scholarly journals Understanding Object Dynamics for Interactive Image-to-Video Synthesis

2021 ◽  
Author(s):  
Andreas Blattmann ◽  
Timo Milbich ◽  
Michael Dorkenwald ◽  
Bjorn Ommer
Keyword(s):  
Object Dynamics

scholarly journals Object-Based Approach for Adaptive Source Coding of Surveillance Video

2019 ◽  
Vol 9 (10) ◽  
pp. 2003 ◽  
Author(s):  
Tung-Ming Pan ◽  
Kuo-Chin Fan ◽  
Yuan-Kai Wang
Keyword(s):  
Linear Model ◽  
Video Transmission ◽  
Moving Objects ◽  
Source Coding ◽  
Video Quality ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Object Based ◽  
High Recognition Accuracy ◽  
Object Dynamics

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.

scholarly journals Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020 ◽  
Vol 12 (18) ◽  
pp. 3053 ◽  
Author(s):  
Thorsten Hoeser ◽  
Felix Bachofer ◽  
Claudia Kuenzer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Land Surface ◽  
Large Scale ◽  
Earth Observation ◽  
Observation Data ◽  
Fine Grained ◽  
The Impact ◽  
Object Dynamics

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

Learning object dynamics for smooth tracking of moving lip contours

2000 ◽  
Vol 36 (6) ◽  
pp. 520 ◽  
Author(s):  
T. Wark ◽  
S. Sridharan ◽  
V. Chandran
Keyword(s):  
Learning Object ◽  
Smooth Tracking ◽  
Object Dynamics

scholarly journals Hand Interactions in Rapid Grip Force Adjustments Are Independent of Object Dynamics

2008 ◽  
Vol 100 (5) ◽  
pp. 2738-2745 ◽  
Author(s):  
Olivier White ◽  
Noreen Dowling ◽  
R. Martyn Bracewell ◽  
Jörn Diedrichsen
Keyword(s):  
Grip Force ◽  
Object Manipulation ◽  
Load Force ◽  
Single Object ◽  
Stereo Display ◽  
Force Increase ◽  
Robotic Devices ◽  
The Moment ◽  
Pure Measurement ◽  
Object Dynamics

Object manipulation requires rapid increase in grip force to prevent slippage when the load force of the object suddenly increases. Previous experiments have shown that grip force reactions interact between the hands when holding a single object. Here we test whether this interaction is modulated by the object dynamics experienced before the perturbation of the load force. We hypothesized that coupling of grip forces should be stronger when holding a single object than when holding separate objects. We measured the grip force reactions elicited by unpredictable load perturbations when participants were instructed to hold one single or two separate objects. We simulated these objects both visually and dynamically using a virtual environment consisting of two robotic devices and a calibrated stereo display. In contrast to previous studies, the load forces arising from a single object could be uncoupled at the moment of perturbation, allowing for a pure measurement of grip force coupling. Participants increased grip forces rapidly (onset ∼70 ms) in response to perturbations. Grip force increases were stronger when the load force on the other hand also increased. No such coupling was present in the reaction of the arms to the load force increase. Surprisingly, however, the grip force interaction did not depend on the nature of the manipulated object. These results show fast obligatory coupling of bimanual grip force responses. Although this coupling may play a functional role for providing stability in bimanual object manipulation, it seems to constitute a relatively hard-wired modulation of a reflex.

scholarly journals ELEMENTS JOINT IMPACT UPON DYNAMIC PROPERTIES OF OSCILLATING SYSTEMS IN PROBLEMS OF ENGINEERING OBJECT DYNAMICS

2017 ◽  
Vol 2017 (4) ◽  
pp. 30-40
Author(s):  
Алексей Орленко ◽  
Aleksey Orlenko ◽  
Сергей Елисеев ◽  
Sergey Eliseev ◽  
Андрей Елисеев ◽  
...  
Keyword(s):  
Dynamic Properties ◽  
Oscillating Systems ◽  
Object Dynamics

scholarly journals Visual cues, expectations, and sensorimotor memories in the prediction and perception of object dynamics during manipulation

2020 ◽  
Vol 238 (2) ◽  
pp. 395-409 ◽  
Author(s):  
Thomas Rudolf Schneider ◽  
Gavin Buckingham ◽  
Joachim Hermsdörfer
Keyword(s):  
Visual Cues ◽  
Object Dynamics
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