scholarly journals Image-Guided Voronoi Aesthetic Patterns with an Uncertainty Algorithm Based on Cloud Model

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Tao Wu ◽  
Limin Zhang
Keyword(s):  
Time Complexity ◽  
High Efficiency ◽  
Cloud Model ◽  
Input Image ◽  
Pattern Generation ◽  
Reference Image ◽  
Clothing Design ◽  
Image Guided ◽  
Digital Entertainment ◽  
Cracked Soil

Tessellation-based art is an important technique for computer aided aesthetic patterns generation, and Voronoi diagram plays a key role in the preprocessing, whose uncertainty mechanism is still a challenge. However, the existing techniques handle the uncertainty incompletely and unevenly, and the corresponding algorithms are not of high efficiency; thus it is impossible for users to obtain the results in real time. For a reference image, a Voronoi aesthetic pattern generation algorithm with uncertainty based on cloud model is proposed, including uncertain line representation using an extended cloud model and Voronoi polygon approximation filling with uncertainty. In view of the different parameters, seven groups of experiments and various experimental analyses are conducted. Compared with the related algorithms, the proposed technique performs better on running time, and its time complexity is approximatively linear related to the size of the input image. The experimental results show that it can produce visually similar effect with the frayed or cracked soil and has three advantages, that is, uncertainty, simplicity, and efficiency. The proposal can be a powerful alternative to the traditional methods and has a prospect of applications in the digital entertainment, home decoration, clothing design, and various fields.

scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

Multiple Image Based Human Joint Angle Computation

2017 ◽  
Vol 865 ◽  
pp. 547-553 ◽  
Author(s):  
Ji Hun Park
Keyword(s):  
Nonlinear Programming ◽  
Joint Angle ◽  
Video Stream ◽  
Input Image ◽  
Reference Image ◽  
Computation Method ◽  
Feature Points ◽  
Center Of Rotation ◽  
Volumetric Reconstruction ◽  
Human Joint

This paper presents a new computation method for human joint angle. A human structure is modelled as an articulated rigid body kinematics in single video stream. Every input image consists of a rotating articulated segment with a different 3D angle. Angle computation for a human joint is achieved by several steps. First we compute internal as well as external parameters of a camera using feature points of fixed environment using nonlinear programming. We set an image as a reference image frame for 3D scene analysis for a rotating articulated segment. Then we compute angles of rotation and a center of rotation of the segment for each input frames using corresponding feature points as well as computed camera parameters using nonlinear programming. With computed angles of rotation and a center of rotation, we can perform volumetric reconstruction of an articulated human body in 3D. Basic idea for volumetric reconstruction is regarding separate 3D reconstruction for each articulated body segment. Volume reconstruction in 3D for a rotating segment is done by modifying transformation relation of world-to-camera to adjust an angle of rotation of a rotated segment as if there were no rotation for the segment. Our experimental results for a single rotating segment show our method works well.

Reference Image Guided Super-Resolution via Progressive Channel Attention Networks

2020 ◽  
Vol 35 (3) ◽  
pp. 551-563
Author(s):  
Huan-Jing Yue ◽  
Sheng Shen ◽  
Jing-Yu Yang ◽  
Hao-Feng Hu ◽  
Yan-Fang Chen
Keyword(s):  
Super Resolution ◽  
Reference Image ◽  
Attention Networks ◽  
Image Guided

A Dynamic Round-Robin Packet Scheduling Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2203-2207 ◽  
Author(s):  
Yong He ◽  
Lei Gao ◽  
Gui Kai Liu ◽  
Yu Zhen Liu
Keyword(s):  
Time Complexity ◽  
High Efficiency ◽  
Packet Scheduling ◽  
Simulation Analysis ◽  
Scheduling Algorithm ◽  
Round Robin ◽  
Good Time ◽  
Performance Simulation ◽  
Fair Scheduling ◽  
Packet Scheduling Algorithm

This paper puts forward a new dynamic round-robin (DYRR) packet scheduling algorithm with high efficiency and good fairness. DYRR algorithm introduces dynamic round-robin concept, that is, the allowance given to each of the flows in a given round is not fixed, but is related with the number of bytes sent of this and other flows of the last round scheduling. The time complexity of the DYRR algorithm is O(1). Results from performance simulation analysis shows that DYRR algorithm can effectively smooth output burst, realize fair scheduling, and have a good time delay characteristic.

