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Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification
[article]
2016
arXiv
pre-print
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In this paper, we introduce a novel fusion method that can enhance object detection performance by fusing decisions from two different types of computer vision tasks: object detection and image classification ...
A recently introduced novel fusion approach called dynamic belief fusion (DBF) is used to fuse the detector output with the classification prior. ...
[11] recently introduced a late fusion approach, called dynamic belief fusion (DBF), which can effectively integrate decisions from multiple complementary object detection algorithms providing enhanced ...
arXiv:1610.06907v1
fatcat:pubi5cg5evboddyr27sa52t6da
DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors
2019
IEEE Transactions on Pattern Analysis and Machine Intelligence
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2022 pre-print arXiv:2204.02890v1 fatcat:ldnondb6dneprbgq4t2itsibvi
In this paper, we propose a novel and highly practical score-level fusion approach called dynamic belief fusion (DBF) that directly integrates inference scores of individual detections from multiple object ...
for the fusion. ...
The final output of the fusion process is a consolidated set of detections, each with a fused detection score.
Dynamic Belief Fusion Object Detection Hypotheses. ...
doi:10.1109/tpami.2019.2952847
pmid:31722478
fatcat:mj7r4wdjlfeibkkdcd4u3rcpx4
A Visibility Restoration Algorithm for Real-World Hazy Scenes
2017
International Journal of Computer Applications
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The detection of objects within the scene is more difficult. ...
Therefore visibility improvement, contrast and features enhancement of images and videos captured in bad weather are also called as dehazing, is an inevitable task. ...
Moreover, all the moving object detection issues of a robot's indoor navigation has been solved by divided and conquered via multisensory fusion methodologies. ...
doi:10.5120/ijca2017914422
fatcat:yrimtoo6rvcs5od2fztqow6r7e
Author Index
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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from Unconstrained Video Sequences
Workshop: A Mutual-Information Scale-space for Image Feature Detection and Feature-based
Classification of Volumetric Brain Images
Toga, Arthur W. ...
Cascade Object Detection with Deformable Part Models
Givens, Geof H. ...
doi:10.1109/cvpr.2010.5539913
fatcat:y6m5knstrzfyfin6jzusc42p54
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 628-640 SAFNet: A Semi-Anchor-Free Network With Enhanced Feature Pyramid for Object Detection. ...
., +, TIP 2020 3092-3103
Hierarchical Feature Fusion Network for Salient Object Detection. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Front Matter: Volume 11373
2020
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
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These two-number sets start with 00, 01, 02, 03, 04, ...
face dataset annotation 11373 05 Automatic tree species identification from natural bark image 11373 06 FRCA: High-efficiency container number detection and recognition algorithm with enhanced attention ...
of affordable house from GF-1 panchromatic image by geographic constraint
[11373-11]
11373 1J
Semi-supervised image classification via attention mechanism and generative adversarial
network [11373 ...
doi:10.1117/12.2561685
fatcat:5dwsd2oxjjcdllprs6rtum4tfu
Multi-sensor Data Fusion Based on Belief Functions and Possibility Theory: Close Range Antipersonnel Mine Detection and Remote Sensing Mined Area Reduction
[chapter]
2008
Humanitarian Demining
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For mined area reduction, three approaches are shown, two of them based on the belief functions and one based on the fuzzy logic. ...
: region-based classification and belief function classification; 3: region-based classification and road detection; 4: region-based classification, minimum distance classification and belief function ...
" meaningful classes); 8: region-based classification, belief function classification and river detection. ...
doi:10.5772/5410
fatcat:smek4osccrfwxfhrxer5o36bom
Foreword to the Special Issue on Data Fusion
2008
IEEE Transactions on Geoscience and Remote Sensing
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Decision Fusion For most of the usual tasks in remote sensing (detection, classification, segmentation, etc.), an abundant literature can be found, with numerous algorithms being proposed. ...
For a recent general survey paper with classification on information fusion, please refer to [13] .
II. ...
He is currently an Associate Professor of telecommunications with the Department of Electronics, University of Pavia. ...
doi:10.1109/tgrs.2008.919761
fatcat:hpgl2mjfezg7ljq2nfnyxty2zu
Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3
2021
Applied Sciences
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In order to quantify the performance, we build a crop circles dataset from images extracted via Google Earth over a desert area in the East Oweinat in the South-Western Desert of Egypt. ...
In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) ...
In particular, it is evident that remote sensing image classification and object detection remain the most active topics so far. ...
doi:10.3390/app11052238
doaj:829a823490c54cec8b9f8587b63c19da
fatcat:3s5pvnuryja4rnc5iqfbdbkr7e
Table of contents
2019
IEEE Transactions on Image Processing
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Piazzo 713 Biomedical and Biological Image Processing Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions ........... .................................... ...
Kautz 723 Radar Imaging, Remote Sensing, and Geophysical Imaging Hyperspectral Imagery Classification via Stochastic HHSVMs ................................................................ ............ ...
doi:10.1109/tip.2018.2878280
fatcat:cwalaxbmvfd3xmp5wdn2kwsk3e
A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas
2020
Remote Sensing
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Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. ...
Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. ...
Due to the speckle noise in SAR images and confusing characteristics of other objects (e.g., shadows) in SAR images, automatic water body detection from SAR images with high precision remains an open challenge ...
doi:10.3390/rs12193205
fatcat:5yedpdnzhjfazp63wfvzu5iw6q
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 293-306 Image enhancement A Contextual Bidirectional Enhancement Method for Remote Sensing Image Object Detection. ...
Missile Sites Using Spatial Fusion of Component Object Detections From Deep Neural Networks. ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
Overview of Dempster-Shafer and belief function tracking methods
2013
Signal Processing, Sensor Fusion, and Target Recognition XXII
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One example of set-based methods is the use of Dempster-Shafer (DS) techniques to support belief-function (BF) tracking. ...
In this paper, we overview the issues and concepts that motivated DS methods for simultaneous tracking and classification/identification. ...
masses with uniform priors. ...
doi:10.1117/12.2016326
fatcat:fnbyw6npdvfa3gdpoiymefjed4
Awareness of Road Scene Participants for Autonomous Driving
[chapter]
2012
Handbook of Intelligent Vehicles
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This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. ...
This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid ...
The objective is to explore diversity of the component classifiers in order to enhance the overall classification performance. ...
doi:10.1007/978-0-85729-085-4_54
fatcat:lrnbnr4q5vbzrd3pausguhbhom
Efficient Object Recognition using Convolution Neural Networks Theorem
2017
International Journal of Computer Applications
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Object recognition is the process of identification of an object in an image. There exist various algorithms for the same. ...
The neural elements learn to recognize objects about which they have no previous information, this "learning" mechanism is affected by the fact that representations of the image are learned by the inner ...
schemes can enhance the classification performance. ...
doi:10.5120/ijca2017913123
fatcat:gwxqn5x2e5b3plawwdvgzlgrey
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