scholarly journals Object Detection in The Image Using the Method of Selecting Significant Structures

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1187
Author(s):  
Vladimir Mokshin ◽  
Ildar Sayfudinov ◽  
Svetlana Yudina ◽  
Leonid Sharnin
Keyword(s):  
Computer Vision ◽  
Iterative Scheme ◽  
Computation Time ◽  
Human Perception ◽  
Local Network ◽  
Video Frame ◽  
Optimization Approach ◽  
Mobile Platforms ◽  
Processing Elements ◽  
Structure Properties

The approach to image segmentation is reviewed in the article. The method of highlighting significant contours in the image is reviewed. Some structures in the image attract attention more than others due to certain distinctive properties. The article reviews the approach of highlighting significant structures in the image representing the areas of candidates identifying the object in the video frame for mobile platforms. For example, such shapes can be smoother, longer and closed. Such structures are called significant. It would be expedient to use only these significant structures to increase the speed of image recognition by computer vision methods focused on the contour selection. This approach allocates the computing resources only to significant structures, thus reducing the total computation time. Since the image consists of many pixels and links between them, which are called edges, significant structures can be measured. The article presents an approach to measuring the structure significance that largely coincides with human perception. Some image structures attract our attention without the need for a systematic scan of the entire image. In most cases, this significance represents the structure properties as a whole, i.e. parts of the structure cannot be isolated. This article presents a measure of significance based on the measurement of length and curvature. The measure highlights structures characteristic of human perception, and they often correspond to objects of interest in the image. A method is presented for calculating significance using an iterative scheme combined into a single local network for processing elements. The optimization approach to represent a processed image highlighting significant locations is used in the network.  

scholarly journals Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 495
Author(s):  
Jessica Thomsen ◽  
Noha Saad Hussein ◽  
Arnold Dolderer ◽  
Christoph Kost
Keyword(s):  
Computation Time ◽  
Energy Systems ◽  
Energy System ◽  
Variable Cost ◽  
Optimization Approach ◽  
Or Efficiency ◽  
Electricity Grid ◽  
Regional Energy ◽  
Long Run ◽  
Regional Energy Systems

Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into demand reduction or efficiency with high upfront costs as it cannot compute the benefits over the time span necessary for full cost recovery. These aspects should be considered when choosing the foresight horizon.

Anti-Raider ATM System Using Mobilenetv2

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Computer Vision ◽  
Research Work ◽  
Computation Time ◽  
Vending Machines ◽  
Google Drive ◽  
Crime Counts

Cash vending machines are ubiquitous and although their technology vouches for its security, they are erratically stormed by the raiders. Albeit the escalating crime counts, the raiders are fleeing from the justice by virtue of evidence lacking. This research work proposes a computer vision based Anti-Raider ATM system. The proposed approach models the image, acquired from the CCTVs against the raider images based on the computer vision and deduces the fact from the MobileNetv2 architecture. Once the model identifies the raider, the image is uploaded to the Google Drive, which serves as evidence for the judicial department. The proposed research is modeled against several optimizers and the result concludes that, among them Adam optimizer has excelled in both computation time and accuracy.

scholarly journals Hyper-optimized tensor network contraction

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 410
Author(s):  
Johnnie Gray ◽  
Stefanos Kourtis
Keyword(s):  
Computation Time ◽  
Quantum Circuit ◽  
Optimization Approach ◽  
Many Body ◽  
Tensor Networks ◽  
Randomized Protocols ◽  
Classical Simulation ◽  
Tensor Network ◽  
Speed Up ◽  
Many Body Systems

Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a variety of benchmarks, including the random quantum circuit instances recently implemented on Google quantum chips. We find that the paths obtained can be very close to optimal, and often many orders or magnitude better than the most established approaches. As different underlying geometries suit different methods, we also introduce a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding. The increase in quality of contraction schemes found has significant practical implications for the simulation of quantum many-body systems and particularly for the benchmarking of new quantum chips. Concretely, we estimate a speed-up of over 10,000× compared to the original expectation for the classical simulation of the Sycamore `supremacy' circuits.

