From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

International Journal of Computer and Applications

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

Bayesian sequential estimation of two parameters of a Weibull distribution

Demanding applications of wavelets in OFDM modulation, Watermarking, cognitive radio and wireless networks has created an immense interest amongst the researchers to work on the design of good wavelet system. Parametric and M-Band wavelets draw a special attention in this category. A commensurate procedure for constructing a superior M-Band Parametric wavelet system is proposed in this paper. The idea of Modulation matrix decomposition and the relation with corresponding Polyphase matrix and its factorization is exploited to possible extent to make the design process plain and easy. The appropriate polynomial matrix is put in place to extend the filter design to any required length. The simple property of the kroneckor product of two matrices is contemplated to achieve M-Band design. This paper ponders on the above-mentioned factors and accomplishes the task of designing an M-Band parametric wavelet, which is complete in itself. Few examples are used to substantiate the proposed method.

Improved Technique for the Construction of Parametric M-Band Wavelets

Due to harmonics, sub-harmonics, and interharmonics in modern electrical grid, resolution of the estimation of parameters like frequency and amplitude plays a vital role in determining the stability of the system. Fast and accurate estimation of parameters with a minimal number of data points is essential for quick real-time action in case of contingency. Dynamic Mode Decomposition (DMD) is one of the recently proposed data-driven methods used to estimate the frequency/amplitude with high-resolution, even though it is a spatiotemporal data analytics tool originated in fluid dynamics. This paper investigates the significance of DMD for a fast and accurate estimation of electric parameters with a minimal number of data points. Further, DMD is compared with DFT and Prony algorithm for electric parameter estimation based on the number of samples required for accurate estimation. This aspect is not considered so far.

Investigating the Significance of Dynamic Mode Decomposition for Fast and Accurate Parameter Estimation in Power Grids

The advent of neural networks has led to the development of image classification algorithms that are applied to different fields In order to recover the vital spatial factor parameters, for example, land cover and land utilization, image grouping is most important in remote sensing Recently, benchmark classification accuracy was achieved using convolutional neural networks (CNNs) for land cover classification The most well-known tool which indicates the presence of green vegetation from multispectral pictures is the Normalized Difference Vegetation Index (NDVI) This chaper utilizes the success of the NDVI for effective classification of a new satellite dataset, SAT-4, where the classes involved are types of vegetation As NDVI calculations require only two bands of information, it takes advantage of both RED- and NIR-band information to classify different land cover The number and size of filters affect the number of parameters in convolutional networks Restricting the aggregate number of trainable parameters reduces the complexity of the function and accordingly decreases overfitting The ConvNet Architecture with two band information, along with a reduced number of filters, was trained, and high-level features obtained from a tested model managed to classify different land cover classes in the dataset The proposed architecture, results in the total reduction of trainable parameters, while retaining high accuracy, when compared with existing architecture, which uses four bands

A two-band convolutional neural network for satellite image classification

An image is an artifact that depicts visual perception, having a similar appearance to an object or person, thus providing a depiction of it. Images accomodate various types of noises which are due to sensor defects, lens distortion, software artifacts, blur etc. Denoising an image not only aims at removing the undesired noise but also, at retaining the features of the original image. The same goes for hyperspectral images which have numerous bands (each band contains information of the same object or location taken under different wavelengths of light) as compared to the red, green and blue bands of a color image. The need for better denoising techniques have brought about the birth of different image denoising algorithms, each with its own unique characteristics. Total Variation Denoising (TVD) is an advent for noise removal developed so as to retain sharp edges in the underlying signal. It is characterised as an optimization problem. Denoising using Legendre-Fenchel transform also widely used. Least Square based denoising technique is computationally demanding and gives better results. This paper compares the efficiency of various image denoising techniques like, total variation denoising, legendre-fenchel transform and wavelet transform denoising with the proposed method of least square denoising. This paper focuses on the hyperspectral image denoising technique based on least square approach using different wavelet filters. The proposed technique gives satisfactory denoising output with less computational time when compared with existing methods.

K. P. Soman

Papers

Bayesian sequential estimation of two parameters of a Weibull distribution

Improved Technique for the Construction of Parametric M-Band Wavelets

Investigating the Significance of Dynamic Mode Decomposition for Fast and Accurate Parameter Estimation in Power Grids

A two-band convolutional neural network for satellite image classification

Hyperspectral image denoising: A least square approach using wavelet filters