Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

Cellular Automata with Synthetic Image A Secure Image Communication with Transform Domain

    Image encryption has attained a great attention due to the necessity to safeguard confidential images. Digital documents, site images, battlefield photographs, etc. need a secure approach for sharing in an open channel. Hardware – software co-design is a better option for exploiting unique features to cipher the confidential images. Cellular automata (CA) and synthetic image influenced transform domain approach for image encryption is proposed in this paper. The digital image is initially divided into four subsections by applying integer wavelet transform. Confusion is accomplished on low – low section of the transformed image using CA rules 90 and 150. The first level of diffusion with consecutive XORing operation of image pixels is initiated by CA rule 42. A synthetic random key image is developed by extracting true random bits generated by Cyclone V field programmable gate array 5CSEMA5F31C6. This random image plays an important role in second level of diffusion. The proposed confusion and two level diffusion assisted image encryption approach has been validated through the entropy, correlation, histogram, number of pixels change rate, unified average change intensity, contrast and encryption quality analyses

Deep Cellular Recurrent Neural Architecture for Efficient Multidimensional Time-Series Data Processing

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the-art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units

Image Encryption Using Meitei Lock Sequence Generated from Hash Functions

Proposed here is a secure image encryption scheme based on generalized Vigenere cipher and Meitei Lock Sequence (MLS) generated from standard hash functions. MLS is a unique random sequence of any length greater than 2 generated from a non-negative array having two or more elements. It is unique in the sense that no two arrays can generate the same sequence however close or similar the two arrays are. In other words, when there is any slight change in any of the input array, the generated MLS’s are drastically different. Also, the length of the sequence can be as infinitely long. These properties make MLS a good key string for a secure encryption scheme. SHA(Secure Hashing Algorithm) or any hash code generator has desirable feature which can be used for generation of MLS. In a hash code generator, it produces unique fixed length sequence from any input string, if there is any slight change in the input, the generated output will be totally different. This feature is made used of  in generating an MLS of any desired length for use in the proposed image  encryption scheme. Experimental results show that the proposed encryption scheme is a secure encryption scheme. The correlation coefficient between the original image and encrypted images are negligibly small indicating that there is no trace of original image information in the encrypted image. Also, the correlation coefficients between the original image and decrypted images with wrong passwords which are close to the encryption password are also negligibly small. These show the tightness of the key system in the encryption scheme.