http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

https://www.cin.ufpe.br/~rps/Artigos/Face%20Detection%20-%20%20A%20Survey.pdf

Face Detection

A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

/pdf/thirty-years-of-graph-matching-in-pattern-recognition-58lvpdkcaj.pdf

Thirty years of graph matching in pattern recognition

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

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

Robust face recognition under variant illumination conditions is a challenging research problem. In this paper, a new shadow compensation method, called adaptive shadow compensation (ASC), will be proposed to eliminate shadows in the face image due to non-frontal lighting directions. ASC performs shadow compensation in each local region, based on Fourier analysis on each local image block, to remove as much shadows as possible. Null-space linear discriminant analysis is then employed to extract discriminant features from ASC compensated images. Experimental results on the Yale B face database show that the proposed ASC method can achieve high face recognition accuracy under illumination variations.

/pdf/adaptive-shadow-compensation-for-face-recognition-under-53cvkd28hg.pdf

Adaptive Shadow Compensation for Face Recognition Under Illumination Variations

The computation efficiency in human identification problem is a very important issue when the number of database templates is large. In this paper, we propose a histogram based approach to improve the computation efficiency for human gait classification. We convert the human gait classification problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multi resolution structure on the Motion Energy Histogram (MEH). To utilize the multi resolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins. Experiments demonstrate the feasibility and validity of the proposed approach.

An Efficient Way to Classify Human Gaits

Error-Correcting Isomorphism of Directed Graphs by Genetic-Based Search.

This study attempts to apply pattern recognition (PR) technologies with associative memory to real-time pattern recognition of engineering components using a client-server network structure in a Web-based recognition system. A remote engineer is able to draw directly the shape of engineering components using the browser, and the recognition system will search for the component database of a company using the Internet. Component patterns are stored in the database system. Their properties and specifications are also attached to the data field of each component pattern except that of engineering components. In our approach, the recognition system adopts parallel computing, and will raise the recognition rate. Our recognition system is a client-server network structure using the Internet. The system uses a recurrent neural network (RNN) with associative memory to perform training and recognition. The last phase utilizes the technology of database matching and solves the problem of spurious state. Our system will be used at the Yang-Fen Automation Electrical Engineering Company.

Using the RNN to develop a Web-based pattern recognition system for pattern search of component database

Among various kinds of documents, forms are one of the important types. The prerequisite for form optical character recognition (Form OCR) is the extraction of characters from form documents. The authors present a clustering based technique for extracting characters from form documents. In this method, they treat the character extraction process as a pattern clustering problem. The feasibility of the novel method is demonstrated through experimenting various kinds of forms. Experimental results reveal the feasibility of the novel method.

Kuo-Chin Fan

Papers

Adaptive Shadow Compensation for Face Recognition Under Illumination Variations

An Efficient Way to Classify Human Gaits

Error-Correcting Isomorphism of Directed Graphs by Genetic-Based Search.

Using the RNN to develop a Web-based pattern recognition system for pattern search of component database

A feature point clustering approach to the segmentation of form documents