The Topological Detection Algorithm of Object Arrays in Noisy Context Based on Fuzzy Spatial Information Fusion and Prim Algorithm

2012 ◽  
Author(s):  
Tao Wusha
Keyword(s):  
Information Fusion ◽  
Spatial Information ◽  
Detection Algorithm

scholarly journals Novel Joint Object Detection Algorithm Using Cascading Parallel Detectors

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Zihan Zhou ◽  
Qinghan Lai ◽  
Shuai Ding ◽  
Song Liu
Keyword(s):  
Object Detection ◽  
Information Fusion ◽  
Spatial Information ◽  
Prediction Method ◽  
Detection Algorithm ◽  
Free Module ◽  
High Quality ◽  
Shape Prediction ◽  
Proposed Model ◽  
Open Datasets

Object detection is an essential computer vision task that aims to detect target objects from an image. The traditional models are insufficient to generate a high-quality anchor box. To solve the problem, we propose a novel joint model called guided anchoring Region proposal networks and Cascading Grid Region Convolutional Neural Networks (RCGrid R-CNN), enhancing the ability of object detection. Our proposed model design is a joint object detection algorithm containing an anchor-based and an anchor-free branch in parallel and symmetry. In the anchor-based, we use nine-point spatial information fusion to obtain better anchor box location and introduce the shape prediction method of Guided Anchoring Region Proposal Networks (GA-RPN) to enhance the accuracy of the predicted anchor box. In the anchor-free branch, we introduce the Feature Selective Anchor-Free module (FSAF) to reduce the overlapping anchor boxes to obtain a more accurate anchor box. Furthermore, inspired by cascading theory, we cascade the new-designed detectors to improve the ability of object detection by setting a gradually increasing Intersection over Union (IoU) threshold. Compared with typical baseline models, we comprehensively evaluated our model by conducting experiments on two open datasets: Pascal VOC2007 and COCO2017. The experimental results demonstrate the effectiveness of RCGrid R-CNN in producing a high-quality anchor box.

CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

2021 ◽  
Author(s):  
Shah bano ◽  
Syed Adnan Shah ◽  
Wakeel Ahmad ◽  
Muhammad Ilyas
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Binary Code ◽  
Spatial Information ◽  
Detection Algorithm ◽  
Support Vector ◽  
Surveillance Systems ◽  
Body Parts ◽  
Public Places ◽  
Surveillance Camera

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.

Hyperspectral Anomaly Detection Algorithm Based on Combination of Spectral and Spatial Information

2018 ◽  
Vol 55 (12) ◽  
pp. 122801
Author(s):  
鞠荟荟 Ju Huihui ◽  
刘志刚 Liu Zhigang ◽  
汪洋 Wang Yang
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Detection Algorithm

Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

2012 ◽  
Vol 13 (7) ◽  
pp. 520-533 ◽  
Author(s):  
Jamal Ghasemi ◽  
Mohammad Reza Karami Mollaei ◽  
Reza Ghaderi ◽  
Ali Hojjatoleslami
Keyword(s):  
Brain Tissue ◽  
Information Fusion ◽  
Spatial Information ◽  
Tissue Segmentation ◽  
Dempster Shafer Theory ◽  
Brain Tissue Segmentation ◽  
Shafer Theory

scholarly journals Designing information fusion for the encoding of visual–spatial information

Ergonomics ◽  
2008 ◽  
Vol 51 (6) ◽  
pp. 775-797 ◽  
Author(s):  
Samuel M. Waldron ◽  
John Patrick ◽  
Geoffrey B. Duggan ◽  
Simon Banbury ◽  
Andrew Howes
Keyword(s):  
Information Fusion ◽  
Spatial Information ◽  
Visual Spatial

scholarly journals Robust Duplicate Detection of 2D and 3D Objects

2012 ◽  
pp. 96-117
Author(s):  
Peter Vajda ◽  
Ivan Ivanov ◽  
Lutz Goldmann ◽  
Jong-Seok Lee ◽  
Touradj Ebrahimi
Keyword(s):  
Spatial Information ◽  
Graph Model ◽  
Detection Algorithm ◽  
Still Images ◽  
3D Object ◽  
Duplicate Detection ◽  
Training Images ◽  
Object Based ◽  
Improved Performance ◽  
2D And 3D

In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters in a number of applications. Furthermore, effectiveness of object duplicate detection algorithm is measured over different object classes. The authors’ method is shown to be robust in detecting the same objects even when images with objects are taken from different viewpoints or distances.

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