【6h】

Simulation of early vision mechanisms and application to object shape recognition

机译:早期视觉机制的仿真及其在物体形状识别中的应用

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摘要

In early stages of vision, the images are processed to generate "maps" or point-by point distributions of values of various quantities including the edge elements, fields of local motion, depth maps and color constancy, etc. These features are then refined and processed in visual cortex. The next stage is recognition which also leads to simple control of behaviors such as steering and obstacle avoidance, etc. In this paper we present a system for object shape recognition that utilizes the features extracted by use of human vision model. The first block of the system performs processing analogous to that in retina for edge feature extraction. The second block represents the processing in visual cortex, where features are refined and combined to form a stimulus to be presented to the recognition model. We use the normalized distances of the edge pixels from the mean to form a feature vector. The next block that accomplishes the task of recognition consists of a counterpropagation neural network model. We use gray scale images of 3D objects to train and test the performance of the system. The experiments show that the system can recognize the objects with some variations in rotation, scaling and translation.
机译:在视觉的早期阶段,对图像进行处理以生成“图”或各种量值的逐点分布,包括边缘元素,局部运动场,深度图和颜色恒定性等。然后对这些特征进行完善和在视觉皮层中处理。下一阶段是识别,这也将导致对行为的简单控制,例如转向和避障等。在本文中,我们提出了一种利用人类视觉模型提取的特征的物体形状识别系统。系统的第一块执行类似于视网膜中边缘特征提取的处理。第二个块表示视觉皮层中的处理,在此过程中对特征进行完善和组合以形成要呈现给识别模型的刺激。我们使用边缘像素到均值的标准化距离来形成特征向量。完成识别任务的下一个模块由反向传播神经网络模型组成。我们使用3D对象的灰度图像来训练和测试系统的性能。实验表明,该系统可以识别出旋转,缩放和平移有些变化的对象。

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