The Experts below are selected from a list of 219 Experts worldwide ranked by ideXlab platform
Partha Pratim Roy - One of the best experts on this subject based on the ideXlab platform.
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fingertip detection and tracking for recognition of air writing in videos
Expert Systems With Applications, 2019Co-Authors: Sohom Mukherjee, Sk Arif Ahmed, Debi Prosad Dogra, Samarjit Kar, Partha Pratim RoyAbstract:Abstract Air-writing is the process of writing characters or words in free space using finger or hand movements without the aid of any hand-held device. In this work, we address the problem of mid-air finger writing using Web-Cam video as input. In spite of recent advances in object detection and tracking, accurate and robust detection and tracking of the fingertip remains a challenging task, primarily due to small dimension of the fingertip. Moreover, the initialization and termination of mid-air finger writing is also challenging due to the absence of any standard delimiting criterion. To solve these problems, we propose a new writing hand pose detection algorithm for initialization of air-writing using the Faster R-CNN framework for accurate hand detection followed by hand segmentation and finally counting the number of raised fingers based on geometrical properties of the hand. Further, we propose a robust fingertip detection and tracking approach using a new signature function called distance-weighted curvature entropy. Finally, a fingertip velocity-based termination criterion is used as a delimiter to mark the completion of the air-writing gesture. Experiments show the superiority of the proposed fingertip detection and tracking algorithm over state-of-the-art approaches giving a mean precision of 73.1 % while achieving real-time performance at 18.5 fps, a condition which is of vital importance to air-writing. Character recognition experiments give a mean accuracy of 96.11 % using the proposed air-writing system, a result which is comparable to that of existing handwritten character recognition systems.
Mario Hernandeztejera - One of the best experts on this subject based on the ideXlab platform.
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hand gesture recognition for human machine interaction
International Conference in Central Europe on Computer Graphics and Visualization, 2004Co-Authors: Elena Sancheznielsen, Luis Antoncanalis, Mario HernandeztejeraAbstract:Even after more than two decades of input devices development, many people still find the interaction with computers an uncomfortable experience. Efforts should be made to adapt computers to our natural means of communication: speech and body language. The PUI paradigm has emerged as a post-WIMP interface paradigm in order to cover these preferences. The aim of this paper is the proposal of a real time vision system for its application within visual interaction environments through hand gesture recognition, using general-purpose hardware and low cost sensors, like a simple personal computer and an USB Web Cam, so any user could make use of it in his office or home. The basis of our approach is a fast segmentation process to obtain the moving hand from the whole image, which is able to deal with a large number of hand shapes against different backgrounds and lighting conditions, and a recognition process that identifies the hand posture from the temporal sequence of segmented hands. The most important part of the recognition process is a robust shape comparison carried out through a Hausdorff distance approach, which operates on edge maps. The use of a visual memory allows the system to handle variations within a gesture and speed up the recognition process through the storage of different variables related to each gesture. This paper includes experimental evaluations of the recognition process of 26 hand postures and it discusses the results. Experiments show that the system can achieve a 90% recognition average rate and is suitable for real-time applications.
Sohom Mukherjee - One of the best experts on this subject based on the ideXlab platform.
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fingertip detection and tracking for recognition of air writing in videos
Expert Systems With Applications, 2019Co-Authors: Sohom Mukherjee, Sk Arif Ahmed, Debi Prosad Dogra, Samarjit Kar, Partha Pratim RoyAbstract:Abstract Air-writing is the process of writing characters or words in free space using finger or hand movements without the aid of any hand-held device. In this work, we address the problem of mid-air finger writing using Web-Cam video as input. In spite of recent advances in object detection and tracking, accurate and robust detection and tracking of the fingertip remains a challenging task, primarily due to small dimension of the fingertip. Moreover, the initialization and termination of mid-air finger writing is also challenging due to the absence of any standard delimiting criterion. To solve these problems, we propose a new writing hand pose detection algorithm for initialization of air-writing using the Faster R-CNN framework for accurate hand detection followed by hand segmentation and finally counting the number of raised fingers based on geometrical properties of the hand. Further, we propose a robust fingertip detection and tracking approach using a new signature function called distance-weighted curvature entropy. Finally, a fingertip velocity-based termination criterion is used as a delimiter to mark the completion of the air-writing gesture. Experiments show the superiority of the proposed fingertip detection and tracking algorithm over state-of-the-art approaches giving a mean precision of 73.1 % while achieving real-time performance at 18.5 fps, a condition which is of vital importance to air-writing. Character recognition experiments give a mean accuracy of 96.11 % using the proposed air-writing system, a result which is comparable to that of existing handwritten character recognition systems.
James D Schwarzmeier - One of the best experts on this subject based on the ideXlab platform.
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building an autonomous vehicle by integrating lego mindstorms and a Web Cam
Technical Symposium on Computer Science Education, 2007Co-Authors: Daniel E Stevenson, James D SchwarzmeierAbstract:There are many possible ways to integrate Lego Mindstorms robots into the standard computer science curriculum. This paper presents a way to use these robots to teach image processing or vision by building an autonomous vehicle. The vehicle uses an off-the-shelf Web Cam for all of its navigation. Integration of the Camera, robot, and controlling computer is discussed, as are the image processing units used, the structure of a state machine controlling them, and the cross-cutting concern of reducing both input and output noise throughout all aspects of the project.
Elena Sancheznielsen - One of the best experts on this subject based on the ideXlab platform.
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hand gesture recognition for human machine interaction
International Conference in Central Europe on Computer Graphics and Visualization, 2004Co-Authors: Elena Sancheznielsen, Luis Antoncanalis, Mario HernandeztejeraAbstract:Even after more than two decades of input devices development, many people still find the interaction with computers an uncomfortable experience. Efforts should be made to adapt computers to our natural means of communication: speech and body language. The PUI paradigm has emerged as a post-WIMP interface paradigm in order to cover these preferences. The aim of this paper is the proposal of a real time vision system for its application within visual interaction environments through hand gesture recognition, using general-purpose hardware and low cost sensors, like a simple personal computer and an USB Web Cam, so any user could make use of it in his office or home. The basis of our approach is a fast segmentation process to obtain the moving hand from the whole image, which is able to deal with a large number of hand shapes against different backgrounds and lighting conditions, and a recognition process that identifies the hand posture from the temporal sequence of segmented hands. The most important part of the recognition process is a robust shape comparison carried out through a Hausdorff distance approach, which operates on edge maps. The use of a visual memory allows the system to handle variations within a gesture and speed up the recognition process through the storage of different variables related to each gesture. This paper includes experimental evaluations of the recognition process of 26 hand postures and it discusses the results. Experiments show that the system can achieve a 90% recognition average rate and is suitable for real-time applications.
