fuel rate and speed. They find the SAP model is highly accurate, roughly independent of internal parameters, and has smaller overestimation/underestimation compared to other methods like polynomials and neural networks.

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This chapter provides a literature review on past research related to hyperloops and other relevant topics. It discusses two papers that introduced using genetic algorithms to optimize pipe sizing. Simpson et al. (1994) presented applying a genetic algorithm to pipe network optimization and found it effective for finding near-optimal solutions. Savic and Walters (1997) described a computer model called GANET that uses genetic algorithms for least-cost water distribution network design, which produced good solutions. Genetic algorithms are an easy and efficient method for optimizing large distribution networks.

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This document proposes using a novel dynamic fuzzy c-means (dFCM) clustering technique combined with an artificial neural network (ANN) approach to reconfigure power distribution networks and minimize active power loss. The dFCM clustering is used to preprocess the input data for the ANN by transforming the input space into kernels, reducing the size of the ANN needed. The proposed framework combines dFCM clustering and ANN and is tested on two power networks, showing benefits like very short processing time, high accuracy, and a simple ANN structure with few neurons compared to other traditional reconfiguration methods.

Kentaro_region_filling_inpainting

Vipin Gupta

This paper proposes a new algorithm for removing large objects from digital images by filling in the removed region in a visually plausible manner. It combines exemplar-based texture synthesis with structure propagation from image inpainting. The key insights are that exemplar-based texture synthesis can replicate both texture and structure if the filling order is determined by propagating confidence in pixel values similar to inpainting, and that a single algorithm can synthesize both pure and composite textures. The proposed algorithm efficiently synthesizes textures and propagates structures into the removed region by sampling exemplar patches based on a confidence term calculated from local image gradients. This allows it to generate natural-looking results while avoiding blurring issues of previous approaches.

A Geometry Projection Method For Shape Optimization

Joaquin Hamad

The document presents a new geometry projection method for shape optimization that combines the advantages of direct geometry representations and fictitious domain analysis methods. An analytical geometry model defines the design domain, while a projection onto a fictitious domain enables simplified response analysis and sensitivity calculations. The geometry projection converges to the analytical geometry model as the numerical mesh is refined, ensuring optimal designs converge to solutions of well-defined continuum problems. Example computations demonstrate the method for minimum compliance with a volume constraint and minimum volume with a stress constraint.

Compressive stregnth of concerte using image processing ANM & ML

SujitMundhe

The document summarizes a student's seminar review on using image processing and machine learning to predict the compressive strength of concrete. The student analyzed various research papers on the topic and identified different methods used, including using greyscale values of images, machine learning algorithms like AdaBoost and Random Forest, and artificial neural networks. The student conducted their own experiments on concrete specimens to collect image and compressive strength data to train models and found that AdaBoost and Random Forest achieved high accuracy. The document outlines the objectives, methodology, findings and provides details on image processing technology and how machine learning can be applied to predict compressive concrete strength.

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This document discusses dimensionality reduction techniques for reducing high-dimensional chemical data to lower dimensions while preserving structural properties. Four techniques are evaluated: principal components analysis, kernel principal components analysis, diffusion maps, and neighbourhood components analysis. Samples from a chemical database are generated containing different molecular property groups for validation. Techniques are evaluated visually by checking if sample structures are preserved in lower dimensions, and quantitatively by measuring local and global distance-based error rates between high and low dimensional representations. The results indicate which techniques reliably preserve properties with low generalization error.

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IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.

Adaptive traffic lights based on traffic flow prediction using machine learni...

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