scholarly journals Visual Information Fusion through Bayesian Inference for Adaptive Probability-Oriented Feature Matching

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2041 ◽  
Author(s):  
David Valiente ◽  
Luis Payá ◽  
Luis Jiménez ◽  
Jose Sebastián ◽  
Óscar Reinoso
Keyword(s):  
Bayesian Inference ◽  
Information Fusion ◽  
Visual Information ◽  
Feature Matching

scholarly journals Multi-sensor Information Fusion Method Based on BP Neural Network

2016 ◽  
Vol 12 (05) ◽  
pp. 53 ◽  
Author(s):  
Lin Liandong
Keyword(s):  
Neural Network ◽  
Bayesian Inference ◽  
Information Fusion ◽  
System Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fusion Algorithm ◽  
Temperature Detection ◽  
Posterior Expectation ◽  
Sensor Information

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

Obstacle Avoidance of Omnidirectional Intelligent Wheelchair Based on Multi-Sensors

2015 ◽  
Vol 742 ◽  
pp. 590-593 ◽  
Author(s):  
Xiu Zhi Li ◽  
Zhao Liu ◽  
Song Min Jia
Keyword(s):  
Obstacle Avoidance ◽  
Information Fusion ◽  
Visual Information ◽  
Distance Information ◽  
Wheelchair Users ◽  
Optical Flow Method ◽  
Intelligent Wheelchair ◽  
Handicapped People ◽  
Relevant Community

As the number of handicapped people increases worldwidely, the role of electric wheelchair becomes important to enhance their mobility. In the relevant community, attention is mainly directed to how to solve the problems in motion control for the wheelchair users, and scarce reports have appeared concerning obstacle avoidance of wheelchair. In this paper, we present a new method of obstacle avoidance for omnidirectional intelligent wheelchair bases on multi-sensors information fusion. Distance information acquired from ultrasonic sensors and visual information acquired from monocular camera are combined together, in which optical flow method is employed to distinguish obstacles. Extensive experiments have been conducted in the laboratory. As shown in experimental results that, the developed omnidirectional intelligent wheelchair works correctly and effectively in obstacle avoidance.

Uniting Keypoints: Local Visual Information Fusion for Large-Scale Image Search

2015 ◽  
Vol 17 (4) ◽  
pp. 538-548 ◽  
Author(s):  
Zhen Liu ◽  
Houqiang Li ◽  
Wengang Zhou ◽  
Richang Hong ◽  
Qi Tian
Keyword(s):  
Information Fusion ◽  
Visual Information ◽  
Large Scale ◽  
Image Search

scholarly journals Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing

2020 ◽  
Vol 37 (4) ◽  
pp. 619-626
Author(s):  
Shizhen Bai ◽  
Fuli Han
Keyword(s):  
Image Processing ◽  
Image Retrieval ◽  
Visual Information ◽  
Feature Matching ◽  
Scale Invariant ◽  
Behavior Recognition ◽  
Processing Technologies ◽  
Tourist Behavior ◽  
Scenic Spot ◽  
Spot Image

The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT) features. The SIFT feature extraction includes five steps: scale space construction, local space extreme point detection, precise positioning of key points, determination of key point size and direction, and generation of SIFT descriptor. Next, multiple correlated images were mined for the target scenic spot image, and the feature matching method between the target image and the set of scenic spot images was introduced in details. On this basis, a tourist behavior recognition method was designed based on temporal and spatial consistency. The proposed method was proved effective through experiments. The research results provide theoretical reference for image retrieval and behavior recognition in many other fields.

Toward a Bioinspired Fusion of Color and Infrared Textural Image Information

2001 ◽  
Vol 5 (6) ◽  
pp. 326-332
Author(s):  
Javier Ruiz-del-Solar ◽  
◽  
Aureli Soria-Frisch ◽  
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Human Brain ◽  
Information Fusion ◽  
Visual Information ◽  
Processing Technologies ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
Organizational Principles ◽  
Processing Of Visual Information

Simultaneous progress in sensor and signal processing technologies stimulates the implementation of more refined pattern recognition systems in order to solve problems of increasing complexity. The progress on both technologies induced the implementation of the here presented framework for the fusion of infrared and color textural information. The framework is based on different aspects of the processing of visual information in the human brain. Some organizational principles of multisensorial information fusion in higher associative areas are also reflected in it. Preliminary results, realized in a simplified framework, show the validity of the biological-based approach in the resolution of multisensorial image fusion.

Sign in / Sign up

Export Citation Format

Share Document