Fast and robust recognition and localization of 2-D objects

Gerdes, R.; Otterbach, R.; Kammüller, Reiner

doi:10.1007/bf01213498

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…Similar ideas have been applied mostly to the problems of object detection, extraction and classification in other classes of digital images [24]. In most of them, the idea comes from the assumption that the first processing stage performs a rough detection of objects of interest, while the second one applies more precise means to improve the identification accuracy [25]. In many papers, the two-stage approach is related to the integration of features (e.g., appearance and spatio-temporal HOGs [26], difference-of-Gaussians and accumulated gradient projection vector [27], entropy of local histograms and heuristic features [28], edge information and SIFT features [29]), combining classifiers (e.g., SVM and random sample consensus-RANSAC [30], two stages of mean-shift clustering [31]), mixed approaches (e.g., Hough transform joined with DBSCAN clustering [32], edge map and SVM [33], HOG and SVM [34], two variants of snakes [35], particle swarm optimization and fuzzy classifier [36]).…”

Section: Two-stage Processing Conceptmentioning

confidence: 99%

Two-stage approach to extracting visual objects from paper documents

Forczmański

Markiewicz

2016

Machine Vision and Applications

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In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haarlike features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machinelearning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

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Section: Two-stage Processing Conceptmentioning

confidence: 99%

Two-stage approach to extracting visual objects from paper documents

Forczmański

Markiewicz

2016

Machine Vision and Applications

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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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Image Analysis and Computer Vision: 1996

Rosenfeld

1997

Computer Vision and Image Understanding

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Fast and robust recognition and localization of 2-D objects

Cited by 6 publications

References 10 publications

Two-stage approach to extracting visual objects from paper documents

Two-stage approach to extracting visual objects from paper documents

Graphical Symbol Recognition

Image Analysis and Computer Vision: 1996

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