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

Color models are used for facilitating the specification of colors in a standard way. A suitable color model is associated with every application based on color space. This paper mainly focuses on the analysis of effectiveness of different color models on single image scale-up problems. Single image scale-up aims in the recovery of original image, where the input image is a blurred and down- scaled version of the original one. In order to identify the effect of different color models on scale-up of single image applications, the experiment is performed with the single image scale-up algorithm on standard image database. The performance of different color models (YCbCr, YCoCg, HSV, YUV, CIE XYZ, Photo YCC, CMYK, YIQ, CIE Lab, YPbPr) are measured by quality metric called Peak Signal to Noise Ratio (PSNR). The experimental results based on the calculated PSNR values prove that YCbCr and CMYK color models give effective results in single image scale-up application when compared with the other available color models.

Analysis of Various Color Space Models on Effective Single Image Super Resolution

Image fusion has enormous applications in the fields of satellite imaging, remote sensing, target tracking, medical imaging, and much more. This paper aims to demonstrate the application of empirical wavelet transform for the fusion of multifocus images incorporating the simple average fusion rule. The method proposed in this paper is experimented on benchmark datasets used for fusing images of different focuses. The effectiveness of the proposed method is evaluated across the existing techniques. The performance comparison of the proposed method is done by visual perception and assessment of standard quality metrics which includes root mean squared error, relative average spectral error, universal image quality index, and spatial information. The experimental result analysis shows that the proposed technique based on the empirical wavelet transform (EWT) outperforms the existing techniques.

Empirical Wavelet Transform for Multifocus Image Fusion

this paper, we present a novel framework for phrase based translation system using translation memory by concept labeling. The concepts are labeled on the input text, followed by the conversion of text into phrases. The phrase is searched throughout the translation memory, where the parallel corpus is stored. The translation memory displays all source and target phrases, wherever the input phrase is present in them. Target phrase corresponding to the output source phrase having the same concept as that of input source phrase, is chosen as the best translated phrase. The system is implemented for English to Tamil translation.

/pdf/phrase-based-english-tamil-translation-system-by-concept-2229anuko2.pdf

Phrase based English - Tamil Translation System by Concept Labeling using Translation Memory

The recent advance in sensor technology is a boon for hyperspectral remote sensing. Though Hyperspectral images (HSI) are captured using these advanced sensors, they are highly prone to issues like noise, high dimensionality of data and spectral mixing. Among these, noise is the major challenge that affects the quality of the captured image. In order to overcome this issue, hyperspectral images are subjected to spatial preprocessing (denoising) prior to image analysis (Classification). In this paper, authors discuss a sparsity based denoising strategy which uses low pass sparse banded filter matrices (AB filter) to effectively denoise each band of HSI. Both subjective and objective evaluations are conducted to prove the efficiency of the proposed method. Subjective evaluations involve visual interpretation while objective evaluations deals with the computation of quality matrices such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) index at different noise variance. In addition to these, the denoised image is followed by a sparsity based classification using Orthogonal Matching Pursuit (OMP) to evaluate the effect of various denoising techniques on classification. Classification indices obtained without and with applying preprocessing are compared to highlight the potential of the proposed method. The experiment is performed on standard Indian Pines Dataset. By using 10% of training set, a significant improvement in overall accuracy (84.21%) is obtained by the proposed method, compared to the other existing techniques.

Hyperspectral Image Denoising Using Low Pass Sparse Banded Filter Matrix for Improved Sparsity Based Classification

With the introduction of fundamental concept computational thinking, Block-based programming tools are turning into more and more common in primary, middle and high school level introductory classes. Block-based programming languages support users to drag and drop different jigsaw shaped blocks and snapping together to form a program. The point of interest is on the programming logic rather than its syntax. In this paper, we present DB-Learn, a browser-based and visual programming environment. This facilitates to learn the concepts of relational algebra concepts. Relational algebra is one of the important concepts to learn and understand the operations of database management systems.

K. P. Soman

Papers

Analysis of Various Color Space Models on Effective Single Image Super Resolution

Empirical Wavelet Transform for Multifocus Image Fusion

Phrase based English - Tamil Translation System by Concept Labeling using Translation Memory

Hyperspectral Image Denoising Using Low Pass Sparse Banded Filter Matrix for Improved Sparsity Based Classification

DB-Learn: Studying Relational Algebra Concepts by Snapping Blocks