A Laboratory for Computer Vision and Image Processing

该仪器奖是用于启动计算机视觉和图像处理实验室的设备。研究项目包括人脸识别、图像代数和端到端系统的数字图像处理。人脸识别问题涉及对人脸的检测、识别、分类和分析等方面的研究。图像代数研究的目的是开发必要的代数操作的有效实现，以执行常见的图像处理任务，如放大，缩小和连接组件标记。端到端系统中的数字处理由必要的算法组成，以在透镜和传输线等组件存在噪声的情况下获得高质量的图像。视觉研究包含了一系列广泛的任务。本研究项目涉及三个这样的任务；端到端系统中的人脸识别、图像代数和数字图像处理。人脸识别是安全应用中的一个重要问题。这是一项人类特别擅长的任务，但已被证明很难用计算机化。图像代数的工作在定义视觉处理任务的通用操作时很有用。这些任务包括图像区域的放大、旋转和标记。这些操作的实现应该尽可能高效。使用数字技术处理图像涉及使用许多可能引入噪声或受到测量误差的设备。这些设备包括测量图像强度和颜色的设备、用于捕获图像的任何镜头和传输线。这些都可以单独处理，但适当的处理是将系统作为一个整体来考虑，作为获得最高质量图像的一种手段。

This instrumentation award is for equipment to start a laboratory in computer vision and image processing. The research projects include projects on face recognition, image algebra, and digital image processing for end-to-end systems. The face recognition problem involves studying issues in detection, identification, classification, and analysis of human faces. The image algebra research aims to develop the necessary efficient implementations of algebraic operations necessary to perform common image processing tasks such as magnification, shrinking, and connected component labeling. The digital processing in end-to-end systems consists of the necessary algorithms to obtain high quality images in the presence of noise form components such as lens and transmission lines. Research in vision encompasses a broad array of tasks. This research project addresses three such tasks; face recognition, image algebra, and digital image processing in end-to-end systems. Face recognition is an important problem with security applications. It is a task that humans are particularly good at and yet has proven very hard to computerize. Work in image algebra is useful in defining operations common to visual processing tasks. Such tasks include the magnification, rotation, and labeling of image regions. The implementations of these operations should be as efficient as possible. The processing of images using digital techniques involves the use of many devices that may introduce noise or be subject to measurement errors. These include the device that measures the intensity and color of the image, any lenses involved in the capture of the image, and transmission lines. Each of these can be treated separately, but the proper treatment is to consider the system as a whole as a means to obtain the highest quality images.