Workshop on Shape, Contour and Grouping

形状、轮廓和分组研讨会

• 批准号: 9712426
• 负责人: David Forsyth
• 金额: $ 1万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9712426&HistoricalAwards=false
• 关键词: Workshop; Shape; Contour; Grouping

A new application for object recognition, indexing image sets in digital libraries, demands recognition strategies that are (1) effective in cluttered scenes and (2) effective for objects recognised at a class level - for example, people or animals. These needs can be met by recognition algorithms that interpret the world in terms of stylised properties in shape, shading, texture and colour, which are caled 'primitives.' In this approach, objects are represented by a collection of spatial relationships between primitives. The primitives themselves are used as a source of grouping constraints. Recognition proceeds by grouping together features or regions that could represent a primitive, and then assembling groups of primitives that could have come from an object. There is no explicit 3D reasoning, but the grouping processes may contain a great deal of implicit 3D reasoning. This approach to shape and to recognition unites themes studied in active research programs in computer vision, both in the United States and in the European Community. A joint workshop, involving both US and E.C. participants (for whom E.C. funding will be available), will explore representations that use grouping and symmetry as a recognition strategy. Topics will include: shading primitives, image shape constraints that result from volumetric primitives, building grouping-based representations and overall strategies for object recognition. The workshop will be held in Erice, Sicily, in early 1998. The proceedings will be published as a volume in the Springer Lecture Notes in Computer Science series.

对象识别的一个新应用，索引数字图书馆中的图像集，要求识别策略（1）在混乱的场景中有效，（2）对在类级别识别的对象有效-例如，人或动物。 这些需求可以通过识别算法来满足，这些算法根据形状、阴影、纹理和颜色中的风格化属性来解释世界，这些属性是标度的“基元”。' 在这种方法中，对象由图元之间的空间关系的集合表示。 基元本身用作分组约束的来源。 识别过程是将可能代表基元的特征或区域分组，然后将可能来自对象的基元组组合在一起。 没有明确的3D推理，但分组过程可能包含大量的隐式3D推理。 这种方法的形状和识别团结在积极的研究计划在计算机视觉，无论是在美国和欧洲共同体的主题。 美国和欧洲共同体参加的联合讲习班。参与者（对于E.C. 资金将可用），将探索使用分组和对称作为识别策略的表示。 主题将包括：着色图元，图像形状的限制， 体积 基元， 建筑 基于分组的表示和对象识别的总体策略。 讲习班将于1998年初在西西里的埃里切举行。 会议记录将作为计算机科学系列中的Springer讲义的一卷出版。