Recognition of dimension sets and integration with vectorized engineering drawings

Das, Atish K.; Langrana, Noshir A.

doi:10.1109/icdar.1995.599010

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Not many approaches exist that address the issues of region selection and recognition in the indexing of technical line drawings. Previous work has focused on the recognition of dimensions and other primitives [6], [7], [8], [9], extraction of text components within drawings [10], scene analysis [11], [12], [13], [14], interpretation [15], [16], CAD reconstruction [17], [18], object grouping [19], and diagram understanding [20]. The only known work on indexing of line drawings [21] performed automatic indexing using manually presegmented graphical objects.…”

Section: Introductionmentioning

confidence: 99%

Indexing of technical line drawing databases

Syeda-Mahmood

1999

IEEE Trans. Pattern Anal. Machine Intell.

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ÐImage indexing, namely, the problem of retrieving content information from images in response to queries, is a key problem underlying the operations in image databases. In this paper we present a method of indexing for 3D object queries in a database of a class of images called technical line drawings. Indexing is achieved as a combination of query-specific region selection and object recognition. The selection phase isolates relevant images and the regions in these images that are likely to contain the queried object. This is done using text information in the query and a grouping mechanism that is guaranteed to isolate single-object containing regions for the class of technical line drawing images. The grouping mechanism is an adaptation of Waltz relaxation to an extended junction set derived by analyzing the physically plausible ways in which interpretation lines interact with object contours. Model-based object recognition then confirms the presence of the part at the selected location using geometrical description of the queried 3D object. Results are shown that indicate that query-specific selection is very effective for reducing the search during indexing while lowering the chance of false positives and negatives.

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Section: Introductionmentioning

confidence: 99%

Indexing of technical line drawing databases

Syeda-Mahmood

1999

IEEE Trans. Pattern Anal. Machine Intell.

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“…Leader (arrowhead) detection has been investigated by Dori et al [15], Lai and Kasturi [16], and Das and Langrana [17]. Dori et al [15] search areas at wire endpoints for potential arrowheads by tracing lines orthogonal to the wire.…”

Section: Introductionmentioning

confidence: 99%

“…Further test requires significant thickness change and enough black pixels in the arrowhead area. Das and Langrana [17] uses contour extraction of thick or filled in areas as processing of arrowhead detection. Suitable contour shapes are arrowhead candidates and their final direction is determined by the found dimension lines.…”

Section: Introductionmentioning

confidence: 99%

Genericity in graphics recognition algorithms

Wenyin¹,

Dori²

1998

Graphics Recognition Algorithms and Systems

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We present the new version of the Graphics Recognition Module developed within the Machine Drawing Understanding System (MDUS) for the graphics recognition contest, to be held during the second IAPR Workshop on Graphics Recognition, Nancy, France, 1997. A generic integrated graphics recognition algorithm is adapted to meet the requirements of the contest: straight and circular, solid and dashed lines, horizontal and vertical text, and filled and hollow arrowheads. We present detailed algorithms for recognizing these graphic objects, which are all based on the same generic recognition algorithm.

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Object‐process based graphics recognition class library: principles and applications

Wenyin

Dori

1999

Softw: Pract. Exper.

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Recognition of dimension sets and integration with vectorized engineering drawings

Cited by 5 publications

References 5 publications

Indexing of technical line drawing databases

Indexing of technical line drawing databases

Genericity in graphics recognition algorithms

Object‐process based graphics recognition class library: principles and applications

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