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Go to Editorial ManagerImage segmentation is the process of automatically dividing an image into distinct, meaningful, and non-overlapping regions. The quality of the segmentation process determines the efficiency of other image processing tasks. Analyzing microstructural images is crucial since the mechanical properties are strongly dependent on the microstructural phases’ statistics. These images are considered one of the most difficult and challenging images to deal with due to their special characteristics, such as the convergence in pixels intensity values, overlapping in colors, boundaries and textures in phase regions, infinite shapes of grains and colonies, etc. As there is no generic technique suitable to be used with all microstructures, this work reviews techniques that have been effectively used and recommended to be employed in metallurgical research, with a brief description of their principles, advantages, and disadvantages, and discusses their applicability. The major aim of this work is to spare time and effort searching for and experimenting with all the available methods for future researchers.
Interest in neural networks as an alternative to the conventional algorithmic techniques has grown rapidly in recent years. Noise removal or noise suppression is an important task in image processing. In general, the results of the noise removal have a strong influence on the quality of the following image processing techniques. In this paper, two feed forward NN schemes have been presented for impulsive noise removal. The computation is reduced by using an artificial image in training. Results of NN schemes show high performance especially when the ratio of impulsive noise in testing are the same or greater than that of training image. The presented schemes are used for grayscale and also for truecolor.
In this paper, a Neural Network (NN) model system for self-organization fish school system is identified. Monitoring and data extraction from fish school video has been achieved by using image processing technique in order to generate the data suitable for parameter identification of NN model system. Data obtained have been used to identify the parameters of a model based on a black-box represented by nonlinear autoregressive exogenous model (NARX). The obtained results show that this system can be used for multi robot formation system.
The use of image communication has increased in recent years. In this approach, the encryption process is performed by hiding the processing steps of the wavelet transform. The attacker cannot obtain the original image unless processing steps are known. In this paper, the performance of three different hidden wavelet-based schemes are applied. First, hiding filter types encryption scheme (HFT), second, hiding wavelet packet tree encryption scheme (HWPT), lastly, by combining the previous two methods (HFTWPT). Several experiments are given to illustrate the performance of the proposed schemes.
The use of image communication has increased in recent years. In this approach, the encryption process is performed by hiding the processing steps of the wavelet transform. The attacker cannot obtain the original image unless processing steps are known. In this paper, the performance of three different hidden wavelet-based schemes are applied. First, hiding filter types encryption scheme (HFT), second, hiding wavelet packet tree encryption scheme (HWPT), lastly, by combining the previous two methods (HFTWPT). Several experiments are given to illustrate the performance of the proposed schemes.