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Interest point detection using imbalance oriented selection

Qi Li, Jieping Ye, Chandra Kambhamettu
2008 Pattern Recognition  
Interest point detection has a wide range of applications, such as image retrieval and object recognition.  ...  Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection.  ...  The research of Q. Li  ... 
doi:10.1016/j.patcog.2007.06.020 fatcat:stwedspp6fau3as4awdpivkbg4

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Sungho Suh, Haebom Lee, Jun Jo, Paul Lukowicz, Yong Lee
2019 Applied Sciences  
Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.  ...  Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies  ...  Acknowledgments: We thank Andrea Bubert and Stefan Quabeck in ISEA of RWTH Aachen University for discussion on data collection and synthetic fault data generation.  ... 
doi:10.3390/app9040746 fatcat:pybgkid22vc6rpbxumyswzeqwm

Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images

Zhu Mao, Fan Zhang, Xianfeng Huang, Xiangyang Jia, Yiping Gong, Qin Zou
2021 Remote Sensing  
First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy.  ...  Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal.  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/rs13050879 fatcat:atfj3xxjt5cydptm3mxvo54z7q

Closed loop control of heliostats

Abraham Kribus, Irina Vishnevetsky, Amnon Yogev, Tatiana Rubinov
2004 Energy  
The method includes a dynamic measurement of the actual radiation incident around the receiver's aperture (spillage), detection of aiming errors, and feedback of a correction signal to the tracking algorithm  ...  Resolution of the closed loop detection algorithm can reach 0.1 mrad, which is insignificant in the overall heliostat beam quality. #  ...  Acknowledgements Support for this work was provided by the Israel Ministry of National Infrastructure. Contributions by R. Ben-Mair, M. Huleihil, Y. Levy, Y. Adikman and J.  ... 
doi:10.1016/s0360-5442(03)00195-6 fatcat:rftp2dopvvg55pdx3xxxdisylu

A distribution balance-based data augmentation method for light-trap pest detection

Yue Teng, Rujing Wang, Ziliang Huang, Shijian Zheng, Qiong Zhou, Jie Zhang, Yingfa Lu, Changbo Cheng
2022 International Conference on Computer Application and Information Security (ICCAIS 2021)  
Existing deep learning-based methods improve the capacity of feature extraction, but ignore the imbalance of object number and size distribution result in insufficient performance.  ...  Agriculture pest disaster is one of the most important reasons that reduce grain yield. Accurate recognition and detection are the core of Integrated Pest Management (IPM).  ...  Due to the rapid development of general object detection, researchers attempt to detect pests with deep learning-based methods.  ... 
doi:10.1117/12.2637829 fatcat:kwhqnvftjffn5npta7nrbw552q

Three-Dimensional Image Inpainting System Using 3D-ED-GAN for Efficient Vision-Based Detection for Rotor Dynamic Balance System

Yi-Hao Chung, Yen-Lin Chen
2022 IEEE Access  
We propose a three-dimensional (3D) image inpainting system using the 3D encoder-decoder generative adversarial network (IISU3EDGAN) for providing accurate detection results in vision-based rotor dynamic  ...  In rotor component detection processes, overexposed images caused by reflections of the metallic rotor shaft affect the accuracy and performance of vision-based inspection systems.  ...  ACKNOWLEDGMENT The authors would like to thank Chao-Wei Yu and Hank Chen for their assistance in experiments, Chun-Ting Chen for her English editing, and the support of TECO Electric and Machinery Company  ... 
doi:10.1109/access.2022.3180339 fatcat:rdnkkgwzkreork6sapeuwgoxqi

Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging

Olivier Janssens, Mia Loccufier, Rik Van de Walle, Sofie Van Hoecke
2017 Infrared physics & technology  
The fault detection is done using an image processing and machine learning solution which can accurately detect the majority of the faults and conditions in our data set.  ...  In this paper the conditions considered are outer-raceway damage in bearings, hard-particle contamination in lubricant and several gradations of shaft imbalance.  ...  In general, there is a relationship between the weight causing the imbalance and the displacement in the image 2 .  ... 
doi:10.1016/j.infrared.2017.02.009 fatcat:kpnfdslmnbbyrbvpglu22bnnre

Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks

Gyorgy Dan, Muhammad Altamash Khan, Viktoria Fodor
2015 IEEE transactions on multimedia  
Our results show that if a-priori information is available about the images, then Top-M interest point selection, limited, octavebased processing at the camera node, together with area-based interest point  ...  We study the statistical characteristics of SURF and BRISK interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource constrained  ...  In general, feature extraction techniques consider the pixel data of an image, detect a set of interest points and extract the related feature descriptors.  ... 
doi:10.1109/tmm.2015.2406574 fatcat:4wzvv5djvfetzatktd6zx4zbp4

Imbalance Problems in Object Detection: A Review [article]

Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas
2020 arXiv   pre-print
In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonomy.  ...  In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before.  ...  ACKNOWLEDGMENTS This work was partially supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) through the project titled "Object Detection in Videos with Deep Neural Networks  ... 
arXiv:1909.00169v3 fatcat:4gzhgl2mirg6zcc2g63e5ha6zi

Mine Classification With Imbalanced Data

D.P. Williams, V. Myers, M.S. Silvious
2009 IEEE Geoscience and Remote Sensing Letters  
In many remote-sensing classification problems, the number of targets (e.g., mines) present is very small compared with the number of clutter objects.  ...  Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly.  ...  Manuscript Various approaches have been developed to handle the issue of class imbalance in general.  ... 
doi:10.1109/lgrs.2009.2021964 fatcat:37p2gioq7rcyxkdjypydwxwxxy

Focal Loss in 3D Object Detection [article]

Peng Yun, Lei Tai, Yuan Wang, Chengju Liu, Ming Liu
2019 arXiv   pre-print
Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to  ...  In this paper, we aim to solve this fore-background imbalance in 3D object detection.  ...  TABLE I I IMAGE-BASED AND POINT-CLOUD-BASED OBJECT DETECTION Image-Based Object Detection Point-Cloud-Based Object Detection Method - 3D-FCN [6] VoxelNet [7] Dimension 2D 3D 3D Input Dense  ... 
arXiv:1809.06065v3 fatcat:dxciuqatevhmhew5fg2lvn3pxu

CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance [chapter]

Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
2017 Lecture Notes in Computer Science  
Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent  ...  UNet) to the point where state of the art results are achieved using only a trivial detection stage.  ...  This paper proposes a generic approach to tackle class imbalance, by using, during training, an online adaptation of the distribution of majority and minority class examples, in the spirit of curriculum  ... 
doi:10.1007/978-3-319-66179-7_73 fatcat:5z5igwron5g3dcwov76lpa3coa

A survey on generative adversarial networks for imbalance problems in computer vision tasks

Vignesh Sampath, Iñaki Maurtua, Juan José Aguilar Martín, Aitor Gutierrez
2021 Journal of Big Data  
Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection  ...  Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks.  ...  Also, we acknowledge the members of the Autonomous and Intelligent Systems Unit, Tekniker, for valuable discussions and collaborations.  ... 
doi:10.1186/s40537-021-00414-0 pmid:33552840 pmcid:PMC7845583 fatcat:g3p6hbjuj5c5vbe23ms4g6ed6q

Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach [article]

Kumari Deepshikha, Anugunj Naman
2020 arXiv   pre-print
An additional condition is enforced on generator network G to produce points in the convex hull of desired imbalanced class.  ...  Further the contention of adversarial game with classifier C, pushes conditional distribution learned by G towards the periphery of the respective class, compensating the problem of class imbalance.  ...  The generator here is made to generate an image that is real or generate an image for minority labels, which causes deterioration of images generated.  ... 
arXiv:2012.04937v1 fatcat:ru6imhn5mjgz3jnipck6xqbtv4

Balanced-YOLOv3: Addressing the Imbalance Problem of Object Detection in PCB Assembly Scene

Jing Li, Yingqian Chen, Weiye Li, Jinan Gu
2022 Electronics  
The object detection algorithm of the PCB (Printed Circuit Board) assembly scene based on CNN (Convolutional Neural Network) can significantly improve the production capacity of intelligent manufacturing  ...  Compared with other current anchor-based object detection algorithms, Balanced-YOLOv3 has excellent detection performance and low computational complexity, which effectively solves the problem of imbalanced  ...  Conflicts of Interest: The authors declare they have no conflict of interest.  ... 
doi:10.3390/electronics11081183 fatcat:dlsmnu7f7nhebg3ege7llvisdi
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