Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

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

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Medical Imaging Signals And Systems

The paper provides a comparative analysis of the application of the most demanded gradational correction models (power, exponential and logarithmic) of a highly lighted digital image, which capable of automatic adaptation to different brightness scales, discusses the features of their practical application, sets up an experiment to improve the highly lighted photo. In the experiment, the test image is modified with use of different gradation correction models and different parameters, this helps to show the practical value of such modifications and the coefficient of image enhancement is given to provide comparative analysis of influence of input parameters to final result. After considering the results of the experiment, analysis and recommendations for the practical use of the models are given which helps to solve actual applied tasks of digital image quality improvement

Comparative Efficiency Analysis of Gradational Correction Models of Highly Lighted Image

The presented article describes the most popular unified models of gradation correction of a darkened digital image, makes a comparative analysis of the effectiveness of these models, formulates practical recommendations for using the considered models of gradation correction when solving actual applied problems of improving the quality of darkened digital images.

Gradational Correction Models Efficiency Analysis of Low-Light Digital Image

The article puts an experiment on application of widely used ORB, SIFT and SURF feature point detectors represented by the corresponding functions in the OpenCV library, to the images of the most common object classes such as human faces, fine details and artificial images. The considered detectors display as a result a huge number of points that are not classified or structured. Building an appropriate classifier would greatly increase the efficiency of subsequent image processing operations: localization, recognition, search, and tracking of objects. The article analyzes the effectiveness of the experimental results at a quantitative and qualitative level taking into account the conditions and limitations (primarily temporal) on solving practical problems in the era of Big Data, as well as taking into account the fact that some detectors are proprietary. According to the analysis results of the usage effectiveness of the features points detectors considered in the work the practical recommendations for specific use cases are given at the end of the work.

Local Feature Detectors Performance Analysis on Digital Image

Braille is the most popular system used for interaction between visually-impaired and sighted people using tactile means. Optical Braille character recognition (OBCR) includes two main steps: Braille cells' recognition (image acquisition, preprocessing, Braille dots' recognition, Braille cells' recognition and segmentation) and Braille cells' transcription to corresponding natural language characters. System example has been created using image processing methods and artificial neural networks approach. These methods allow to achieve high speed and recognition accuracy level. System can adapt to factors like quality of input patterns and differences between them dynamically. In this paper, artificial neural network is developed to identify letter's images of Cyrillic alphabet in Braille representation system. Network will be trained and tested for identifying of scanned Cyrillic letters in Braille. Some of the letters are noised with some type of noise to simulate the real-world environment.

Braille Character Recognition Based on Neural Networks

The paper discusses promising ways to build an invariant model and an efficient image search algorithm in Big Image Warehouse. Based on open data using the appropriate Python prototype, Search by Image Engine, a series of experiments is conducted to search for images in Big Data Warehouses. Estimates of the effectiveness of the search using the developed prototype, taking into account valid image transforms are given.

Anastasiya Chupryna

Papers

Comparative Efficiency Analysis of Gradational Correction Models of Highly Lighted Image

Gradational Correction Models Efficiency Analysis of Low-Light Digital Image

Local Feature Detectors Performance Analysis on Digital Image

Braille Character Recognition Based on Neural Networks

Search by Image Engine for Big Data Warehouse