Efficient correspondence search algorithm for GOBO projection-based real-time 3D measurement

Author(s):  
Patrick Dietrich ◽  
Stefan Heist ◽  
Peter Lutzke ◽  
Martin Landmann ◽  
Pascal Grosmann ◽  
...  
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.


2010 ◽  
Vol 5 (1) ◽  
pp. 78-88 ◽  
Author(s):  
Marcelo Porto ◽  
André Silva ◽  
Sergo Almeida ◽  
Eduardo Da Costa ◽  
Sergio Bampi

This paper presents real time HDTV (High Definition Television) architecture for Motion Estimation (ME) using efficient adder compressors. The architecture is based on the Quarter Sub-sampled Diamond Search algorithm (QSDS) with Dynamic Iteration Control (DIC) algorithm. The main characteristic of the proposed architecture is the large amount of Processing Units (PUs) that are used to calculate the SAD (Sum of Absolute Difference) metric. The internal structures of the PUs are composed by a large number of addition operations to calculate the SADs. In this paper, efficient 4-2 and 8-2 adder compressors are used in the PUs architecture to achieve the performance to work with HDTV (High Definition Television) videos in real time at 30 frames per second. These adder compressors enable the simultaneous addition of 4 and 8 operands respectively. The PUs, using adder compressors, were applied to the ME architecture. The implemented architecture was described in VHDL and synthesized to FPGA and, with Leonardo Spectrum tool, to the TSMC 0.18μm CMOS standard cell technology. Synthesis results indicate that the new QSDS-DIC architecture reach the best performance result and enable gains of 12% in terms of processing rate. The architecture can reach real time for full HDTV (1920x1080 pixels) in the worst case processing 65 frames per second, and it can process 269 HDTV frames per second in the average case.


1987 ◽  
Vol 87 (1) ◽  
pp. 171-182
Author(s):  
J.A. Dow ◽  
J.M. Lackie ◽  
K.V. Crocket

An image analysis package based on a BBC microcomputer has been developed, which can simultaneously track many moving cells in vitro. Cells (rabbit neutrophil leucocytes, BHK C13 fibroblasts, or PC12 phaeochromocytoma cells) are viewed under phase optics with a monochrome TV camera, and the signal digitized. Successive frames are acquired by the computer as a 640 X 256 pixel array. Under controlled lighting conditions, cells can readily be isolated from the background by binary filtering. In real-time tracking, the positions of a given cell in successive frames are obtained by searching the area around the cell's centroid in the previous frame. A simple box-search algorithm is described, which proves highly successful at low cell densities. The resilience of different search algorithms to various exceptional conditions (such as collisions) is discussed. The success of this system in real-time tracking is largely dependent upon the leisurely speed of movement of cells, and on obtaining a clean, high quality optical image to analyse. The limitations of this technique for different cell types, and the possible configurations of more sophisticated hardware, are outlined. This system provides a versatile and automated solution to the problem of studying the movement of tissue cells.


2021 ◽  
Vol 60 (03) ◽  
Author(s):  
Hechen Zhang ◽  
Yiping Cao ◽  
Chengmeng Li ◽  
Lu Wang ◽  
Hongmei Li ◽  
...  

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