Kamil Dimililer

Neural Networks Arbitration for Optimum DCT Image Compression

Image compression using Discrete Cosine Transform (DCT) is one of the simplest commonly used compression methods. The quality of compressed images, however, is marginally reduced at higher compression ratios due to the lossy nature of DCT compression, thus, the need for finding an optimum DCT compression ratio. An ideal image compression system must yield high quality compressed images with good compression ratio, while maintaining minimum time cost. Neural networks perform well in simulating non-linear relationships. This paper suggests that a neural network could be trained to recognize an optimum ratio for DCT compression of an image upon presenting the image to the network. The neural network associates the image intensity with its compression ratios in search for an optimum ratio. Experimental results suggest that a trained neural network can simulate such non-linear relationship and thus can be successfully used to provide an intelligent optimum image compression system.

Comparison Criteria for Optimum Image Compression

Recently, many efficient image compression methods have been developed in order to compress image data prior to further processing. Discrete cosine transforms and wavelet transforms are examples of these compression methods. The aim of the work presented within this paper is to introduce comparison criteria for optimum compression using these well known image compression techniques. The comparison criteria will comprise visual inspection and computed analysis

Backpropagation Neural Network Implementation for Medical Image Compression

Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

Intelligent coin identification system

The use of neural networks to simulate our perception of patterns is important in developing intelligent recognition systems. Currently, coin identification by machines relies on the assessment of the coins physical parameters. An intelligent coin identification system that uses coin patterns for identification helps prevent confusion between different coins of similar physical dimensions. In this paper, an intelligent coin identification system (ICIS) is proposed. ICIS uses a neural network and pattern averaging to recognize rotated coins at various degrees. Slot machines in Europe accept the new Turkish 1 Lira coin as a 2 Euro coin due to physical similarities. A 2 Euro coin is roughly worth 4 times the new Turkish 1 Lira. ICIS was implemented to identify the 2-EURO and 1-TL coins and the results were found to be encouraging

Medical radiographs compression using neural networks and Haar wavelet

Efficient storage and transmission of medical images in telemedicine is of utmost importance however, this efficiency can be hindered due to storage capacity and constraints on bandwidth. Thus, a medical image may require compression before transmission or storage. Ideal image compression systems must yield high quality compressed images with high compression ratio; this can be achieved using wavelet transform based compression, however, the choice of an optimum compression ratio is difficult as it varies depending on the content of the image. In this paper, a neural network is trained to relate radiograph image contents to their optimum image compression ratio. Once trained, the neural network chooses the ideal Haar wavelet compression ratio of the x-ray images upon their presentation to the network. Experimental results suggest that our proposed system, can be efficiently used to compress radiographs while maintaining high image quality.

ILTDS: Intelligent Lung Tumor Detection System on CT Images

Cancer detection and research on early detection solutions play life sustaining role for human health. Computed Tomography images are widely used in radiotherapy planning. Computed Tomography images provide electronic densities of tissues of interest, which are mandatory. For certain target delineation, the good spatial resolution and soft/hard tissues contrast are needed. Also, Computed Tomography techniques are preferred compared to X-Ray and magnetic resonance imaging images. Image processing techniques have started to become popular in use of Computed Tomography images. Artificial neural networks propose a quite different approach to problem solving and known as the sixth generation of computing. In this study, two phases are proposed. For first phase, image pre-processing, image erosion, median filtering, thresholding and feature extraction of image processing techniques are applied on Computed Tomography images in detail. In second phase, an intelligent image processing system using back propagation neural networks is applied to detect lung tumors.

ICIS: A Novel Coin Identification System

When developing intelligent recognition systems, our perception of patterns can be simulated using neural networks. An intelligent coin identification system that uses coin patterns for classification helps prevent confusion between different coins of similar physical dimensions. Currently, coin identification by machines relies on the assessment of the coin’s physical parameters. In this paper, a rotation-invariant intelligent coin identification system (ICIS) is presented. ICIS uses a neural network and pattern averaging to recognize rotated coins at various degrees. Slot machines in Europe accept the new Turkish 1-Lira coin as a 2-Euro coin due to physical similarities. A 2-Euro coin is roughly worth 4 times the new Turkish 1-Lira. ICIS was implemented to identify the 2 EURO and 1 TL coins and the results were found to be encouraging.

Rotated Coin Recognition Using Neural Networks

Neural networks have been used in the development of intelligent recognition systems that simulate our ability recognize patterns. However, rotated objects may cause incorrect identification by recognition systems. Our quick glance provides an overall approximation of a pattern regardless of noise or rotations. This paper proposes that the overall approximation of a pattern can be achieved via pattern averaging prior to training a neural network to recognize that pattern in various rotations. Pattern averaging provides the neural network with “fuzzy” rather than “crisp” representations of the rotated objects, thus, minimizing computational costs and providing the neural network with meaningful learning of various rotations of an object. The proposed method will be used to recognize rotated coins and is implemented to solve an existing problem where slot machines in Europe accept the new Turkish 1 Lira coin as a 2 Euro coin.

Gaussian Noise and Haar Wavelet Transform Image Compression on Transmission of Dermatological Images

Telemedicine provides medical information and services using telecommunication technologies. Teledermatology, is a special part in the medical field of dermatology and one of the most common applications of telemedicine and e-health. Telecommunication technologies are used in Teledermatology to exchange medical information over a distance using audio, visual and data communication. Medical images require compression; Wavelet-based image compression provides substantial improvements in picture quality at higher compression ratios. An ideal image compression system must yield high quality compressed image with high compression ratio; this ratio can be achieved using transform-based image compression, however the contents of the image affects the choice of an optimum compression ratio and the optimum compression method. This paper presents image compression method, Haar wavelet transform, which can be applied to compress dermatology images before the transmission through a communication channel.

A preliminary framework using bpnn for performability modelling and analyses of a kerberos server

This paper presents a preliminary framework using back propagation neural networks for performability evaluation of Kerberos servers. In the modelling approach, keys under pseudo-secure conditions are dynamically renewed and also, temporary interruption to link/server access is involved. This interruption has implications on mortification of systems performance. Since this effects the network communications cost, an analytical model is created for evaluation. Analytical models results provide understanding on the values of authentication key renewal(restoration) times, times passed between renewals(restorations) and failures(breakdowns) of the server. Additionally, another distinctive way of critical thinking and problem solving is suggested, and an intelligent system using back propagation neural networks is applied to detect performability modelling.