Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Presents a methodology for evaluating graphics recognition systems operating on images that contain straight lines, circles, circular arcs, and text blocks. It enables an empirical comparison of vectorization software packages and uses practical performance evaluation methods that can be applied to complete vectorization systems. The methodology includes a set of matching criteria for pairs of graphical entities, a set of performance evaluation metrics, and a benchmark for the evaluation of graphics recognition systems. The benchmark was tested on three systems. The results are reported and analyzed in the paper.

Empirical performance evaluation of graphics recognition systems

Accurate and efficient vectorization of line drawings is essential for their higher level processing. We present a thinningless sparse pixel vectorization (SPV) algorithm. Rather than visiting all the points along the wire's black area, SPV sparsely visits selected medial axis points. The result is a crude polyline, which is refined through polygonal approximation by removing redundant points. Due to the sparseness of pixel examination and the use of a specialized data structure, SPV is both time efficient and accurate, as evaluated by our proposed performance evaluation criteria.

Sparse pixel vectorization: an algorithm and its performance evaluation

In this paper, we present a complete system for the analysis of architectural drawings, with the aim of reconstructing in 3D the represented buildings. We successively describe the graphics recognition algorithms used for image processing and feature extraction, the 2D modeling step, which includes symbol recognition and converts the drawing into a description in terms of basic architectural entities, and a proposed 3D modeling process which matches reconstructed floors. The system also includes a powerful and flexible user interface.

A complete system for the analysis of architectural drawings

Accurate and efficient vectorization of line drawings is essential for any higher level processing in document analysis and recognition systems. In spite of the prevalence of vectorization and line detection methods, no standard for their performance evaluation protocol exists. We propose a protocol for evaluating both straight and circular line extraction to help compare, select, improve, and even design line detection algorithms to be incorporated into line drawing recognition and understanding systems. The protocol involves both positive and negative sets of indices, at pixel and vector levels. Time efficiency is also included in the protocol. The protocol may be extended to handle lines of any shape as well as other classes of graphic objects.

http://dori2.technion.ac.il/opm/documents/articles/article166.pdf

A protocol for performance evaluation of line detection algorithms

This paper describes a protocol for systematically evaluating the performance of dashed-line detection algorithms. It includes a test image generator which creates random line patterns subject to prespecified constraints. The generator also outputs ground truth data for each line in the image. The output of the dashed line detection algorithm is then compared to these ground truths and evaluated using a set of criteria.

A Benchmark: Performance Evaluation of Dashed-Line Detection Algorithms

In this paper, we describe a system that detects lines of various types, e.g., solid lines and dotted lines, on document images. The main techniques are based on the recursive morphological transforms, namely the recursive opening and closing transforms. The advantages of the transforms are that they can perform binary opening and closing with any sized structuring element simultaneously in constant time per pixel, and that they offer a solution to morphological image analysis problems where the sizes of the structuring elements have to be determined after the examination of the image itself. The system is evaluated on about 1,200 totally ground-truthed IRS tax form images of different qualities. The line detection output is compared with a set of hand-drawn groundtruth lines. The statistics like the number and rate of correct detection, miss detection, and false alarm are calculated. The performance of 32 algorithms for solid line detection are compared to find the best one. The optimal algorithm tuning parameter settings could be estimated on the fly using a regression tree.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Bin Kong

Papers

A Benchmark: Performance Evaluation of Dashed-Line Detection Algorithms

Automatic line detection in document images using recursive morphological transforms