Parallel Vision Algorithms for Shared-Memory and Pipeline Multiprocessors

视觉机器必须支持对大量时变图像数据进行广泛的复杂操作，并以高速执行所有操作。本研究的目标是设计、实现和评估在两个紧密耦合的多处理器上同时执行的动态、协作、并行视觉算法：一个线性连接的8处理器Aspex PIPE和一个10处理器共享内存的sequence Balance。这种两级组织对应于计算机视觉所需的两种处理。首先，将为PIPE开发低级视觉算法，检测视频速率图像序列中的原始“标记”。第二种，高级视觉，需要更多的符号和几何处理（例如，在基于模型的对象识别中），并将为后续开发。研究活动将包括开发在两台机器上协调多层图像分析的方法，在共享内存多处理器上实现高级视觉的动态负载平衡技术，以及多分辨率图像分析和基于模型识别的并行算法。​​​//

A vision machine must support a wide range of complex operations on massive amounts of time-varying image data, and execute all of the operations at high speeds. The goal of this research is to design, implement, and evaluate dynamic, cooperative, parallel vision algorithms which execute simultaneously on two tightly-coupled multiprocessors: a linearly-connected, 8-processor Aspex PIPE, and a 10-processor, shared-memory Sequent Balance. This two-level organization corresponds to the two kinds of processing required in computer vision. In the first, low-level vision algorithms will be developed for the PIPE, detecting primitive "tokens" in a video-rate sequence of images. The second, high-level vision, requires more symbolic and geometric processing (e.g., in model-based object recognition), and will be developed for the Sequent. Research activities will include the development of methods for coordinating multiple levels of image analysis on the two machines, dynamic load- balancing techniques for high-level vision on the shared-memory multiprocessor, and parallel algorithms for multiresolution image analysis and model-based recognition. //