Visual pattern recognition using neural networks

Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks. In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance. We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations. Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Technology Directions for the 21st Century

The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter

Image based recognition of the monuments in Prizren

Image classification application has recently been covering a high number of research fields. In the other hand as the performance of the mobile devices is being updated day by day, the implementation of image recognition algorithms in them, is not only being trendy but very helpful in everyday tasks. With the automatic monument recognition, visiting a city is easy and fun. This application recognizes the captured monument, gives useful information and describes that particular landmark. In this thesis there are used four historical monuments of the city of Prizren, Kosovo. The images where taken specially for the research from the different angles of the city and the dataset for the training and testing process has been created. Although these monuments differ from one another in the archaeological structure, the classification process is not an easy approach. Here will be presented an approach for the recognition of these particular monuments by using computer vision and machine learning methods on images. The image processing classification techniques and algorithms used in the literatures not only for the landmark recognition but overall the methods, will be described

A Comparative Study of Handwritten Character Recognition by using Image Processing and Neural Network Techniques

This study aims to analyze the effects of noise, image filtering, and edge detection techniques in the preprocessing phase of character recognition by using a large set of character images exported from MNIST database trained with various sizes of neural networks. Canny edge detection algorithm was deployed to smooth the edges of the images while the Sobel edge detection algorithm was used to detect the edges of the images. Skeletonization algorithm was applied to re-shape the structural shapes. In the context of the image filtering, the Laplacian filter was utilized to enhance the images and High pass filtering was used to highlight the fine details in blurred images. Gaussian noise, image noise with Gaussian intensity, function in Matlab with the probability density function P was deployed on character images of MINST. Pattern recognition neural networks are widely used in optical character recognition. Feedforward neural networks are deployed in this study. A comprehensive analysis of the above-mentioned image processing techniques is included during character recognition. Improved accuracy is observed with character recognition during the prediction phase of the neural networks. A sample of unknown characters is tested with the application of High pass filtering + feedforward neural network and 89%, the highest, average output prediction accuracy was obtained. Other prediction accuracies were also tabulated for the reader’s attention

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

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