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

MODIS-Aqua Data Based Detection and Classification of Algal Blooms along the Coast of India Using RLS Classifier

Understanding the meaning of protein sequences is tedious with human efforts alone. Through this work, we experiment an NLP technique to extract features and give appropriate representation for the protein sequences. In this paper, we have used GloVe representation for the same. A dataset named Swiss-Prot has been incorporated into this work. We were able to create a representation that has comparable ability to understand the semantics of protein sequences compared to the existing ones. We have analyzed the performance of representation by the classification of different protein families in the Swiss-Prot dataset using machine learning technique. The analysis done by us proved the significance of this representation.

Significance of Global Vectors Representation in Protein Sequences Analysis

/pdf/an-approach-to-handle-idioms-and-phrasal-verbs-in-2ttu1irdj2.pdf

An Approach to Handle Idioms and Phrasal Verbs in EnglishTamil Machine Translation System

Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods.

K. P. Soman

Papers

MODIS-Aqua Data Based Detection and Classification of Algal Blooms along the Coast of India Using RLS Classifier

Significance of Global Vectors Representation in Protein Sequences Analysis

An Approach to Handle Idioms and Phrasal Verbs in EnglishTamil Machine Translation System

Multi-sensor data fusion using NIHS transform and decomposition algorithms

Variance Based Offline Power Disturbance Signal Classification Using Support Vector Machine and Random Kitchen Sink