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Discerning structure from freeform sketches

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discerning structure from freeform sketches
作者:	Shilman, Michael M.
面頁冊數:	183 p.
附註:	Chair: A. Richard Newton.
附註:	Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0849.
Contained By:	Dissertation Abstracts International65-02B.
標題:	Computer Science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3121695
ISBN:	0496690469

Discerning structure from freeform sketches
Shilman, Michael M.

Discerning structure from freeform sketches [electronic resource] - 183 p.

Chair: A. Richard Newton.

Thesis (Ph.D.)--University of California, Berkeley, 2003.

Each of these techniques is built on a general recognition architecture that uses a MultiTree protocol to compactly represent multiple tree hypotheses. The protocol enables functional and concurrent composition of off-the-shelf recognizers. The techniques are evaluated using different tree edit distance metrics.

ISBN: 0496690469Subjects--Topical Terms:

212513
Computer Science.

Discerning structure from freeform sketches
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520 # $a Each of these techniques is built on a general recognition architecture that uses a MultiTree protocol to compactly represent multiple tree hypotheses. The protocol enables functional and concurrent composition of off-the-shelf recognizers. The techniques are evaluated using different tree edit distance metrics.
520 # $a For document annotations, I present a style-independent recognition algorithm for common annotation markup structures. The algorithm combines minimal temporal assumptions with spatial statistics to yield accurate results. Among other things, the results of this recognition can be used to reflow annotation markup as the underlying document layout changes.
520 # $a For note-taking, I present an algorithm to distinguish handwriting from drawings in arbitrary, freeform notes. The algorithm is language-independent, depends only minimally on writing order, and detects writing at any angle on the page.
520 # $a For sketched diagrams, I present a general recognition framework based on context-free visual language grammars and statistical language parsing. The approach is evaluated on a language for user interface design and generalizes to a variety of inking scenarios.
520 # $a These claims are validated by considering three separate scenarios in detail: note-taking in meetings, annotating digital documents with markup, and sketching diagrams. For each of these common tasks, recognition should be flexible enough to incorporate the writing and drawing styles of an individual user, yet accurate enough to satisfy the scenarios for which it was intended. To this end, I have developed several algorithms that attempt to satisfy these constraints.
520 # $a This work explores user interfaces and algorithms for recognizing freeform sketching in digital ink. Like other recognition work before it, it is based on the premise that an increased understanding of the ink results in increased utility to the user. Interpreted digital ink is preferable to physical ink for its archival, search, editing, and simulation capabilities and its relative ease of integration with existing computer applications. However, unlike previous recognition work it also posits that a computer should never obstruct the user's thought capture in creative scenarios. In other words, the primary goal of a pen computer should be to unobtrusively capture freeform digital ink; the user should not be concerned with how the ink will be interpreted.
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