Automated Design of Image Recognition Process for Picking System

2016 ◽  
Vol 10 (5) ◽  
pp. 737-752 ◽  
Author(s):  
Taiki Ogata ◽  
◽  
Kazuaki Tsujimoto ◽  
Taigo Yukisawa ◽  
Yanjiang Huang ◽  
...  
Keyword(s):  
Image Recognition ◽  
Optimization Problem ◽  
Algorithm Design ◽  
Optimization Method ◽  
Automated Design ◽  
Recognition Algorithm ◽  
Design System ◽  
Patterned Surfaces ◽  
Recognition Method ◽  
F Measure

In this study, we propose an automated design system for an image recognition algorithm applicable to picking work in general and actual factory environments. Considering that an image recognition algorithm design consists of frameworks for selecting a rough recognition method from any of the three basic procedures of pre-processing of contained images, feature-extraction, and discrimination, we formulate it as an optimization problem and propose a random multi-start optimization method by which to derive solutions. We have conducted four types of evaluation experiments for the following four combinations: large or small degrees of similarity in the shape of objects to be recognized and whether they have patterned surfaces. The evaluation experiments show that the proposed design system succeeds in selecting a framework that fits the features of the objects to be recognized and that the designed basic processes have an F measure of 0.9 or more.

An Image Recognition Method Based on Ant Colony Optimization Neural Network Using in Imaging Fuze

2011 ◽  
Vol 121-126 ◽  
pp. 1886-1890
Author(s):  
Ke Yong Wang ◽  
Shi Kai Xing
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Image Recognition ◽  
Recognition Algorithm ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Target Image ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Training Time

Target image recognition is an important issue in the information processing of imaging fuse system. In the paper, the main frame is proposed which can solve the problem of target image recognition and many computer simulation experiments are carried out. A recognition algorithm based on ant colony optimization and neural network is proposed. It overcomes the shortcomings of traditional BP algorithm and converges fast. The results of experiments prove that the presented algorithm can shorten the training time effectively and increase the accuracy of recognition, so it is very useful in improving the effective destroying ability of the missile.

scholarly journals An Novel Image Recognition method based on Three-way Decision

2018 ◽  
Vol 176 ◽  
pp. 01035
Author(s):  
Jin Dai ◽  
Shuai Shao ◽  
Zu Wang ◽  
Xianjing Zhao
Keyword(s):  
Image Recognition ◽  
Iterative Process ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Positive Sample ◽  
Human Cognition ◽  
Recognition Method ◽  
Decision Region ◽  
Level Information ◽  
Delay Decision

The traditional recognition method takes the low-level information of the image as the foundation. The image recognition center of gravity is biased towards the typical features, and achieves the effect of recognition by region-dependent segmentation. Because the general image segmentation is a regular rectangle, easily lead to the same target is divided into different sub-blocks, ignoring the image of the fuzzy part, so the image recognition is not complete. An image recognition algorithm based on threeway decision is proposed. It takes full advantage of effective information in the image, improving the image recognition accuracy. First, this method divided the image into three regions: positive region, negative region and delay decision region. Second, an iterative process is performed on the region of the delay decision. Final, image recognition is performed on the positive sample region. Based on the basic theory of the three-way decision, the more obvious the decision result is, the more iterations are, and the information is added to the classifier until the blurred part of image cannot be divided. Finally, to achieve the realize effective image recognition. This method simulates the process of human cognition effectively, and makes the utilization of the effective information reach the maximum in the recognition process. The results of the experimental analysis showed that the method is more concise and efficient, and the recognition accuracy is more accurate.

scholarly journals Apple Image Recognition Multi-Objective Method Based on the Adaptive Harmony Search Algorithm with Simulation and Creation

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 180 ◽  
Author(s):  
Liqun Liu ◽  
Jiuyuan Huo
Keyword(s):  
Image Recognition ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Natural Scene ◽  
Multi Objective Optimization ◽  
Recognition Method ◽  
Multi Objective

Aiming at the low recognition effect of apple images captured in a natural scene, and the problem that the OTSU algorithm has a single threshold, lack of adaptability, easily caused noise interference, and over-segmentation, an apple image recognition multi-objective method based on the adaptive harmony search algorithm with simulation and creation is proposed in this paper. The new adaptive harmony search algorithm with simulation and creation expands the search space to maintain the diversity of the solution and accelerates the convergence of the algorithm. In the search process, the harmony tone simulation operator is used to make each harmony tone evolve towards the optimal harmony individual direction to ensure the global search ability of the algorithm. Despite no improvement in the evolution, the harmony tone creation operator is used to make each harmony tone to stay away from the current optimal harmony individual for extending the search space to maintain the diversity of solutions. The adaptive factor of the harmony tone was used to restrain random searching of the two operators to accelerate the convergence ability of the algorithm. The multi-objective optimization recognition method transforms the apple image recognition problem collected in the natural scene into a multi-objective optimization problem, and uses the new adaptive harmony search algorithm with simulation and creation as the image threshold search strategy. The maximum class variance and maximum entropy are chosen as the objective functions of the multi-objective optimization problem. Compared with HS, HIS, GHS, and SGHS algorithms, the experimental results showed that the improved algorithm has higher a convergence speed and accuracy, and maintains optimal performance in high-dimensional, large-scale harmony memory. The proposed multi-objective optimization recognition method obtains a set of non-dominated threshold solution sets, which is more flexible than the OTSU algorithm in the opportunity of threshold selection. The selected threshold has better adaptive characteristics and has good image segmentation results.