scholarly journals Computer Vision Intelligent Approaches to Extract Human Pose and Its Activity from Image Sequences

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 159
Author(s):  
Paulo J. S. Gonçalves ◽  
Bernardo Lourenço ◽  
Samuel Santos ◽  
Rodolphe Barlogis ◽  
Alexandre Misson
Keyword(s):  
Computer Vision ◽  
Human Body ◽  
Prediction Models ◽  
Image Sequences ◽  
Video Frame ◽  
Support Vector ◽  
Training Phase ◽  
Human Body Model ◽  
Body Model ◽  
Human Pose

The purpose of this work is to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), support vector machines (SVM) and long short-term memory networks (LSTM) to predict human pose and activity from image sequences, based on computer vision approaches to gather the required features. To obtain the human pose semantics (output classes), based on a set of 3D points that describe the human body model (the input variables of the predictive model), prediction models were obtained from the acquired data, for example, video images. In the same way, to predict the semantics of the atomic activities that compose an activity, based again in the human body model extracted at each video frame, prediction models were learned using LSTM networks. In both cases the best learned models were implemented in an application to test the systems. The SVM model obtained 95.97% of correct classification of the six different human poses tackled in this work, during tests in different situations from the training phase. The implemented LSTM learned model achieved an overall accuracy of 88%, during tests in different situations from the training phase. These results demonstrate the validity of both approaches to predict human pose and activity from image sequences. Moreover, the system is capable of obtaining the atomic activities and quantifying the time interval in which each activity takes place.

Hybrid Optimization Approach for Evaluating Photovoltaic Cell Parameters

2013 ◽  
Vol 373-375 ◽  
pp. 1261-1264
Author(s):  
Mei Ying Ye
Keyword(s):  
Cell Model ◽  
Computation Time ◽  
Photovoltaic Cell ◽  
Evaluation Procedure ◽  
Model Parameters ◽  
Optimization Approach ◽  
Problem Space ◽  
Local Optima ◽  
Cell Parameters ◽  
Intelligent Technique

A new hybrid intelligent technique is proposed to evaluate photovoltaic cell model parameters in this paper. The intelligent technique is based on a hybrid of genetic algorithm (GA) and LevenbergMarquardt algorithm (LMA). In the proposed hybrid intelligent technique, the GA firstly searches the entire problem space to get a set of roughly estimated solutions, i.e. near-optimal solutions. Then the LMA performs a local optima search in order to carry out further optimizations. An example has been used to demonstrate the evaluation procedure in order to test the performance of the proposed approach. The results show that the proposed technique has better performance than the GA approach in terms of the objective function value, the computation time and the reconstructedI-Vcurve shape.

An Identification Method of Library Identifiers Based on Computer Vision

2013 ◽  
Vol 380-384 ◽  
pp. 3534-3537
Author(s):  
Li Ya Liu
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Network Model ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Computation Time ◽  
Principal Component ◽  
Structural Features ◽  
Traditional Methods ◽  
Quantum Neural Network

For traditional methods of library identifies based on the two-dimensional code characteristics, these methods are time consuming and a lot of prior experience is required. A method of library identifies based on computer vision technology is proposed. In this method, a preprocessing, such as image equalization, binarization and wavelet change, is first performed on the acquired library label images. Then on the basis of the structural features of the character, the features of library identifiers are obtained by applying PCA for a principal component analysis. A quantum neural network model is designed to have an optimization analysis and calculation on the extracted features, to avoid the drawbacks which need a lot of prior knowledge for the traditional methods. At the same time, an optimization is carried out for the neural network model saving a large amount of computation time. The experimental results show that a recognition rate, up to 98.13%, is obtained by using this method. With a high recognition speed, the method can meet the actual needs to be applied in a practical system.

