The Perceptual Identification and Representation of Image Contours

图像轮廓的感知识别与表示

• 批准号: 0962119
• 负责人: James Todd
• 金额: $ 43.61万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0962119&HistoricalAwards=false
• 关键词: Perceptual; Identification; Representation; Image; Contours

It has long been recognized that a convincing pictorial representation of an object can sometimes be achieved by drawing just a few salient contours in an image. This phenomenon is really quite remarkable, given that a line drawing effectively strips away almost all of the variations in color and shading that are ordinarily available in natural scenes. Somehow the artists who create such drawings are able to capture the essential information for perceptual recognition with just a few simple strokes. Although a well structured line drawing is easily interpreted by human observers, the ability to create these drawings can require considerable artistic skill. Indeed, despite almost a half century of research in the field of computer vision, there are no existing algorithms that can duplicate the performance of a competent human artist. In this project, Dr. James Todd and his students at the Ohio State University will investigate how human observers perceptually identify different types of image contours, such as shadows, corners or occlusion. The group will also examine which contours in an image are perceptually most important for creating pictorial representations of objects. The stimuli in these studies will include drawings by artists with varying amounts of training, who will be asked to produce line drawings of objects with known 3D structures. The drawings will be ranked by human observers to assess their relative perceptual effectiveness. The contours in the drawings will also be compared with different aspects of the depicted surface geometry in order to determine which specific aspects of a surface are most important for its pictorial depiction. A better understanding of how human observers perceptually determine the 3D shapes of surfaces from 2D image data has many possible applications, including the design of more robust and effective algorithms in machine vision, improved techniques for 3D visualization in computer graphics and design, and the potential development of more functional prosthetic devices for the blind. This work may also have a significant impact on how students are taught to draw in art or design courses.

长期以来，人们已经认识到，有时可以通过在图像中绘制一些显着轮廓来实现对象的令人信服的绘画表示。鉴于一条线绘图有效地剥离了几乎所有颜色和阴影的变化，这些现象确实非常出色。创建此类图纸的艺术家以某种方式只能通过一些简单的笔触来捕获感知识别的基本信息。 尽管人类观察者很容易解释结构良好的线图，但创建这些图纸的能力可能需要相当大的艺术技能。 确实，尽管在计算机视觉领域进行了将近半个世纪的研究，但没有现有的算法可以复制一个有能力的人类艺术家的表现。在这个项目中，詹姆斯·托德（James Todd）博士及其学生在俄亥俄州立大学（Ohio State University）将调查人类观察者在感知上如何识别不同类型的图像轮廓，例如阴影，角落或遮挡。该组还将检查图像中的哪些轮廓对于创建对象的图形表示最重要。这些研究的刺激将包括具有不同培训数量的艺术家的图纸，他们将被要求制作具有已知3D结构的物体的线图。这些图纸将由人类观察者进行排名，以评估其相对感知的有效性。图纸中的轮廓也将与所描绘的表面几何形状的不同方面进行比较，以确定表面的哪个特定方面对于其图形描述最重要。更好地了解人类观察者如何感知地确定来自2D图像数据的表面的3D形状具有许多可能的应用，包括设计机器视觉中更强大，更有效的算法，改进的计算机图形图和设计中3D可视化的技术，以及对盲人的更多功能性提案的潜在开发。 这项工作也可能对教导学生学习艺术或设计课程有重大影响。