rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Advances in Pattern Recognition - ICAPR 2001
    sub title: Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings
  name:
    name part: Singh, Sameer.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Murshed, Nabeel.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Kropatsch, Walter.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2001
    edition: 1st ed. 2001.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 482 p. online resource.
  abstract: The paper is organized as follows: In section 2, we describe the no- orientation-discontinuity interfering model based on a Gaussian stochastic model in analyzing the properties of the interfering strokes. In section 3, we describe the improved canny edge detector with an ed- orientation constraint to detect the edges and recover the weak ones of the foreground words and characters; In section 4, we illustrate, discuss and evaluate the experimental results of the proposed method, demonstrating that our algorithm significantly improves the segmentation quality; Section 5 concludes this paper. 2. The norm-orientation-discontinuity interfering stroke model Figure 2 shows three typical samples of original image segments from the original documents and their magnitude of the detected edges respectively. The magnitude of the gradient is converted into the gray level value. The darker the edge is, the larger is the gradient magnitude. It is obvious that the topmost strong edges correspond to foreground edges. It should be noted that, while usually, the foreground writing appears darker than the background image, as shown in sample image Figure 2(a), there are cases where the foreground and background have similar intensities as shown in Figure 2(b), or worst still, the background is more prominent than the foreground as in Figure 2(c). So using only the intensity value is not enough to differentiate the foreground from the background. (a) (b) (c) (d) (e) (f).
  table of contents: INVITED TALKS -- Towards Bridging the Gap between Statistical and Structural Pattern Recognition: Two New Concepts in Graph Matching -- Learning and Adaptation in Robotics -- Image-based self-localization by means of zero phase representation in panoramic images -- NEURAL NETWORKS & COMPUTATIONAL INTELLIGENCE -- A Cascaded Genetic Algorithm for efficient optimization and pattern matching -- Using Unlabelled Data to Train a Multilayer Perceptron -- A Neural Multi -Expert Classification System for MPEG Audio Segmentation -- Pattern Recognition with Quantum Neural Networks -- Pattern Matching and Neural Networks based Hybrid Forecasting System -- Invariant Face Detection in Color Images Using Orthogonal Fourier-Mellin Moments and Support Vector Machines -- CHARACTER RECOGNITION & DOCUMENT ANALYSIS -- Character Extraction from Interfering Background - Analysis of Double-sided Handwritten Archival Documents -- An Enhanced HMM Topology in an LBA Framework for the Recognition of Handwritten Numeral Strings -- Segmentation of Printed Arabic Text -- A Time-Length Constrained Level Building Algorithm for Large Vocabulary Handwritten Word Recognition -- Preventing Overfitting in Learning Text Patterns for Document Categorization -- Image Document Categorization using Hidden Tree Markov Models and Structured Representations -- Handwriting Quality Evaluation -- FEATURE SELECTION & ANALYSIS -- Texture Based Look-Ahead for Decision-Tree Induction -- Feature Based Decision Fusion -- Feature Selection Based on Fuzzy Distances Between Clusters: First Results on Simulated Data -- Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise -- Integrating Recognition Paradigms in a Multiple-path Architecture -- A New Geometric Tool for Pattern Recognition - An Algorithm for Real Time Insertion of Layered Segment Trees -- PATTERN RECOGNITION & CLASSIFICATION -- Improvements in K-Nearest Neighbor Classification -- Branch & Bound Algorithm with Partial Predic…
  note: edited by Sameer Singh, Nabeel Murshed, Walter Kropatsch.
  subject:
    topic: Pattern recognition
  subject:
    topic: Optical data processing
  subject:
    topic: Computer graphics
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Pattern Recognition
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Computer Graphics
  subject:
    topic: Natural Language Processing (NLP)
  classification: Q337.5
  classification: TK7882.P3
  classification: 006.4
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Computer Science, 2013
  identifier: 9783540447320
  identifier: https://doi.org/10.1007/3-540-44732-6
  record info:
    record creation date: 121227
    record change date: 20191025032150.0
    record identifier: 978-3-540-44732-0