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Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification [article]

Yilun Cao and Hyungtae Lee and Heesung Kwon
2016 arXiv   pre-print
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

Hyungtae Lee, Heesung Kwon
2019 IEEE Transactions on Pattern Analysis and Machine Intelligence  
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

Sanjay Sharma, Padma J., Samta Gajbhiye
2017 International Journal of Computer Applications  
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  
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  
., +, 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

Zhigeng Pan, Xun Wang
2020 Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)  
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]

Nada Milisavljevic, Isabelle Bloch, Marc Acheroy
2008 Humanitarian Demining  
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

Paolo Gamba, Jocelyn Chanussot
2008 IEEE Transactions on Geoscience and Remote Sensing  
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

Mohamed Lamine Mekhalfi, Carlo Nicolò, Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Eslam Al Maghayreh
2021 Applied Sciences  
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  
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

Lifu Chen, Peng Zhang, Jin Xing, Zhenhong Li, Xuemin Xing, Zhihui Yuan
2020 Remote Sensing  
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  
., +, 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

Erik Blasch, Jean Dezert, B. Pannetier, Ivan Kadar
2013 Signal Processing, Sensor Fusion, and Target Recognition XXII  
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]

Anna Petrovskaya, Mathias Perrollaz, Luciano Oliveira, Luciano Spinello, Rudolph Triebel, Alexandros Makris, John-David Yoder, Christian Laugier, Urbano Nunes, Pierre Bessiere
2012 Handbook of Intelligent Vehicles  
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

Aarushi Thakral, Shaurya Shekhar, Akila Victor
2017 International Journal of Computer Applications  
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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