Resolution enhancement of images for further pattern recognition applications

Autor: Maha Awad, Heba A. El-Khobby, Saleh A. Alshebeili, Mustafa M. Abd Elnaby, Salaheldin M. Diab, Fathi E. Abd El-Samie, El-Sayed M. El-Rabaie, B. M. Sallam, Said E. El Khamy, Fatma G. Hashad, Osama S. Faragallah, Alaa M. Abbas
Rok vydání: 2016
Předmět:
Discrete wavelet transform
Computer science
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Bilinear interpolation
02 engineering and technology
030507 speech-language pathology & audiology
03 medical and health sciences
0202 electrical engineering
electronic engineering
information engineering
Image scaling
Feature (machine learning)
Discrete cosine transform
Time domain
Electrical and Electronic Engineering
Decimation
business.industry
020206 networking & telecommunications
Pattern recognition
Atomic and Molecular Physics
and Optics
Electronic
Optical and Magnetic Materials
Discrete sine transform
Feature (computer vision)
Computer Science::Computer Vision and Pattern Recognition
Pattern recognition (psychology)
Bicubic interpolation
Artificial intelligence
Mel-frequency cepstrum
0305 other medical science
business
Interpolation
Zdroj: Optik. 127:484-492
ISSN: 0030-4026
DOI: 10.1016/j.ijleo.2015.08.122
Popis: In storing large databases of images such as fingerprint and medical databases, the required memory size becomes a great challenge. This work demonstrates a framework for reducing the size of large image databases used in pattern recognition applications with decimation, and reconstructing the images with their original sizes using interpolation for feature extraction. For pattern recognition applications, a new trend based on Mel-Frequency Cepstral Coefficients (MFCCs) is presented in the paper. To reconstruct the images to their original sizes, interpolation methods like bilinear, bicubic, warped-distance, and neural methods are investigated and compared. The sensitivity of the extracted features from the images to the interpolation method used is studied. For the feature extraction process, the interpolated images are converted into one dimensional signals with lexicographic ordering and employed in time domain or transformed to Discrete Wavelet Transform (DWT), Discrete Sine Transform (DST), or Discrete Cosine Transform (DCT) domain. The MFCCs and polynomial shape coefficients are then extracted to generate the database of features, which can be used for pattern identification using neural networks. The pattern recognition is conducted by getting features from the pattern image under test. Experimental results show that feature extraction from an interpolated image to retain the original image dimensions can be used robustly for pattern recognition. In addition, the results reveal that the best domain for feature extraction is the DCT.
Databáze: OpenAIRE
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