A parallel-line detection algorithm based on HMM decoding

Zheng, Yefeng; Li, Huiping; Doermann, David

doi:10.1109/tpami.2005.89

Cited by 36 publications

(2 citation statements)

References 37 publications

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“…The user can correct the extraction result if necessary. There are numerous segment vectorization methods [29,30,5]. By combining our algorithm with one of these methods, detectability, and robustness would be improved.…”

Section: D Elements Extractionmentioning

confidence: 99%

Co-Ordinate: Reconstruction of Three Dimensional Geometric Diagrams from a Single Image

Matsuyama¹,

Konno²

2014

The Journal of the Society for Art and Science

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We designed a new algorithm for reconstructing three-dimensional (3D) geometric diagrams. By extracting the orthogonality of the coordinate axes, the contents of labels, the positional relationships between labels and other elements, and direct and indirect connection relationships between elements, we define a score function and derive the optimal parameters that minimize it. This allows the elements to be reconstructed in 3D space and allows the system to create diagrams with different viewpoints. In our system, the reconstructed elements are redrawn with explicit orthogonal coordinate axes. The user can directly move the axes as an interface for changing viewpoints. The system allows the user to edit and attach elements to the diagram interactively.

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Section: D Elements Extractionmentioning

confidence: 99%

Co-Ordinate: Reconstruction of Three Dimensional Geometric Diagrams from a Single Image

Matsuyama¹,

Konno²

2014

The Journal of the Society for Art and Science

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“…Regarding line detection, the well known and most used algorithm is used, i.e. Hough's transform [14], but other algorithms can be used too, as in [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A methodology for speeding up edge and line detection algorithms focusing on memory architecture utilization

2013

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In this paper, a new methodology for speeding up edge and line detection algorithms is presented, achieving improved performance over the state of the art software library OpenCV (speedup from 1.35 up to 2.22) and other conventional implementations, in both general and embedded processors, by reducing the number of load/store and arithmetic instructions, the number of data cache accesses and data cache misses in memory hierarchy and the algorithm memory size. This is achieved by fully exploiting the combination of the software and hardware parameters which are considered simultaneously as one problem and not separately. Furthermore, the edge and line detection algorithms have been simplified for a computer vision application in a Virtex-5 Xilinx FPGA using Microblaze soft processor (detection and measurement of flow fronts in a microfluid device); it achieves speedup up to 660 times in comparison with conventional software implementations.

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Graphical Symbol Recognition

Santosh

Wendling

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic steps as well as prominent concepts or techniques and research standpoints/directions that are associated with graphical symbol recognition. Keywords. Graphics processing • graphical symbol • recognition, retrieval and spotting • statistical approaches • structural and syntactic approaches • real-world problems. '... the recurring wish for methods capable of efficiently combining structural and statistical methods' and 'the very structural and spatial nature of the information we work with makes structural methods quite natural in the community.' The problem can further be extended to symbol spotting, but one can view this as a kind of retrieval (29-33), which is guided by user queries. Additionally, the recognition/retrieval process can be made with the help of local descriptors like scale invariant feature transform (SIFT) and with the use of techniques like bag-of-features so that either primitive or region extraction (segmentation) can be avoided. The question always remains the same, 'what approach does what (i.e., performance) in which context?'. As mentioned earlier, graphics recognition has an extremely rich state-of-the-art literature in symbol recognition and localization (6; 9; 34; 35). But the major state-of-the-art methods for symbol recognition,

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A parallel-line detection algorithm based on HMM decoding

Cited by 36 publications

References 37 publications

Co-Ordinate: Reconstruction of Three Dimensional Geometric Diagrams from a Single Image

Co-Ordinate: Reconstruction of Three Dimensional Geometric Diagrams from a Single Image

A methodology for speeding up edge and line detection algorithms focusing on memory architecture utilization

Graphical Symbol Recognition

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