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An introduction to digital image processing

Wayne Niblack
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The article was published on 1986-01-01 and is currently open access. It has received 1745 citations till now. The article focuses on the topics: Digital image processing & Image processing.

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Journal ArticleDOI

An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy

TL;DR: Two novel objective functions based on the normalized incomplete Beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively, and the QPSO and AQPSO perform better than GA and PSO for the enhancement of these images.
Proceedings ArticleDOI

Handwritten Carbon Form Preprocessing Based on Markov Random Field

TL;DR: This paper proposes a statistical approach to degraded handwritten form image preprocessing including binarization and form line removal by a Markov random field (MRF) where the prior is learnt from a training set of high quality binarized images, and the probabilistic density is learnt on-the-fly from the gray-level histogram of input image.
Proceedings ArticleDOI

Efficient computation of adaptive threshold surfaces for image binarization

TL;DR: This work proposes a different method to determine an adaptive threshold surface, inspired by multiresolution approximation, which is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.
Proceedings ArticleDOI

Real-time detection and reading of LED/LCD displays for visually impaired persons

TL;DR: A novel system that acquires video, detects and reads LED/LCD characters in real time, reading them aloud to the user with synthesized speech is described, demonstrating the feasibility of the system.
Journal ArticleDOI

A double continuous approach to visualization and analysis of categorical maps

TL;DR: A method to visualize multiple membership maps, called ‘Colour mixture’ (CM) is described and compared with alternative techniques: defuzzification and Pixel mixture, which provides a basis for automated generalization.
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