scholarly journals Automatic Circle Detection on Images Based on an Evolutionary Algorithm That Reduces the Number of Function Evaluations

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Erik Cuevas ◽  
Eduardo L. Santuario ◽  
Daniel Zaldívar ◽  
Marco Perez-Cisneros
Keyword(s):  
Evolutionary Algorithm ◽  
Hough Transform ◽  
Automatic Detection ◽  
Experimental Results ◽  
Natural Images ◽  
Evolutionary Approach ◽  
Circle Detection ◽  
Noisy Images ◽  
Accuracy Speed ◽  
Fitness Calculation

This paper presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on a newly developed evolutionary algorithm called the Adaptive Population with Reduced Evaluations (APRE). Our proposed algorithm reduces the number of function evaluations through the use of two mechanisms: (1) adapting dynamically the size of the population and (2) incorporating a fitness calculation strategy, which decides whether the calculation or estimation of the new generated individuals is feasible. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. Experimental results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique with regard to accuracy, speed, and robustness.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

SEGMENTATION OF HANDWRITTEN CHINESE CHARACTERS FROM DESTINATION ADDRESSES OF MAIL PIECES

2002 ◽  
Vol 16 (01) ◽  
pp. 85-96 ◽  
Author(s):  
YUE LU ◽  
CHEW LIM TAN ◽  
PENGFEI SHI ◽  
KEHUA ZHANG
Keyword(s):  
Hough Transform ◽  
Structural Features ◽  
Experimental Results ◽  
Chinese Characters ◽  
Topological Relations ◽  
Destination Address

In this paper, we illustrate a method to segment handwritten Chinese characters from destination addresses of mail pieces. Fast Hough transform is utilized to detect the reference lines preprinted on the mail piece. In the segmentation, subassemblies of Chinese characters are merged based on the structural features of Chinese characters and the subassemblies' topological relations, viz. upper–lower, inside–outside and left–right relations. The width of subassemblies and the spacing between neighboring subassemblies in the whole image of the destination address are analyzed to guide the merging of the left–right subassemblies. Experimental results with real mail piece images show that the proposed approach has achieved a promising performance for segmenting handwritten Chinese characters.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

A Robust Multi-Circle Detector Based on Horizontal and Vertical Search Analysis Fitting with Tangent Direction

2019 ◽  
Vol 33 (04) ◽  
pp. 1954013 ◽  
Author(s):  
Shaodong Li ◽  
Zhijiang Du ◽  
Hongjian Yu ◽  
Jiafu Yi
Keyword(s):  
Region Growing ◽  
The Other ◽  
Natural Images ◽  
Circle Detection ◽  
Edge Segment ◽  
Vertical Search ◽  
Search Order ◽  
Tangent Direction ◽  
The One ◽  
Helmholtz Principle

In this paper, we propose an efficient Multi-Circle detector which follows the fixed search order. The method makes use of horizontal and vertical search to realize circle detection, which is named as HVCD. First, this method computes edge areas in a given image. The edge areas could be divided into some regions by means of region growing. Each of regions could be efficiently searched to achieve not only one-pixel wide edges but edge segments as well. Next, the candidate circles can be extracted from every edge segment. Finally, the circle candidates could be validated with the help of Helmholtz principle. Experimental results demonstrate that HVCD could effectively detect circles on synthetic and natural images on the one hand; on the other hand, HVCD here could solve the weakness in the process of circle Hough transform implementation and EDcircles implementation.

Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

2019 ◽  
pp. 258-279
Author(s):  
Lavika Goel ◽  
Lavanya B. ◽  
Pallavi Panchal
Keyword(s):  
Face Recognition ◽  
Evolutionary Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Recognition Rate ◽  
Experimental Results ◽  
Nature Inspired Algorithm ◽  
In The Wild ◽  
Gravitational Search

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.

Semi-Automatic Sensor Ontology Matching Based on Interactive Multi-Objective Evolutionary Algorithm

2020 ◽  
pp. 27-42
Author(s):  
Xingsi Xue ◽  
Junfeng Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Test Performance ◽  
Experimental Results ◽  
Semantic Gap ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Multi Objective ◽  
Dominance Rule ◽  
Ontology Alignment Evaluation Initiative

Since different sensor ontologies are developed independently and for different requirements, a concept in one sensor ontology could be described with different terminologies or in different context in another sensor ontology, which leads to the ontology heterogeneity problem. To bridge the semantic gap between the sensor ontologies, authors propose a semi-automatic sensor ontology matching technique based on an Interactive MOEA (IMOEA), which can utilize the user's knowledge to direct MOEA's search direction. In particular, authors construct a new multi-objective optimal model for the sensor ontology matching problem, and design an IMOEA with t-dominance rule to solve the sensor ontology matching problem. In experiments, the benchmark track and anatomy track from the Ontology Alignment Evaluation Initiative (OAEI) and two pairs of real sensor ontologies are used to test performance of the authors' proposal. The experimental results show the effectiveness of the approach.

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