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A Direct Method for 3D Hand Pose Recovery
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
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2014 (English)In: 22nd International Conference on Pattern Recognition, 2014, p. 345-350Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel approach for performing intuitive 3D gesture-based interaction using depth data acquired by Kinect. Unlike current depth-based systems that focus only on classical gesture recognition problem, we also consider 3D gesture pose estimation for creating immersive gestural interaction. In this paper, we formulate gesture-based interaction system as a combination of two separate problems, gesture recognition and gesture pose estimation. We focus on the second problem and propose a direct method for recovering hand motion parameters. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Our experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation. This application is intended to explore the system capabilities in real-time biomedical applications. Eventually, system usability test is conducted to evaluate the learnability, user experience and interaction quality in 3D interaction in comparison to 2D touch-screen interaction.

Place, publisher, year, edition, pages
2014. p. 345-350
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:umu:diva-108475DOI: 10.1109/ICPR.2014.68ISI: 000359818000057ISBN: 978-1-4799-5208-3 (print)OAI: oai:DiVA.org:umu-108475DiVA, id: diva2:853542
Conference
22ND International Conference on Pattern Recognition (ICPR, 24–28 August 2014, Stockholm, Sweden
Available from: 2015-09-14 Created: 2015-09-11 Last updated: 2019-11-11Bibliographically approved
In thesis
1. Object Detection and Recognition in Unstructured Outdoor Environments
Open this publication in new window or tab >>Object Detection and Recognition in Unstructured Outdoor Environments
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Computer vision and machine learning based systems are often developed to replace humans in harsh, dangerous, or tedious situations, as well as to reduce the required time to accomplish a task. Another goal is to increase performance by introducing automation to tasks such as inspections in manufacturing applications, sorting timber during harvesting, surveillance, fruit grading, yield prediction, and harvesting operations.Depending on the task, a variety of object detection and recognition algorithms can be applied, including both conventional and deep learning based approaches. Moreover, within the process of developing image analysis algorithms, it is essential to consider environmental challenges, e.g. illumination changes, occlusion, shadows, and divergence in colour, shape, texture, and size of objects.

The goal of this thesis is to address these challenges to support development of autonomous agricultural and forestry systems with enhanced performance and reduced need for human involvement.This thesis provides algorithms and techniques based on adaptive image segmentation for tree detection in forest environment and also yellow pepper recognition in greenhouses. For segmentation, seed point generation and a region growing method was used to detect trees. An algorithm based on reinforcement learning was developed to detect yellow peppers. RGB and depth data was integrated and used in classifiers to detect trees, bushes, stones, and humans in forest environments. Another part of the thesis describe deep learning based approaches to detect stumps and classify the level of rot based on images.

Another major contribution of this thesis is a method using infrared images to detect humans in forest environments. To detect humans, one shape-dependent and one shape-independent method were proposed.

Algorithms to recognize the intention of humans based on hand gestures were also developed. 3D hand gestures were recognized by first detecting and tracking hands in a sequence of depth images, and then utilizing optical flow constraint equations.

The thesis also presents methods to answer human queries about objects and their spatial relation in images. The solution was developed by merging a deep learning based method for object detection and recognition with natural language processing techniques.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2019. p. 88
Series
Report / UMINF, ISSN 0348-0542 ; 19.08
Keywords
Computer vision, Deep Learning, Harvesting Robots, Automatic Detection and Recognition
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-165069 (URN)978-91-7855-147-7 (ISBN)
Public defence
2019-12-05, MA121, MIT Building, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2019-11-14 Created: 2019-11-08 Last updated: 2019-11-12Bibliographically approved

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Abedan Kondori, FaridOstovar, AhmadLiu, LiLi, Haibo

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