Results 211 to 220 of about 474,599 (267)
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The Visual Computer, 2021
Image dehazing aims to remove the haze noise and restore the image content from hazy images. It is a challenging task because of the unbalanced distribution of the haze noise and the variety of the image contents. Most existing methods apply convolutional neural networks to learn the dehazing process by blind end-to-end training, which relies on the ...
Fei Yang, Qian Zhang
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Image dehazing aims to remove the haze noise and restore the image content from hazy images. It is a challenging task because of the unbalanced distribution of the haze noise and the variety of the image contents. Most existing methods apply convolutional neural networks to learn the dehazing process by blind end-to-end training, which relies on the ...
Fei Yang, Qian Zhang
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Object discovery in depth images
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016We present an unsupervised method for discovering objects from depth information. Our method can identify new common objects appearing in different depth images. We use 2D bounding box proposals to detect candidate locations of objects in each depth image, and then retrieve the corresponding 3D bounding boxes using the depth information. Invalid object
Tzu-Wei Huang +3 more
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Proceedings., IEEE International Conference on Robotics and Automation, 2002
Motion relative to a surface is addressed. Image flow can be caused either by motion of objects in the world or by motion of the eye through the world. If knowledge exists of eye motion, dense range maps can be computed locally for all stationary object pixels.
James S. Albus, Tsai Hong
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Motion relative to a surface is addressed. Image flow can be caused either by motion of objects in the world or by motion of the eye through the world. If knowledge exists of eye motion, dense range maps can be computed locally for all stationary object pixels.
James S. Albus, Tsai Hong
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Imaged Depth Versus True Depth
Proceedings, 2015Summary The use of the term ‘velocity’ to describe both the true propagation velocity and the modelling “velocity” used in processing (what is here termed ‘pro-velocity’) has given rise to a damaging level of confusion whereby a great majority of geoscientists take the depth image to be a good representation of depth in the subsurface. The relationship
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Parsing the Hand in Depth Images
IEEE Transactions on Multimedia, 2014Hand pose tracking and gesture recognition are useful for human-computer interaction, while a major problem is the lack of discriminative features for compact hand representation. We present a robust hand parsing scheme to extract a high-level description of the hand from the depth image.
Hui Liang 0003 +2 more
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Depth-resolved Lensless Imaging
Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP), 2018A numerical approach is developed to reconstruct 3D images from a set of wavelengthand phase-resolved diffraction patterns, resulting in a computational depth-resolved imaging method.
Du, M., Eikema, K. S.E., Witte, S.
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Robust enhancement of depth images from depth sensors
Computers & Graphics, 2017In recent years, depth cameras (such as Microsoft Kinect and ToF cameras) have gained much popularity in computer graphics, visual computing and virtual reality communities due to their low price and easy availability. While depth cameras (e.g. Microsoft Kinect) provide RGB images along with real-time depth information at high frame rate, the depth ...
A. B. M. Tariqul Islam +3 more
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Kinetic depth images: flexible generation of depth perception
The Visual Computer, 2016In this paper we present a systematic approach to create smoothly varying images from a pair of photographs to facilitate enhanced awareness of the depth structure of a given scene. Since our system does not rely on sophisticated display technologies such as stereoscopy or auto-stereoscopy for depth awareness, it (a) is inexpensive and widely ...
Sujal Bista +2 more
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2019
As robots are developed for large-scale applications in autonomous driving, package delivery, and agriculture, there is a growing need for affordable and reliable depth sensing. Robots use active illumination sensors like scanning LIDAR and depth cameras to perceive their worlds. Scanning LIDAR is prevalent because it offers long-range, robust sensing,
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As robots are developed for large-scale applications in autonomous driving, package delivery, and agriculture, there is a growing need for affordable and reliable depth sensing. Robots use active illumination sensors like scanning LIDAR and depth cameras to perceive their worlds. Scanning LIDAR is prevalent because it offers long-range, robust sensing,
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2012
This thesis depicts approaches toward real-time depth sensing. While humans are very good at estimating distances and hence are able to smoothly control vehicles and their own movements, machines often lack the ability to sense their environment in a manner comparable to humans. This discrepancy prevents the automation of certain job steps.
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This thesis depicts approaches toward real-time depth sensing. While humans are very good at estimating distances and hence are able to smoothly control vehicles and their own movements, machines often lack the ability to sense their environment in a manner comparable to humans. This discrepancy prevents the automation of certain job steps.
openaire +4 more sources