Comparison of Traditional 2D and Virtual Patterns Design in 3D

2009 ◽  
Vol 13 (5) ◽  
pp. 542-549
Author(s):  
Agnieszka Cichocka ◽  
◽  
Pascal Bruniaux ◽  
Keyword(s):  
Design Method ◽  
Pattern Generation ◽  
The Body ◽  
Generation Process ◽  
Clothing Design ◽  
Symmetric Pattern ◽  
Present Context ◽  
Left And Right ◽  
The Right ◽  
Garment Design

This paper presents a garment pattern generation process and modelling of virtual garment design method in 3D. This work characterize our global project on virtual clothing design contains of the conception of virtual adaptive mannequin, and also of the creation and modelling of garment in 3D. According to the ideas of mass customization and e-commerce, as well the need of numerical innovations in garment industry we employ our model of virtual garment and methodology enabling to conceive the virtual clothing directly on a mannequin morphotype in 3D. This method gives us possibility to create a perfect garment and taking into account all peculiarities of human body. In 2D method we made two patterns: left and right side separately to obtain best result and to get possibility to compare two cases: when symmetric pattern is used and when we use pattern consisted of two parts specially made for each side of the body. In the present context we use the example of a basic women's shirt: bodice and sleeve. We used the method of making pattern employed in Russia compared with French method. Finally the superposition of virtual and real pattern was done in order to visualise the right results.

scholarly journals Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Tao Wu
Keyword(s):  
Evolutionary Algorithm ◽  
Cloud Model ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Input Image ◽  
Average Score ◽  
Signal To Noise ◽  
Feature Similarity ◽  
Contrast Factor ◽  
Noise Ratio

The process of creating nonphotorealistic rendering images and animations can be enjoyable if a useful method is involved. We use an evolutionary algorithm to generate painterly styles of images. Given an input image as the reference target, a cloud model-based evolutionary algorithm that will rerender the target image with nonphotorealistic effects is evolved. The resulting animations have an interesting characteristic in which the target slowly emerges from a set of strokes. A number of experiments are performed, as well as visual comparisons, quantitative comparisons, and user studies. The average scores in normalized feature similarity of standard pixel-wise peak signal-to-noise ratio, mean structural similarity, feature similarity, and gradient similarity based metric are 0.486, 0.628, 0.579, and 0.640, respectively. The average scores in normalized aesthetic measures of Benford’s law, fractal dimension, global contrast factor, and Shannon’s entropy are 0.630, 0.397, 0.418, and 0.708, respectively. Compared with those of similar method, the average score of the proposed method, except peak signal-to-noise ratio, is higher by approximately 10%. The results suggest that the proposed method can generate appealing images and animations with different styles by choosing different strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art.

scholarly journals The Improved Algorithm of Fast Panorama Stitching for Image Sequence and Reducing the Distortion Errors

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhong Qu ◽  
Si-Peng Lin ◽  
Fang-Rong Ju ◽  
Ling Liu
Keyword(s):  
Feature Detection ◽  
Image Sequence ◽  
Input Image ◽  
Coordinate Space ◽  
Reference Image ◽  
Image Stitching ◽  
Feature Points ◽  
Improved Method ◽  
Sift Feature ◽  
Improved Algorithm

The traditional image stitching result based on the SIFT feature points extraction, to a certain extent, has distortion errors. The panorama, especially, would get more seriously distorted when compositing a panoramic result using a long image sequence. To achieve the goal of creating a high-quality panorama, the improved algorithm is proposed in this paper, including altering the way of selecting the reference image and putting forward a method that can compute the transformation matrix for any image of the sequence to align with the reference image in the same coordinate space. Additionally, the improved stitching method dynamically selects the next input image based on the number of SIFT matching points. Compared with the traditional stitching process, the improved method increases the number of matching feature points and reduces SIFT feature detection area of the reference image. The experimental results show that the improved method can not only accelerate the efficiency of image stitching processing, but also reduce the panoramic distortion errors, and finally we can obtain a pleasing panoramic result.

