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

Due to the rapid development of mobile devices equipped with cameras, the realization of what you get is what you see is not a dream anymore In general, texts in images often draw people’s attention due to the following reasons: semantic meanings to objects in the image (eg, the name of the book), information about the environment (eg, a traffic sign), or commercial purpose (eg, an advertisement) The mass development of mobile device with low cost cameras boosts the demand of recognizing characters in nature scenes via mobile devices such as smartphones Employing text detection algorithms along with character recognition techniques on mobile devices assists users in understanding or gathering useful information around them A useful mobile application is the translation tool Using handwriting as the input is widely used in current translation tools on smartphones However, capturing images and recognizing texts directly is more intuitive and convenient for users A translation tool with character recognition techniques recognizes texts on the road signs or restaurant menus Such application greatly helps travelers and blinds

/pdf/preprocessing-for-images-captured-by-cameras-2a96jdn9x5.pdf

Preprocessing for Images Captured by Cameras

A graphic-based approach to the computation of the number of stack filters

In this paper, a novel manifold learning algorithm termed orthogonal nearest neighbor feature space embedding (ONNFSE) is proposed to eliminate three drawbacks of the nearest feature space embedding (NFSE) approach. The first one is an extrapolation error, a feature line passes through two far neighbor points is selected for scatter matrix calculating when the distance of a specified point to this line is small. The calculated scatter matrix could not efficiently preserve the local topological structure among samples. The incorrect selection will reduce the recognition rates. The interpolation error is similar the extrapolation one. To remedy these two problems, the nearest neighbor feature space is built in the proposed ONNFSE. The last problem should be solved is the non-orthogonal eigenvectors found by the NFSE algorithm. The modified ONNFSE algorithm generates orthogonal bases which possess the more discriminating power. Experimental results are conducted to demonstrate the effectiveness of our proposed algorithm.

Orthogonal Nearest Neighbor Feature Space Embedding

A graphic searching algorithm is proposed to find the optimal stack filter. The search of the optimal stack filter is reduced to a problem of finding a minimal path from the root node to the optimal node in the error cone graph (ECG). Two graphic searching techniques, the greedy and A* algorithms, are applied to avoid the searching an extremely large search space. Experimental results are given to show the efficiency and performance of our proposed method.

Finding of optimal stack filter by using graphic searching methods

Remote sensing is used in an increasingly wide range of applications. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. Therefore, we organized a Special Issue on remote sensing titled “Artificial Intelligence and Machine Learning Applications in Remote Sensing.” In this paper, we review nine articles included in this Special Issue, most of which report studies based on satellite data and DL, reflecting the most prevalent trends in remote sensing research, as well as how DL architecture and the functioning of DL models can be analyzed and explained is a hot topic in AI research. DL methods can outperform conventional machine learning methods in remote sensing; however, DL remains a black box and understanding the details of the mechanisms through which DL models make decisions is difficult. Therefore, researchers must continue to investigate how explainable DL methods for use in the field of remote sensing can be developed.

https://www.mdpi.com/2072-4292/15/3/569/pdf?version=1674031220

Kuo-Chin Fan

Papers

Preprocessing for Images Captured by Cameras

A graphic-based approach to the computation of the number of stack filters

Orthogonal Nearest Neighbor Feature Space Embedding

Finding of optimal stack filter by using graphic searching methods

Special Issue Review: Artificial Intelligence and Machine Learning Applications in Remote Sensing