RI: Detecting Boundaries for Segmentation and Recognition

形状是一种强大的视觉线索，用于识别图像中的对象，将图像分割成与各个对象对应的区域，以及更一般地理解场景的3D结构。然而，为了能够利用形状信息，我们需要可靠的方法来检测对象边界的碎片，这本身就是一个难题。本项目研究解决这些问题的可能办法。具体来说，它探讨了可靠地检测遮挡边界的方法，通过关注结合运动线索与外观线索的问题，以更好地检测遮挡边界。此外，该项目还通过研究分割和类别识别中边界信息的整合，探索在关键视觉任务中使用边界的不同方式。该项目预计将在两个领域取得进展：1）从图像中检测边界信息作为形状线索; 2）将边界信息用于分割和识别任务。在第一个方面，它扩展了目前的轮廓检测方法，包括运动线索。这是由在许多实际应用中的时间信息的可用性。假设我们可以提取边界片段，在分割和识别中有效地使用它们的问题仍然是一个悬而未决的问题。该项目将为回答这个问题做出重大贡献，包括将边界信息与图像分割和识别算法相结合。 该项目将带来与广泛应用直接相关的进展。特别是，从图像序列中可靠地检测边界的能力在涉及分析视频以进行对象发现、识别和分割的所有应用中至关重要。该项目还将有助于开发基于形状表示的更可靠的类别识别算法，这是一项广泛领域的技术，包括国防，医疗保健，人机交互，图像检索和数据挖掘，工业和个人机器人，制造业，科学图像分析，监控和安全以及运输。虽然该项目主要侧重于自动分割和识别，但它也将有助于人工辅助分割和图像编辑，随着消费者视频和图像编辑工具的出现，这一技术领域变得越来越重要。该项目的另一个预期产品是生成标记数据的语料库，包括运动数据。http://www.cs.cmu.edu/~hebert/boundaries/

Shape is a powerful visual cue for recognizing objects in images, segmenting images into regions corresponding to individual objects, and, more generally, understanding the 3D structures of scenes. However, to be able to exploit shape information, we need reliable ways of detecting fragments of object boundaries, a difficult problem in itself. This project investigates possible solutions to these problems. Specifically, it explores ways to reliably detect occluding boundaries by focusing on the question of combining motion cues with appearance cues for better detection of occlusion boundaries. In addition, the project explores different ways in which boundaries can be used in key vision tasks by investigating the integration of boundary information in segmentation and category recognition.The project is expected to generate advances in two areas: 1) detecting boundary information from images as shape cues and 2) using boundary information in segmentation and recognition tasks. In the first area, it extends the current approaches for contour detection to include motion cues. This is motivated by the availability of temporal information in many practical applications. Assuming that we can extract boundary fragments, the issue of using them effectively in segmentation and recognition remains an open question. The project will make substantial contributions toward answering that question, including integrating boundary information with image segmentation and recognition algorithms. The project will result in advances that are directly relevant to a wide range of applications. In particular, the ability to reliably detect boundaries from image sequences is crucial in all applications that involve the analysis of videos for object discovery, recognition, and segmentation. The project is also expected to contribute to the development of more reliable category recognition algorithms based on shape representations, an enabling technology in a wide variety of fields including defense, health care, human-computer interaction, image retrieval and data mining, industrial and personal robotics, manufacturing, scientific image analysis, surveillance and security, and transportation. Although the project focuses primarily on automatic segmentation and recognition, it will also contribute to human-assisted segmentation and image editing, a technology area that is becoming increasingly important with the advent of consumer video and image editing tools. An additional expected product of the project is the generation of a corpus of labeled data, including motion data.URL: http://www.cs.cmu.edu/~hebert/boundaries/