scholarly journals Image Inpainting Based on Multi-Patch Match with Adaptive Size

2020 ◽  
Vol 10 (14) ◽  
pp. 4921 ◽  
Author(s):  
Shiyuan Yang ◽  
Haitao Liang ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
Xiaodong Chen
Keyword(s):  
Patch Size ◽  
Source Region ◽  
Image Inpainting ◽  
Experimental Results ◽  
Multiple Target ◽  
Fixed Size ◽  
Single Target ◽  
Low Efficiency

Patch-based image inpainting methods iteratively fill the missing region via searching the best sample patch from the source region. However, most of the existing approaches basically use the fixed size of patch regardless of content features nearby, which may lead to inpainting defects. Also, global match is needed for searching the best sample patch, but only to fill one target patch in each iteration, resulting in low efficiency. To handle the issues above, we first evaluate the nonuniformity in an image, by which the patch size is adaptively determined. Moreover, we divide the source region into multiple non-overlapping subregions with different nonuniformity levels, and the patch match proceeds in every subregion, respectively. This strategy not only saves the match time for single target patch, but also reduces the mismatch, and enables the simultaneous filling of multiple target patches in a single iteration. Experimental results show that in comparison to previous patch-based works, our method has achieved further improvement both in quality and efficiency. We believe our method could provide a new way for patch match with better accuracy and efficiency in image inpainting tasks.

Mining discriminative patches for script identification in natural scene images

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
The Other ◽  
Natural Scene ◽  
Fixed Size ◽  
Script Identification ◽  
Aspect Ratios ◽  
Novel Approach ◽  
Public Datasets ◽  
Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

Algorithm and Application of Springback Modification for Complex Bending Parts

2012 ◽  
Vol 538-541 ◽  
pp. 1031-1034
Author(s):  
Er Ming He ◽  
Hong Wei Zhang ◽  
Zhi Bin Zhao
Keyword(s):  
Torsion Angle ◽  
Experimental Results ◽  
Engineering Project ◽  
Practical Engineering ◽  
Open Api ◽  
Low Efficiency

To solve the problems of complex bending parts forming, such as low efficiency and accuracy, and loss of modification experience data, the algorithm of three parameters segment springback modification (TPSSM) based on arc approximation is presented in this paper. Firstly, the centroid line is divided into several arc sections according to the requirement and each section is modified based on presumptive correcting factor of radius, torsion and torsion angle. Then based on UG re-development, the program and UI of TPSSM are developed with UG/Open API and MFC. Finally, experimental results show that this method can effectively realize springback modification of bending parts, which is worthy for practical engineering project.

scholarly journals TSUNAMI GENERATION AND PROPAGATION

1972 ◽  
Vol 1 (13) ◽  
pp. 146
Author(s):  
Joseph L. Hammack ◽  
Frederic Raichlen
Keyword(s):  
Linear Theory ◽  
Source Region ◽  
Three Dimensional ◽  
Frequency Dispersion ◽  
Nonlinear Effects ◽  
Its Region ◽  
Experimental Results ◽  
Specific Class ◽  
Two Dimensional ◽  
Three Dimensional Models

A linear theory is presented for waves generated by an arbitrary bed deformation {in space and time) for a two-dimensional and a three -dimensional fluid domain of uniform depth. The resulting wave profile near the source is computed for both the two and three-dimensional models for a specific class of bed deformations; experimental results are presented for the two-dimensional model. The growth of nonlinear effects during wave propagation in an ocean of uniform depth and the corresponding limitations of the linear theory are investigated. A strategy is presented for determining wave behavior at large distances from the source where linear and nonlinear effects are of equal magnitude. The strategy is based on a matching technique which employs the linear theory in its region of applicability and an equation similar to that of Korteweg and deVries (KdV) in the region where nonlinearities are equal in magnitude to frequency dispersion. Comparison of the theoretical computations with the experimental results indicates that an equation of the KdV type is the proper model of wave behavior at large distances from the source region.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

Study on the Characteristics of the Automatic Tracking of Multiple Target Tracking

2014 ◽  
Vol 496-500 ◽  
pp. 1564-1567
Author(s):  
Jing Feng He ◽  
Ming Ji ◽  
Song Cheng ◽  
Ya Nan Wang
Keyword(s):  
Target Tracking ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Targets ◽  
Automatic Tracking ◽  
Single Target ◽  
Development Direction ◽  
The Future ◽  
Single Target Tracking

Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targets, and the development direction of the future.

scholarly journals Fractal Reflectarray Antennas: State of Art and New Opportunities

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
S. Costanzo ◽  
F. Venneri ◽  
G. Di Massa ◽  
A. Borgia ◽  
A. Costanzo ◽  
...  
Keyword(s):  
Patch Size ◽  
Comprehensive Analysis ◽  
Scaling Factor ◽  
Frequency Selective Surfaces ◽  
Fixed Size ◽  
Large Bandwidth ◽  
Unit Cells ◽  
Experimental Validations ◽  
State Of Art ◽  
Minkowski Fractal

Fractal geometries are appealing in all applications where miniaturization capabilities are required, ranging from antennas to frequency selective surfaces (FSS) design. Recently, some fractal patches configurations, giving low losses, reduced size, and quite good phase ranges, have been proposed for the design of reflectarray unit cells. This paper reviews existing fractal-based reflectarrays, highlighting their benefits and limitations. Furthermore, a comprehensive analysis of an innovative reflectarray unit cell, using a fractal-shaped fixed-size patch, is presented. The miniaturization capabilities of the Minkowski fractal shape are fully exploited to obtain a compact cell offering quite good phase agility, by leaving unchanged the patch size and acting only on the fractal scaling factor. Experimental validations are fully discussed on a realized 10 GHz0.3λ×0.3λcell. This is subsequently adopted to synthesize various reflectarray prototypes offering single or multiple-beam capabilities over a quite large angular region (up to 50 degrees). Finally, experimental validations on a realized15×15elements prototype are presented to demonstrate the wide angle beam-pointing capabilities as well as a quite large bandwidth of about 6%.

scholarly journals An image inpainting method for object removal based on difference degree constraint

2020 ◽  
Author(s):  
Lei Zhang ◽  
Minhui Chang
Keyword(s):  
Source Region ◽  
Image Inpainting ◽  
Mismatch Error ◽  
Matching Rule ◽  
Target Region ◽  
Object Removal ◽  
Degree Constraint ◽  
Mean Differences ◽  
Degree Of Similarity ◽  
Restoration Effect

Abstract In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.

Extended Probabilistic Data Association Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1600-1604
Author(s):  
Wan Li Xu ◽  
Zhun Liu ◽  
Jun Hui Liu
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Experimental Results ◽  
Effective Algorithm ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Event Based ◽  
Association Algorithm

[Purpos In order to improve the accuracy of target tracking and reduce losing rate of target in the multiple target tracking, a new algorithm called Extended Probabilistic Data Association (EPDA) is presented in this paper. [Metho This paper defines joint association event based on the number of target and puts forward the EPDA for target tracking. [Result Experimental results show that this algorithm has higher accuracy of target tracking than the Probabilistic Data Association algorithm and costs much less time relative to the Joint Probabilistic Data Association algorithm. [Conclusion Consequently, EPDA is an effective algorithm to balance the accuracy and the losing rate in target tracking.

Multiple-Target Selective Disassembly Sequence Planning With Disassembly Sequence Structure Graphs

2012 ◽  
Author(s):  
Shana Smith ◽  
Wei-Han Chen
Keyword(s):  
Model Complexity ◽  
Multiple Target ◽  
Sequence Structure ◽  
Specific Product ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Single Target ◽  
Disassembly Sequence ◽  
Graph Size ◽  
Selective Disassembly

Modern green products must be easy to disassemble. Selective disassembly is used to access and remove specific product components for reuse, recycling, or remanufacturing. Early related studies developed various heuristic or graph-based approaches for single-target selective disassembly. More recent research has progressed from single-target to multiple-target disassembly, but disassembly model complexity and multiple constraints, such as fastener constraints and disassembly directions, still have not been considered thoroughly. In this study, a new graph-based method using disassembly sequence structure graphs (DSSGs) was developed for multiple-target selective disassembly sequence planning. The DSSGs are built using expert rules, which eliminate unrealistic solutions and minimize graph size, which reduces searching time. Two or more DSSGs are combined into one DSSG for accessing and removing multiple target components. In addition, a genetic algorithm is used to decode graphical DSSG information into disassembly sequences and optimize the results. Using a GA to optimize results also reduces searching time and improves overall performance, with respect to finding global optimal solutions. Case studies show that the developed method can efficiently find realistic near-optimal multiple-target selective disassembly sequences for complex products.

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