Results 41 to 50 of about 1,105,657 (336)

Efficient and Scalable Object Localization in 3D on Mobile Device

Journal of Imaging, 2022
Two-Dimensional (2D) object detection has been an intensely discussed and researched field of computer vision. With numerous advancements made in the field over the years, we still need to identify a robust approach to efficiently conduct classification ...
Neetika Gupta, Naimul Mefraz Khan
doaj   +1 more source

A Survey of Object Detection for UAVs Based on Deep Learning

Remote Sensing, 2023
With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs.
Guangyi Tang, Jianjun Ni, Yonghao Zhao, Yang Gu, Weidong Cao   +4 more
doaj   +1 more source

Collision Detection for Deformable Objects [PDF]

Computer Graphics Forum, 2005
AbstractInteractive environments for dynamically deforming objects play an important role in surgery simulation and entertainment technology. These environments require fast deformable models and very efficient collision handling techniques. While collision detection for rigid bodies is well investigated, collision detection for deformable objects ...
Teschner, Matthias, Kimmerle, S., Heidelberger, Bruno, Zachmann, Gabriel, Raghupathi, L., Fuhrmann, A., Cani, M.-P., Faure, F., Magnenat-Thalmann, Nadia, Strasser, W., Volino, P.   +10 more
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BlitzNet: A Real-Time Deep Network for Scene Understanding [PDF]

, 2017
Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly performs object detection and semantic segmentation in one forward pass ...
Dvornik, Nikita, Mairal, Julien, Schmid, Cordelia, Shmelkov, Konstantin   +3 more
core   +4 more sources

Detecting Objects from No-Object Regions: A Context-Based Data Augmentation for Object Detection [PDF]

International Journal of Computational Intelligence Systems, 2021
Data augmentation is an important technique to improve the performance of deep learning models in many vision tasks such as object detection. Recently, some works proposed the copy-paste method, which augments training dataset by copying foreground objects and pasting them on background images.
Yutong Chun, Wang Chen, Feiteng Han, Jun Zhang, Kangwei Liu   +4 more
openaire   +2 more sources

Generic Object Detection With Dense Neural Patterns and Regionlets [PDF]

, 2014
This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection.
Lin, Yuanqing, Sun, Miao, Wang, Xiaoyu, Zou, Will Y.   +3 more
core   +1 more source

An Object Detection Using Image Processing In Digital Forensics Science

JISR on Computing, 2018
Object detection is one of the most important sectors in digital forensics science. The object detection technique is valuable for a number of purposes for instance: medical diagnosis scanners, traffic monitoring system, airport security examination ...
Kamran Ali Changezi, Muhammad Wasil Zafar   +1 more
doaj   +1 more source

Enhanced Sparse Detection for End-to-End Object Detection

IEEE Access, 2022
In this paper, we propose an enhanced end-to-end object detector based on Sparse R-CNN (EnSparse R-CNN), which aims at backbone, neck and head of object detector.
Yongwei Liao, Gang Chen, Runnan Xu
doaj   +1 more source

Salient object detection via objectness measure [PDF]

2015 IEEE International Conference on Image Processing (ICIP), 2015
Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth ...
R Sai Srivatsa, R. Venkatesh Babu
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Multiple kernels for object detection [PDF]

2009 IEEE 12th International Conference on Computer Vision, 2009
Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ2 kernels, each of which captures a different feature channel ...
Vedaldi, A, Gulshan, V, Varma, M, Zisserman, A, IEEE   +4 more
openaire   +3 more sources