Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Image Compression using Wavelet and Modified Extreme Learning Machine

The development of Internet and multimedia technologies that grow exponentially, resulting in the amount of information managed by computer is necessary. This causes serious problems in storage and transmission image data. Therefore, should be considered a way to compress data so that the storage capacity required will be smaller. This paper presents a method of compressing still images combining the powerful features of modified extreme learning machine (MELM) for learning with discrete wavelet transform (DWT) in image transformation. DWT, based on the haar wavelet, has been used to transform the image and the coefficients acquired from DWT are then trained with MELM. MELM has the property that it selects a minimal number of coefficients to model the training data. The coefficients are then quantized and encoded using the Huffman coding algorithm. The performance of the proposed method is aspiring and comparable with the existing image compression standards

Study of machine learning techniques for image compression

In the age of the Internet and cloud-based applications, image compression has become increasingly important. Moreover, image processing has recently sparked the interest of technology companies as autonomous machines powered by artificial intelligence using images as input are rapidly growing. Reducing the amount of information needed to represent an image is key to reducing the amount of storage space, transmission bandwidth, and computation time required to process the image, which in turn saves resources, energy, and money. This study aims to investigate machine learning techniques (Fourier, wavelets, and PCA) for image compression. Several Fourier and wavelet methods are included, such as the wellknown Cooley-Tukey algorithm, the discrete cosine transform, and the Mallart algorithm, among others. To comprehend each step of image compression, an object-oriented Matlab code has been developed in-house. To do so, extensive research in machine learning techniques, not only in terms of theoretical understanding, but also in the mathematics that support it. The developed code is used to compare the performance of the different compression techniques studied. The findings of this study are consistent with the advances in image compression technologies in recent years, with the dominance of the JPEG compression method (Fourier) and later JPEG2000 (wavelets) reigning supreme

A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements

We present a Bayesian framework for reconstruction of subsurface hydraulic properties from nonlinear dynamic flow data by imposing sparsity on the distribution of the solution coefficients in a compression transform domain

Securing the Biometric through ECG using Machine Learning Techniques

In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG). The main reason behind this is the certain uniqueness in the ECG signals of the individual.  In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques.  The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3. The work has shown how machine learning can be used to classify the ECG signal