Author
F. J. Madrid-Cuevas
Other affiliations: Cordoba University
Bio: F. J. Madrid-Cuevas is an academic researcher from University of Córdoba (Spain). The author has contributed to research in topics: Polygonal chain & Thresholding. The author has an hindex of 20, co-authored 45 publications receiving 2434 citations. Previous affiliations of F. J. Madrid-Cuevas include Cordoba University.
Papers
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TL;DR: A fiducial marker system specially appropriated for camera pose estimation in applications such as augmented reality and robot localization is presented and an algorithm for generating configurable marker dictionaries following a criterion to maximize the inter-marker distance and the number of bit transitions is proposed.
1,758 citations
TL;DR: Two Mixed Integer Linear Programming (MILP) approaches to generate configurable square-based fiducial marker dictionaries maximizing their inter-marker distance are proposed.
415 citations
TL;DR: A new algorithm is presented that detects a set of dominant points on the boundary of an eight-connected shape to obtain a polygonal approximation of the shape itself and iteratively deletes redundant break points until the required approximation is achieved.
89 citations
TL;DR: The results obtained show that, independently of the technique employed, the use of depth silhouettes increases the success significantly and how the best results are obtained through the combined use of PCA and HMM.
78 citations
TL;DR: The proposed method provides a criterion to reduce in a significant way the number of initial values to be considered as threshold candidates and can be applied to any feature image provided by an edge detector upon which hysteresis must be implemented.
63 citations
Cited by
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TL;DR: An analysis of comparative surveys done in the field of gesture based HCI and an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters are provided.
Abstract: As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.
1,338 citations
Book•
14 Dec 2016
TL;DR: Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.
Abstract: Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.The second edition is updated to cover new features and changes in OpenCV 2.0, especially the C++ interface.Computer vision is everywherein security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.This book includes:A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms
1,222 citations
TL;DR: This paper proposes a multi-scale strategy for speeding up marker detection in video sequences by wisely selecting the most appropriate scale for detection, identification and corner estimation.
488 citations
TL;DR: Two Mixed Integer Linear Programming (MILP) approaches to generate configurable square-based fiducial marker dictionaries maximizing their inter-marker distance are proposed.
415 citations