scholarly journals Classification For Glass Bottles based on Improved Selective Search Algorithm

2020 ◽  
Vol 64 (1) ◽  
pp. 223-251
Author(s):  
Shuqiang Guo ◽  
Baohai Yu ◽  
Manyang Gao ◽  
Xinxin Zhou ◽  
Bo Wang
Keyword(s):  
Search Algorithm ◽  
Selective Search ◽  
Glass Bottles

scholarly journals Optimal Allocation Algorithm of Geological and Ecological High-resolution Remote Sensing Monitoring Sampling Points

2020 ◽  
Vol 24 (1) ◽  
pp. 105-110
Author(s):  
Taifu Bi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Search Algorithm ◽  
Support Vector ◽  
Allocation Algorithm ◽  
Remote Sensing Monitoring ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Sampling Points ◽  
Selective Search

Abstract: The purpose of this study is to solve the problem of unsatisfactory image representation of monitoring sampling points in high-resolution remote sensing due to the complexity of geological ecology. Firstly, three algorithms used in remote sensing technology were introduced, that is, extraction algorithm of monitoring sampling point (selective search algorithm), discriminant algorithm (support vector machine) and BING algorithm. Then, the BING algorithm was improved. Finally, the superiority of the improved BING algorithm was verified through experimental data set. The results showed that selective search algorithm could generate more candidate windows in remote sensing image and had better adaptability. The improved algorithm had higher quality of candidate windows extracted from remote sensing images. Although the IBING algorithm could greatly improve the extraction speed of remote sensing, the detection time of each image became larger. Such testing times were still acceptable. Therefore, in this research, the allocation algorithm of geological and ecological high-resolution remote sensing monitoring sampling points was optimized, which had a good guiding significance for the application of remote sensing technology in geological and ecological research.

scholarly journals Analysis of Keypoint Detection Algorithms for Mass Candidates Selection in Mammography Images

2018 ◽  
Author(s):  
Felipe Victor de Sá Oliveira ◽  
Gersica Agripino Alencar ◽  
Filipe Rolim Cordeiro
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Search Algorithm ◽  
Reduction Rate ◽  
Candidate Selection ◽  
Correct Interpretation ◽  
Detection Algorithms ◽  
Mammographic Images ◽  
The World ◽  
Selective Search

Breast cancer has been a growing problem for women around the world. The correct interpretation of mammographic images is important for the diagnosis of breast cancer. However, this is a difficult task even for a specialist. Image processing is used to make the diagnosis less susceptible to errors. Thus, the present work proposes a new method for the search of lesion candidates in mammographic images. To verify the efficiency of the approach, the behavior of the SURF, SIFT, BRISK and ORB algorithms were analyzed, as well as the Selective Search algorithm for candidate selection. A total of 1210 mammography images were used, from the CBIS-DDSM database. Results show that the SURF algorithm presented better performance, generating on average, for each image, 4.11 candidates considered in the internal area of the lesion, reducing exploratory space by 72%, whereas the ORB generated on average 1.6 candidates with a reduction rate of 96.30%.

Region Proposal for Line Insulator Based on the Improved Selective Search Algorithm

2020 ◽  
pp. 686-696
Author(s):  
Shuqiang Guo ◽  
Baohai Yue ◽  
Qianlong Bai ◽  
Huanqiang Lin ◽  
Xinxin Zhou
Keyword(s):  
Search Algorithm ◽  
Selective Search

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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