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

Diabetes is a chronic condition much prevalent globally. It is an incurable condition, which has to be managed well. Otherwise, it leads to serious complications like heart diseases, stroke, kidney failure, diabetic retinopathy etc. Hyperglycaemia associated with diabetes leads to cardiovascular malfunctioning after the exclusion of other causes like high blood pressure, high cholesterol and obesity. Hence, timely detection and efficient control of diabetes is of extreme significance. We discuss here the role of deep learning in diabetes detection and management. Diabetic neuropathy results in reduction of heart rate variability (HRV). Reduced HRV is thus a marker of diabetic neuropathy. The parameter of HRV is derived from electrocardiography (ECG) signals. ECG is extracted non-invasively from a person. The anomalies in different parts of ECG waveform (like P-wave, ST segment etc.) clearly indicate diseases affecting the heart. These parameters, other than heart rate variations, are dependent on the shape and size of the ECG waveform, and it is extremely difficult to analyse these ECG morphological changes. Hence, many artificial intelligence-based works for diabetes detection have selected HRV as a parameter to assess cardiac autonomic dysfunctions among several available ECG-derived features. Presently deep learning techniques have been increasingly deployed in biomedical signal analysis. As the intricacies and enormity connected with data rise to high levels, a lot of thought and research has to be performed just for deciding which features need to be found out so as to suit what final diagnosis information has to be understood from the data. Deep learning is completely devoid of this manual feature deciding processes unlike machine learning. Deep learning network units do feature extraction and transformation in an implicit manner. Deep learning method is the latest technology which is capable of digging out even the minute information regarding the subtle changes in HRV signals due to diabetes providing an extremely high accuracy of detection. Hybrid deep learning network (cascade of two deep learning architectures) and the deployment of very large-sized input datasets for training these networks can further better the efficacy of abnormality detection. All the different deep learning algorithms used for non-invasive computer-aided diabetes diagnosis, making use of HRV input, are briefly discussed in this book chapter.

Diabetes Detection and Sensor-Based Continuous Glucose Monitoring – A Deep Learning Approach

The progression of social media contents, similar like Twitter and Facebook messages and blog post, has created, many new opportunities for language technology. The user generated contents such as tweets and blogs in most of the languages are written using Roman script due to distinct social culture and technology. Some of them using own language script and mixed script. The primary challenges in process the short message is identifying languages. Therefore, the language identification is not restricted to a language but also to multiple languages. The task is to label the words with the following categories L1, L2, Named Entities, Mixed, Punctuation and Others This paper presents the AmritaCen_NLP team participation in FIRE2015-Shared Task on Mixed Script Information Retrieval Subtask 1: Query Word Labeling on language identification of each word in text, Named Entities, Mixed, Punctuation and Others which uses sequence level query labelling with Support Vector Machine. CCS Concepts • Theory of computation~Support vector machines • Computing methodologies~Natural language Processing • Information systems~Information extraction • Humancentered computing~Social tagging systems

/pdf/amritacen-nlp-fire-2015-language-identification-for-indian-1j9juxpno9.pdf

AmritaCEN_NLP@ FIRE 2015 Language Identification for Indian Languages in Social Media Text

The pure SVD based watermarking scheme does not have high data payload and high security. In this paper, we proposed a new robust multi image-watermarking scheme based on Framelet and SVD. It mainly addresses the multi-user problem in digital rights management. Framelet transform (first level) is applied to the gray scale cover image resulting in eight-detailed band (H1L, H2L, LH1, H1H1, H2H1, LH2, H1H2 and H2H2) and one coarse band (LL). We use SVD to embed eight watermark images on to these subbands. To ensure security, watermark images are scrambled before embedding using secret key. We show that, embedding data in these subbands is robust and resilient to some image processing operation like lossy compression, histogram equalization, convolution, median filtering, removal of lines, cropping, rescaling, noise adding and gamma correction. Moreover, the proposed watermarking method results in an almost imperceptible difference between the watermarked image and the cover image.

Multi Image-Watermarking Scheme Based on Framelet and SVD

Social media has been experiencing an enormous amount of activity from millions of people across the globe over last few years. This resulted in the accumulation of substantial amount of textual data and increased several opportunities of analysis. Sentiment analysis and classification is one such task where the opinion expressed in the text is identified and classified accordingly. This becomes even more trickier in code-mixed text due to free style of writing which does not have a proper syntactic structure. In this paper, we worked on such Hind–English code-mixed texts obtained from SentiMix shared task of SemEval-2020. We created a novel customized embedding model for feature generation from Hindi–English code-mixed texts to classify them to various sentiments like positive, neutral and negative using deep learning techniques. It is observed that attention-based CNN-Bi-LSTM model has achieved better performance out of all models with 70.32% F1-score.

Sentiment Analysis on Hindi–English Code-Mixed Social Media Text

This paper is aimed at diagnosing automotive engine fault in real-time utilizing BigData framework called spark. An automobile in the present day world is equipped with millions of sensors which are under the command of a central unit the ECU (Electronic Control Unit). ECU holds all information about the engine. A network of ECUs connected across the globe is a source tap of BigData. Leveraging the new sources of BigData by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. A piezoelectric transducer coupled to the ECU captures the vibration signals from the engine. The engine fault is detected by carving the problem into a pattern classification problem under machine learning after extracting cyclostationary features from the vibration signal. Spark-streaming framework, the most versatile BigData framework available today with immense computational capabilities is employed for engine fault detection and analysis.

K. P. Soman

Papers

Diabetes Detection and Sensor-Based Continuous Glucose Monitoring – A Deep Learning Approach

AmritaCEN_NLP@ FIRE 2015 Language Identification for Indian Languages in Social Media Text

Multi Image-Watermarking Scheme Based on Framelet and SVD

Sentiment Analysis on Hindi–English Code-Mixed Social Media Text

Real-time automotive engine fault detection and analysis using bigdata platforms