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AbstractThe field of computer vision studies how computers can gain understanding from images and videos, similar to human cognitive abilities. One of the classical challenges is to reconstruct a 3D object from images taken by several unknown cameras.
Md Atiqur Rahman Ahad +3 more
core +7 more sources
International Journal for Research in Applied Science and Engineering Technology, 2021 Computer vision may be a field of computer science that trains computers to interpret and perceive the visual world. exploitation digital pictures from cameras and videos and deep learning models, machines will accurately determine and classify objects — and so react to what they "see.”.
Rajesh Singh +3 more
+9 more sources
IET Computer Vision, 2022 Despite recent advances in 3‐D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups.
Meysam Madadi +3 more
doaj +1 more source
The Programming Historian, 2022 This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model.
Daniel van Strien +4 more
doaj +1 more source
QUANTITATIVE COMPARISON BETWEEN NEURAL NETWORK- AND SGM-BASED STEREO MATCHING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Over the last decades, various methods for three-dimensional detection of the environment have been developed and successfully used. This work considers classical stereo methods, which can determine depth information by the means of correspondence ...
A. Frenzel, N. Deckers, R. Reulke
doaj +1 more source
ROBUST AERIAL OBJECT TRACKING IN HIGH DYNAMIC FLIGHT MANEUVERS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Integrating drones into the civil airspace is one of the biggest challenges for civil aviation, responsible authorities and involved com- panies around the world in the upcoming years. For a full integration into non-segregated airspace such a system has
A. Nussberger, H. Grabner, L. van Gool
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Sim-to-Real Transfer for Object Detection in Aerial Inspections of Transmission Towers
IEEE Access, 2023 Training deep learning models for object detection usually requires a large amount of data, a condition that is not common for most real-world applications, especially in the context of aerial imagery.
Augusto J. Peterlevitz +15 more
doaj +1 more source
A Convolutional Neural Network for Nonrigid Structure from Motion
International Journal of Digital Multimedia Broadcasting, 2022 In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network.
Yaming Wang +3 more
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Automatic evaluation of practices in Moodle for Self Learning in Engineering
Journal of Technology and Science Education, 2015 The first years in engineering degree courses are usually made of large groups with a low teacher-student ratio. Overcrowding in classrooms hinders continuous assessment much needed to promote independent learning.
Carles Sanchez +5 more
doaj +1 more source
Are object detection assessment criteria ready for maritime computer vision? [PDF]
, 2019 Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently.
Dong, Huixu +3 more
core +2 more sources