Edge Detection and Surface Recognition, U.S.-Turkey Collaborative Research

描述：该项目支持Anil K博士的合作。Jain，密歇根州立大学计算机科学系，东兰辛，Muhittin Gokmen博士，伊斯坦布尔技术大学计算机科学系，伊斯坦布尔，土耳其。 研究涉及图像表示，边缘检测和表面重建领域的正则化理论。 正则化是解决不适定早期视觉问题的一般框架。 通过正则化获得的图像或表面的Lambda-Tau（L-T）表示提供了比广泛用于解决视觉问题的尺度空间表示更大的优势。 L-T表示，来自混合能量泛函，采样图像在连续函数的尺度空间和Sobolev空间。 在这个空间中表示的边和曲面具有不同的平滑度和连续性，分别由参数L和T控制。 研究的主要重点将集中在以下主题：L-T表示的分析，以及它在解决早期视觉问题中的应用;广义边缘检测器的开发，该检测器可以在统一的框架下包含大多数现有的边缘算子;以及在L-T空间中表示的表面的重建。 适用范围： 该项目汇集了两个在该领域具有出色研究能力的团队，并在一个重要领域进行互补的专业合作。 Jain博士在密歇根州立大学的模式识别和图像处理（PRIP）实验室发表了大量关于处理范围数据，表面重建和3-D对象识别的主题。 Gokmen博士的研究小组在正则化理论、边缘检测、表面重建和物体识别方面拥有丰富的专业知识。 预计这一合作将对两个集团有利。 该项目符合国际方案司的目标。

Description: This project supports collaboration by Dr. Anil K. Jain, Department of Computer Science, Michigan state University, East Lansing, and Dr. Muhittin Gokmen, Department of Computer Science, Istanbul Technical University, Istanbul, Turkey. The research deals with the area of image representation, edge detection, and surface reconstruction using regularization theory. Regularization is a general framework to solve ill-posed early vision problems. The Lambda-Tau (L-T) representation of images or surfaces obtained by means of regularization provides additional advantages over the scale-space representation which is widely used to solve vision problems. The L-T representation, derived from a hybrid energy functional, samples an image in both scale-space and Sobolev space of continuous functions. Edges and surfaces represented in this space have different levels of smoothness and continuity, controlled by parameters L and T, respectively. The main focus of the research will be on the following topics: the analysis of the L-T representation, and its use in solving early vision problems; the development of a generalized edge detector which can encompass most of the existing edge operators under a unified framework; and reconstruction of surfaces represented in the L-T space. Scope: This project brings together two teams with excellent capabilities in research in the field, and with complementary expertise to collaborate in an important area. Dr. Jain's Pattern Recognition and Image Processing (PRIP) Laboratory at MSU has published extensively on topics dealing with range data, surface reconstruction, and 3-D object recognition. Dr. Gokmen's research group has substantial expertise in regularization theory, edge detection, surface reconstruction and object recognition. The collaboration is expected to be beneficial to the two groups. This project meets the objectives of the Division of International Programs.