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

Coarse classification of Chinese characters via stroke clustering method

Color identification of vehicles plays a significant role in crime detection. In this study, a novel scheme for the color identification of vehicles is proposed using the locating algorithm of regions of interest (ROIs) as well as the color histogram features from still images. A coarse-to-fine strategy was adopted to efficiently locate the ROIs for various vehicle types. Red patch labeling, geometrical-rule filtering, and a texture-based classifier were cascaded to locate the valid ROIs. A color space fusion together with a dimension reduction scheme was designed for color classification. Color histograms in ROIs were extracted and classified by a trained classifier. Seven different classes of color were identified in this work. Experiments were conducted to show the performance of the proposed method. The average rates of ROI location and color classification were 98.45% and 88.18%, respectively. Moreover, the classification efficiency of the proposed method was up to 18 frames per second.

/pdf/vehicle-color-classification-using-manifold-learning-methods-3goo15fzgu.pdf

Vehicle color classification using manifold learning methods from urban surveillance videos

This paper proposes a wavelet-based approach to solving the shape from shading (SFS) problem. The proposed method takes advantage of the nature of wavelet theory, which can be applied to efficiently and accurately represent "things", to develop a faster algorithm for reconstructing better surfaces. In order to improve the robustness of the algorithm, two new constraints are introduced into the objective function to strengthen the relation between an estimated surface and its counterpart in the original image. Thus, solving the SFS problem becomes a constrained optimization process. In the first stage of the process, the set of function variables to be solved is represented by a wavelet format. Due to this format, the set of differential operators of different orders which is involved in the whole process can be approximated with the connection coefficients of Daubechies bases. In each iteration of the optimization process an appropriate step size which will result in maximum decrease of the objective function is determined. After finding correct iterative schemes, the solution of the SFS problem will finally be decided. Compared with conventional algorithms, the proposed scheme makes great improvements on the accuracy as well as the convergence speed of the SFS problem. >

Wavelet-based shape from shading

Recursive hierarchical radical extraction for handwritten Chinese characters

This paper presents a human action recognition system for recognizing various behaviors directly from videos Firstly, we triangulate the human body to different triangle meshes Then, we use a depth-first search (dfs) scheme to find a spanning tree from the set of meshes All leafs of the spanning tree are adopted as the extremities Different from traditional approaches to find the extremities on the targetpsilas silhouette as skeletons, the extremities found from the internal centroids of triangle meshes can represent a human posture more accurately and robustly To model each human action, all the input skeleton sequences are then transformed into symbol sequences Then, we design a string matching scheme to measure the similarity between any two human behaviors Since 2D postures are used in this paper, the above scheme is sensitive to different view points To solve the view independent problem, a 2D matrix is then constructed for recording the symbol relations between two viewpoints Thus, our proposed matching scheme is almost view-invariant Experimental results show that the proposed scheme is a robust, efficient, and promising tool in human action recognition

Kuo-Chin Fan

Papers

Coarse classification of Chinese characters via stroke clustering method

Vehicle color classification using manifold learning methods from urban surveillance videos

Wavelet-based shape from shading

Recursive hierarchical radical extraction for handwritten Chinese characters

Human Action Recognition Using Star Templates and Delaunay Triangulation