Integration of visual modules: an extension of the Marr paradigm
Integration of visual modules: an extension of the Marr paradigm
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International Journal of Computer Vision
International Journal of Computer Vision
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Computer architecture (2nd ed.): design and performance
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Multiple view geometry in computer vision
Signal Processing for Computer Vision
Signal Processing for Computer Vision
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International Journal of Computer Vision
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Pattern Recognition Letters
Early Cognitive Vision: Using Gestalt-Laws for Task-Dependent, Active Image-Processing
Natural Computing: an international journal
Matched Filter Computation on FPGA, Cell and GPU
FCCM '07 Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
A Real-Time Embedded System for Stereo Vision Preprocessing Using an FPGA
RECONFIG '08 Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs
A Hybrid FPGA/Coarse Parallel Processing Architecture for Multi-modal Visual Feature Descriptors
RECONFIG '08 Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs
Continuous dimensionality characterization of image structures
Image and Vision Computing
Accelerating Compute-Intensive Applications with GPUs and FPGAs
SASP '08 Proceedings of the 2008 Symposium on Application Specific Processors
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IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
A phase-based approach to the estimation of the optical flow field using spatial filtering
IEEE Transactions on Neural Networks
Low-cost FPGA stereo vision system for real time disparity maps calculation
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Adaptive Census Transform: A novel hardware-oriented stereovision algorithm
Computer Vision and Image Understanding
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In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas with independently moving objects as well as a condensed geometric description of the scene. The system operates at more than 20聽Hz using a hybrid architecture consisting of one dual-GPU card and one quad-core CPU. The different processing stages of visual information have rather different characteristics that in some cases make fine-grained parallelization on a GPU less applicable. However, for most of the stages that are not efficiently implementable on a GPU, a coarse parallelization on multiple CPU-cores is applicable. We show that with such hybrid parallelism, we can achieve a speed up of approximately a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU-core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications.