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

The ultimate aim of this research is to develop a Rule Based Machine Translation System (RBMT) using sentence simplification. The sentence pattern for English is SVO and Tamil is SOV. Complex and larger sentence are not easy to parse and translate. So, the sentence simplifier is also accommodated in the rule based system to split a large sentence into simple multiple sentences. Machine translation is the process of translating sentence from one language to another language. Here, English is the source language for the translation system and Tamil is the target language. During the translation process, the system need to learn and get trained by linguistic rules. These rules are classified into two types, namely reordering rules and morphological rules. A bilingual dictionary has been created to support this automatic translation. Linguistic information act as a backbone for the proposed system. To evaluate the performance of the system, we have experimented by testing 250 sentence patterns and we got the overall accuracy about 0.7186. Module wise human evaluation has been done to understand the issues in each modules.

Improving the rule based machine translation system using sentence simplification (english to tamil)

Categorization of scene images is considered as a challenging prospect due to the fact that different classes of scene images often share similar image statistics. This chapter presents a transfer learning based approach for scene classification. A pre-trained Convolutional Neural Network (CNN) is used as a feature extractor for the images. The pre-trained network along with classifiers such as Support Vector Machines (SVM) or Multi Layer Perceptron (MLP) are used to classify the images. Also, the effect of single plane images such as, RGB2Gray, SVD Decolorized and Modified SVD decolorized images are analysed based on classification accuracy, class-wise precision, recall, F1-score and equal error rate (EER). The classification experiment for SVM was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. By comparing the results of models trained on RGB images with those grayscale images, the difference in the results is very small. These grayscale images were capable of retaining the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of the given scene images.

Scene Classification Using Transfer Learning

Inpainting is the technique of reconstructing unknown or damaged portions of an image in a visually plausible way. Inpainting algorithm automatically fills the damaged region in an image using the information available in undamaged region. Propagation of structure and texture information becomes a challenge as the size of damaged area increases. In this paper, a hierarchical inpainting algorithm using wavelets is proposed. The hierarchical method tries to keep the mask size smaller while wavelets help in handling the high pass structure information and low pass texture information separately. The performance of the proposed algorithm is tested using different factors. The results of our algorithm are compared with existing methods such as interpolation, diffusion and exemplar techniques.

/pdf/hirarchical-digital-image-inpainting-using-wavelets-3ycwgi0a07.pdf

Hirarchical Digital Image Inpainting Using Wavelets

This paper presents the method of Morpheme Extraction and lemmatization for Tamil language in Morpheme Extraction Task (MET) of FIRE-2014. Tamil is a morphologically rich and agglutinative language. Such a language needs deeper analysis at the word level to capture the meaning of the word from its morphemes and its categories. In this attempt, the methodology employed to extract Tamil morphemes and lemmas are based on a supervised machine learning algorithm for nouns and verbs and simple suffix stripping for pronouns and proper nouns. Morphemes are extracted for other Part-of-Speech categories using Tamil Part of Speech tagger. In supervised learning, Morphological analyzer problem is redefined as a classification problem. We decompose the problem of noun and verb morpheme extraction into two sub-problems: learning to perform morpheme identification of words in a text, and learning to perform morpheme tagging. In addition to the Morpheme extraction task results of FIRE-2014, we have carried out different experiments to show the effectiveness of the proposed method.

AMRITA_CEN@FIRE-2014: Morpheme Extraction and Lemmatization for Tamil using Machine Learning

In this paper, we present a new approach for Named Entity Recognition (NER) in Tamil language using Random Kitchen Sink algorithm. Named Entity recognition is the process of identification of Named Entities (NEs) from the text. It involves the identifying and classifying predefined categories such as person, location, organization etc. A lot of work has been done in the field of Named Entity Recognition for English language and Indian languages using various machine learning approaches. In this work, we implement the NER system for Tamil using Random Kitchen Sink algorithm which is a statistical and supervised approach. The NER system is also implemented using Support Vector Machine (SVM) and Conditional Random Field (CRF). The overall performance of the NER system was evaluated as 86.61% for RKS, 81.62% for SVM and 87.21% for CRF. Additional results have been taken in SVM and CRF by increasing the corpus size and the performance are evaluated as 86.06% and 87.20% respectively.

K. P. Soman

Papers

Improving the rule based machine translation system using sentence simplification (english to tamil)

Scene Classification Using Transfer Learning

Hirarchical Digital Image Inpainting Using Wavelets

AMRITA_CEN@FIRE-2014: Morpheme Extraction and Lemmatization for Tamil using Machine Learning

Randomized kernel approach for Named Entity Recognition in Tamil