Open Access
Learning Three-Dimensional Shape Models for Sketch Recognition
TLDR
This paper focuses on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation, and demonstrates how to use models learned in three dimensions for recognition of two-dimensional sketches of.Abstract:
Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches ofread more
Citations
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Computer vision : a modern approach = 计算机视觉 : 一种现代的方法
David Forsyth,Jean Ponce +1 more
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Proceedings Article
Robot vision
TL;DR: A scheme is developed for classifying the types of motion perceived by a humanlike robot and equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented.
Proceedings ArticleDOI
Constellation models for sketch recognition
Dana Sharon,M. van de Panne +1 more
TL;DR: This work draws on constellation models first proposed in the computer vision literature to develop probabilistic models for object sketches, based on multiple example drawings, which are applied to estimate the most-likely labels for a new sketch.
Book ChapterDOI
Flexible Parts-based Sketch Recognition
Michiel van de Panne,Dana Sharon +1 more
TL;DR: This chapter proposes two template-based methods for sketch recognition that employ a hierarchy-of-parts template model that provides explicit support for templates with optional parts and captures significant parts-based variation which would otherwise require a multitude of fixed-structure templates to model.
References
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Journal ArticleDOI
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Book
Robot Vision
TL;DR: Robot Vision as discussed by the authors is a broad overview of the field of computer vision, using a consistent notation based on a detailed understanding of the image formation process, which can provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.
Computer vision : a modern approach = 计算机视觉 : 一种现代的方法
David Forsyth,Jean Ponce +1 more
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Book
Computer Vision: A Modern Approach
David Forsyth,Jean Ponce +1 more
TL;DR: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications as discussed by the authors, which includes essential topics that either reflect practical significance or are of theoretical importance.
Proceedings ArticleDOI
Object class recognition by unsupervised scale-invariant learning
TL;DR: The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).