Research on Action Design System Based on VB.NET Technology

2014 ◽  
Vol 644-650 ◽  
pp. 2929-2933
Author(s):  
Lin Xu ◽  
Wei Lei ◽  
Wei Min Xu
Keyword(s):  
Image Recognition ◽  
Martial Arts ◽  
Traditional Education ◽  
Block Matching ◽  
Internet Technology ◽  
Design System ◽  
Recognition Method ◽  
Complementary Relationship ◽  
Platform Architecture ◽  
Application Scope

With the continuous development of internet technology, its application scope has been widely. In the field of education, it is becoming more and more mature and forming a complementary relationship with traditional education. In this paper, through t computer.NET platform architecture, we analyze the composition and running mechanism of.NET structure. Based on this we construct the exact image recognition method with block matching, which matches the martial arts action image. We use VB.NET internet technology to construct the martial arts teaching action design system, and use simulation technology to test. The results show that the system can meet the needs of martial arts teaching, and has strong practicability.

scholarly journals Dual-channel convolutional neural network for power edge image recognition

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fangrong Zhou ◽  
Yi Ma ◽  
Bo Wang ◽  
Gang Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Classification Method ◽  
Power Equipment ◽  
Recognition Method ◽  
Edge Image ◽  
Dual Channel

AbstractIn view of the low accuracy and poor processing capacity of traditional power equipment image recognition methods, this paper proposes a power equipment image recognition method based on a dual-channel convolutional neural network (DC-CNN) model and random forest (RF) classification. In the aspect of feature extraction, the DC-CNN model extracts the characteristics of power equipment through two independent CNN models. In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the RF, an RF classification method incorporating deep learning is proposed. Finally, the proposed DC-CNN model and RF classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images.

The Improvement of Newton Method and the Validation of Optimization Idea of Blind Walking Repeatedly

2011 ◽  
Vol 250-253 ◽  
pp. 4061-4064
Author(s):  
Chun Ling Zhang
Keyword(s):  
Saddle Point ◽  
Newton Method ◽  
Optimization Problem ◽  
Initial Point ◽  
Optimization Method ◽  
Maximum Point ◽  
Optimal Result ◽  
Feasible Domain ◽  
Parallel Arithmetic ◽  
Newton Direction

The existence of maximum point, oddity point and saddle point often leads to computation failure. The optimization idea is based on the reality that the optimum towards the local minimum related the initial point. After getting several optimal results with different initial point, the best result is taken as the final optimal result. The arithmetic improvement of multi-dimension Newton method is improved. The improvement is important for the optimization method with grads convergence rule or searching direction constructed by grads. A computational example with a saddle point, maximum point and oddity point is studied by multi-dimension Newton method, damped Newton method and Newton direction method. The importance of the idea of blind walking repeatedly is testified. Owing to the parallel arithmetic of modernistic optimization method, it does not need to study optimization problem with seriate feasible domain by modernistic optimization method.

Parameter study and optimization design of a hat oblique tunnel portal

2021 ◽  
pp. 095440972110140
Author(s):  
Zijian Guo ◽  
Tanghong Liu ◽  
Wenhui Li ◽  
Yutao Xia
Keyword(s):  
High Speed ◽  
Optimization Design ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Method ◽  
Design Parameters ◽  
Parameter Study ◽  
Buffer Structure ◽  
Tunnel Entrance ◽  
The Ideal

The present work focuses on the aerodynamic problems resulting from a high-speed train (HST) passing through a tunnel. Numerical simulations were employed to obtain the numerical results, and they were verified by a moving-model test. Two responses, [Formula: see text] (coefficient of the peak-to-peak pressure of a single fluctuation) and[Formula: see text] (pressure value of micro-pressure wave), were studied with regard to the three building parameters of the portal-hat buffer structure of the tunnel entrance and exit. The MOPSO (multi-objective particle swarm optimization) method was employed to solve the optimization problem in order to find the minimum [Formula: see text] and[Formula: see text]. Results showed that the effects of the three design parameters on [Formula: see text] were not monotonous, and the influences of[Formula: see text] (the oblique angle of the portal) and [Formula: see text] (the height of the hat structure) were more significant than that of[Formula: see text] (the angle between the vertical line of the portal and the hat). Monotonically decreasing responses were found in [Formula: see text] for [Formula: see text] and[Formula: see text]. The Pareto front of [Formula: see text] and[Formula: see text]was obtained. The ideal single-objective optimums for each response located at the ends of the Pareto front had values of 1.0560 for [Formula: see text] and 101.8 Pa for[Formula: see text].

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