By Dr. Muhammad Sarfraz
Clever reputation equipment have lately confirmed to be integral in numerous glossy industries, together with computing device imaginative and prescient, robotics, scientific imaging, visualization and the media. moreover, they play a serious function within the conventional fields equivalent to personality attractiveness, traditional language processing and private identity.
This state-of-the-art e-book attracts jointly the most recent findings of specialists and researchers from worldwide. it's a well timed advisor for all these require accomplished, state of the art suggestion at the current prestige and destiny capability of clever attractiveness technology.
Computer-Aided clever acceptance strategies and Applications:
- Provides the person group with platforms and instruments for software in a really wide selection of parts, together with: IT, schooling, safeguard, banking, police, postal companies, production, mining, medication, multimedia, leisure, communications, facts visualization, wisdom extraction, trend type and digital reality.
- Disseminates info in a plethora of disciplines, for instance development reputation, AI, snapshot processing, machine imaginative and prescient and pics, neural networks, cryptography, fuzzy common sense, databases, evolutionary algorithms, form and numerical analysis.
- Illustrates all thought with real-world examples and case studies.
This helpful source is vital examining for computing device scientists, engineers, and experts requiring up to date entire information at the newest advancements in computer-aided clever popularity options and functions. Its distinctive, functional method may be of curiosity to senior undergraduate and graduate scholars in addition to researchers and specialists within the box of clever recognition.
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Extra info for Computer-aided intelligent recognition techniques and applications
7 (a) Original template for character, ‘ ’; (b) normalized 40×40 template for character ‘ ’. 2 Template Matching Template matching for character recognition is straightforward and is reliable. This method is more tolerant to noise than the structural analysis method. In this approach, the templates are normalized to 40 × 40 pixels and stored in the database. The extracted character, after normalization, is matched with all the characters in the database using the Hamming distance approach. 1). 1) i=1 j=1 where mismatchi j = 1 if originali j = extractedi j 0 if originali j = extractedi j where nrows and ncols are the number of rows and columns in the original and extracted images.
In this strategy, some threshold is taken to avoid unnecessary segmentation by some unwanted black pixels. 6. Character Recognition This is the last phase in the LPR system. This phase is divided into two stages: 1. Normalization of individual characters. 2. Recognition using template matching. 1 Normalization In this phase, first the extracted characters are refined to fit the characters into a window without white spaces on all four sides. 7(a) shows the template for the extracted character ‘ ’ with respect to the above refinement.
And Kim, Y. D. “An approach to Korean license plate recognition based on vertical edge matching,” IEEE International Conference on Systems, Man, and Cybernetics, 4, pp. 2975–2980, 2000.  Hontani, H. and Koga, T. “Character extraction method without prior knowledge on size and information,” Proceedings of the IEEE International Vehicle Electronics Conference (IVEC’01), pp. 67–72, 2001.  Park, S. , Kim, K. , Jung, K. and Kim, H. J. “Locating car license plates using neural networks,” IEE Electronics Letters, 35(17), pp.