Fast Generation of Micro-Channels on Cylindrical Surfaces by Elliptical Vibration Texturing

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Ping Guo ◽  
Yong Lu ◽  
Pucheng Pei ◽  
Kornel F. Ehmann
Keyword(s):  
Process Parameters ◽  
High Efficiency ◽  
Low Cost ◽  
Pattern Generation ◽  
Depth Of Cut ◽  
Channel Formation ◽  
Structured Surfaces ◽  
Elliptical Vibration ◽  
Micro Channels ◽  
Elliptical Vibration Texturing

Micro-structured surfaces are assuming an ever-increasing role since they define the ultimate performance of many industrial components and products. Micro-channels, in particular, have many potential applications in micro-fluidic devices, micro heat exchangers, and friction control. This paper proposes an innovative vibration-assisted machining method to generate micro-channels on the external surface of a cylinder. This method, referred to as elliptical vibration texturing, was originally developed by the authors to generate dimple patterns. It uses the modulation of the depth-of-cut by tool vibrations to create surface textures. The most promising features of the proposed method are its high efficiency, low cost, and scalability for mass production. It is shown that with proper combinations of the process parameters the created dimples start to overlap and form channels. An analytical model is established to predict channel formation with respect to the overlapping ratios of the dimples. Channel formation criteria and expressions for channel geometries are given along with a channel generation map that relates channel geometry to the process parameters. Experimental results are given to verify the model. A further example of micro-pattern generation is also given to showcase the flexibility of the process.

scholarly journals High-Efficiency Min-Entropy Estimation Based on Neural Network for Random Number Generators

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Na Lv ◽  
Tianyu Chen ◽  
Shuangyi Zhu ◽  
Jing Yang ◽  
Yuan Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Sample Size ◽  
Time Complexity ◽  
Random Number ◽  
High Efficiency ◽  
Estimation Methods ◽  
Random Numbers ◽  
Real World Data ◽  
Entropy Estimation

Random number generator (RNG) is a fundamental and important cryptographic element, which has made an outstanding contribution to guaranteeing the network and communication security of cryptographic applications in the Internet age. In reality, if the random number used cannot provide sufficient randomness (unpredictability) as expected, these cryptographic applications are vulnerable to security threats and cause system crashes. Min-entropy is one of the approaches that are usually employed to quantify the unpredictability. The NIST Special Publication 800-90B adopts the concept of min-entropy in the design of its statistical entropy estimation methods, and the predictive model-based estimators added in the second draft of this standard effectively improve the overall capability of the test suite. However, these predictors have problems on limited application scope and high computational complexity, e.g., they have shortfalls in evaluating random numbers with long dependence and multivariate due to the huge time complexity (i.e., high-order polynomial time complexity). Fortunately, there has been increasing attention to using neural networks to model and forecast time series, and random numbers are also a type of time series. In our work, we propose several new and efficient approaches for min-entropy estimation by using neural network technologies and design a novel execution strategy for the proposed entropy estimation to make it applicable to the validation of both stationary and nonstationary sources. Compared with the 90B’s predictors officially published in 2018, the experimental results on various simulated and real-world data sources demonstrate that our predictors have a better performance on the accuracy, scope of applicability, and execution efficiency. The average execution efficiency of our predictors can be up to 10 times higher than that of the 90B’s for 106 sample size with different sample spaces. Furthermore, when the sample space is over 22 and the sample size is over 108, the 90B’s predictors cannot give estimated results. Instead, our predictors can still provide accurate results. Copyright© 2019 John Wiley & Sons, Ltd.

Sign in / Sign up

Export Citation Format

Share Document