A Laboratory for Computer Vision and Image Processing

这个仪器奖是用于启动实验室的设备 计算机视觉和图像处理。 的研究项目 包括人脸识别、图像代数和数字 用于端到端系统的图像处理。 人脸识别 问题涉及研究检测，识别， 分类和人脸分析。 图像代数 研究的目的是开发必要的有效实施， 执行普通图像处理所需的代数运算 任务，如放大、缩小和连接的组件 标签。 端到端系统中的数字处理包括 必要的算法，以获得高质量的图像， 存在来自组件（例如透镜和传输）的噪声 线 视觉研究包括一系列广泛的任务。 这 研究项目解决了三个这样的任务：面部识别， 图像代数和端到端系统中的数字图像处理。 人脸识别是一个重要的安全问题 应用. 这是人类特别擅长的任务 但事实证明它很难计算。 图像代数（Image Algebra） 在定义视觉处理任务的通用操作时很有用。 这些任务包括放大、旋转和标记 图像区域。 这些操作的实现应该 尽可能高效。 使用数字图像处理 技术涉及使用许多设备， 噪声或受到测量误差的影响。 其中包括 测量图像强度和颜色的设备，任何 镜头参与图像的捕获和传输 线 每一个都可以单独处理，但适当的 治疗是考虑系统作为一个整体，以获得 最高质量的图像。

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.