Search

  • Select Article Type
  • Abstract Supplements
  • Blood Group Review
  • Call to Arms
  • Hypothesis
  • In Memoriam
  • Interview
  • Introduction
  • Letter to the Editor
  • Short Report
  • abstract
  • Abstracts
  • Article
  • book-review
  • case-report
  • case-study
  • Clinical Practice
  • Commentary
  • Conference Presentation
  • conference-report
  • congress-report
  • Correction
  • critical-appraisal
  • Editorial
  • Editorial Comment
  • Erratum
  • Events
  • Letter
  • Letter to Editor
  • mini-review
  • minireview
  • News
  • non-scientific
  • Obituary
  • original-paper
  • original-report
  • Original Research
  • Pictorial Review
  • Position Paper
  • Practice Report
  • Preface
  • Preliminary report
  • Product Review
  • rapid-communication
  • Report
  • research-article
  • Research Communicate
  • research-paper
  • Research Report
  • Review
  • review -article
  • review-article
  • review-paper
  • Review Paper
  • Sampling Methods
  • Scientific Commentary
  • short-communication
  • short-report
  • Student Essay
  • Varia
  • Welome
  • Select Journal
  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls

 

Article | 07-May-2018

3D Target Recognition Based on Decision Layer Fusion

Target recognition has always been a hot research topic in computer image and pattern recognition. This paper proposes a target recognition method based on decision layer fusion. ModelNet[1]—The 3D CAD model library, which is used to be identified. Features are extracted from the model’s point cloud data and multi-view images. The image is identified using the AlexNet[2] network, The point cloud is identified by the VoxNet[3] network. The fusion algorithm is used in the decision layer to

Ma Xing, Yu Fan, Yu Haige, Wei Yanxi, Yang Wenhui

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 19–22

Article | 01-June-2015

TARGET RECOGNITION BASED ON ROUGH SET WITH MULTI-SOURCE INFORMATION

As the attributes provided by multi-source information can be used to distinguish between the different species of targets, attributes recognition becomes the most important work in target recognition. In this paper, a new method for attributes recognition was proposed with rough set theory. It used a new way to described the target with a information system consisting of four elements, reduced the attribute value according to the mission requirements, valuated the attribute based on the degree

Cheng Zengping

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1063–1084

Article | 01-March-2015

IMAGE FUSION AND RECOGNITION BASED ON COMPRESSED SENSING THEORY

As the compressed sensing theory can offer a better performance than Nyquist sampling theorem when dealing with large amounts of data, it becomes very popular for image fusion and target recognition in image processing. In this paper, a new image fusion algorithm based on compressed sensing was proposed. By discrete cosine transform, it fused images through weighted coefficient, recovered the fusion images by basic pursuit algorithm. Moreover, a recognition algorithm in compressed sensing was

Qiuchan Bai, Chunxia Jin

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 159–180

Article | 05-September-2013

Research of Image Pre-processing Algorithm Based on FPGA

How to design a low-cost , reliable and real-time target recognition system with large amount of data has become a hot topic in the area of image processing .However, Edge detection has played an important role in target recognition system. The threshold of traditional canny edge detection algorithm must be setting by human, and has a large number of calculations. In order to overcome the shortcomings of the traditional Canny algorithm, proposing an adaptive threshold edge detection algorithm

Yang Yongjin, Zhou Xinmei, Xiang Zhongfan

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1499–1515

Article | 01-December-2016

HYPERSPECTRAL DATA FEATURE EXTRACTION USING DEEP BELIEF NETWORK

Hyperspectral data has rich spectrum information, strong correlation between bands and high data redundancy. Feature band extraction of hyperspectral data is a prerequisite and an important basis for the subsequent study of classification and target recognition. Deep belief network is a kind of deep learning model, the paper proposed a deep belief network to realize the characteristics band extraction of hyperspectral data, and use the advantages of unsupervised and supervised learning of deep

Jiang Xinhua, Xue Heru, Zhang Lina, Zhou Yanqing

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1991–2009

Article | 01-December-2014

RESEARCH ON UNDERWATER TARGET SIGNAL DETECTION AND RECOGNITION PROCESSING ALGORITHM

underwater target signal de-noising was presented. For multiple underwater target recognition, the model of underwater target multi-sensor signal recognition was studied. On the basis of analyzing the principle of D-S method and the fusion of multiple signal recognition, the concrete measures of D-S data fusion reasoning was researched and analyzed. By using the combination of simulation calculation and experiment measures, the results show the signal processing method is correct.

Lijuan Wang, Xiaojing Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1753–1772

Research Article | 13-December-2017

HUMANITARIAN DEMINING ROBOT GRYPHON CURRENT STATUS AND AN OBJECTIVE EVALUATION

Mechanical systems or robots to assist landmine detection are expected to greatly improve quality of humanitarian demining tasks. These new systems could provide: i) safer operation; ii) advanced methods for automatic target recognition and discrimination; iii) consistent performance with less influence of “human-factors”; iv) better detection performance, i.e., higher probability of detection (POD) and lower false alarm rate (FAR); among others. However, despite many research/development

Edwardo F. Fukushima, Marc Freese, Toshiaki Matsuzawa

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 3, 735–753

No Record Found..
Page Actions