scholarly journals A Parallel Implementation of Unscheduled Flow Control in Interconnected Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
G. Ozdemir Dag ◽  
Mustafa Bagriyanik
Keyword(s):  
Power Systems ◽  
Power System ◽  
Parallel Computation ◽  
High Performance ◽  
Power Flow ◽  
Parallel Implementation ◽  
Computation Time ◽  
Test System ◽  
Fuzzy Decision Making ◽  
Optimization Approach

The unscheduled power flow problem needs to be minimized or controlled as soon as possible in a deregulated power system since the transmission systems are mostly operated at their power-carrying limits or very close to it. The time spent for simulations to determine the current states of all the system and control variables of the interconnected power system is important. Taking necessary action in case of any failure of equipment or any other occurrence of an undesired situation could be critical. Using supercomputing facilities and parallel computing techniques together decreases the computation time greatly. In this study, a parallel implementation of a multiobjective optimization approach based on both genetic algorithms and fuzzy decision making to manage unscheduled flows is presented. Parallel computation techniques are applied using supercomputers (high-performance computers). The proposed method is applied to the IEEE 300 bus test system. Two different cases for some parameters of GA are considered to see the power of parallel computation technique. Then the simulation results are presented.

scholarly journals Enhancing the Human with Computers: Ongoing research at the Computer Graphics, Image Processing and Interaction Group

2011 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Luciana Nedel ◽  
Anderson Maciel ◽  
Carla Dal Sasso Freitas ◽  
Claudio Jung ◽  
Manuel Oliveira ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Graphics ◽  
Data Visualization ◽  
Human Perception ◽  
Natural World ◽  
Massive Data ◽  
3D Interaction ◽  
Ongoing Research ◽  
Surgery Planning

The Computer Graphics, Image Processing and Interaction (CGIP) group at UFRGS concentrates expertise from many different and complementary graphics related domains. In this paper we introduce the group and present our re- search lines and some ongoing projects. We selected mainly the projects related to 3D interaction and navigation, which includes applications as massive data visualization, surgery planning and simulation, tracking and computer vision algorithms, and modeling approaches for human perception and natural world.

scholarly journals Assessing tACS-induced phosphene perception using closed-loop Bayesian optimization

2017 ◽  
Author(s):  
Romy Lorenz ◽  
Laura E. Simmons ◽  
Ricardo P. Monti ◽  
Joy L. Arthur ◽  
Severin Limal ◽  
...  
Keyword(s):  
Relative Phase ◽  
Rating Scale ◽  
Computational Modelling ◽  
Human Perception ◽  
Past Research ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Experimental Conditions ◽  
Combinatorial Explosion ◽  
Transcranial Alternating Current Stimulation

AbstractTranscranial alternating current stimulation (tACS) can evoke illusory flash-like visual percepts known as phosphenes. The perception of phosphenes represents a major experimental challenge when studying tACS-induced effects on cognitive performance. Besides growing concerns that retinal phosphenes themselves could potentially have neuromodulatory effects, the perception of phosphenes may also modify the alertness of participants. Past research has shown that stimulation intensity, frequency and electrode montage affect phosphene perception. However, to date, the effect of an additional tACS parameter on phosphene perception has been completely overlooked: the relative phase difference between stimulation electrodes. This is a crucial and timely topic given the confounding nature of phosphene perception and the increasing number of studies reporting changes in cognitive function following tACS phase manipulations. However, studying phosphene perception for different frequencies and phases simultaneously is not tractable using standard approaches, as the physiologically plausible range of parameters results in a combinatorial explosion of experimental conditions, yielding impracticable experiment durations. To overcome this limitation, here we applied a Bayesian optimization approach to efficiently sample an exhaustive tACS parameter space. Moreover, unlike conventional methodology, which involves subjects judging the perceived phosphene intensity on a rating scale, our study leveraged the strength of human perception by having the optimization driven based on a subject’s relative judgement. Applying Bayesian optimization for two different montages, we found that phosphene perception was affected by differences in the relative phase between cortical electrodes. The results were replicated in a second study involving new participants and validated using computational modelling. In summary, our results have important implications for the experimental design and conclusions drawn from future tACS studies investigating the effects of phase on cognition.

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