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  • 1.
    Abedan Kondori, Farid
    et al.
    Umeå universitet.
    Yousefi, Shahrouz
    KTH Royal Institute of Technology.
    Li, Haibo
    KTH Royal Institute of Technology.
    Direct three-dimensional head pose estimation from Kinect-type sensors2014In: Electronics Letters, ISSN 0013-5194, E-ISSN 1350-911X, Vol. 50, no 4, p. 268-270Article in journal (Refereed)
    Abstract [en]

    A direct method for recovering three-dimensional (3D) head motion parameters from a sequence of range images acquired by Kinect sensors is presented. Based on the range images, a new version of the optical flow constraint equation is derived, which can be used to directly estimate 3D motion parameters without any need of imposing other constraints. Since all calculations with the new constraint equation are based on the range images, Z(xyt), the existing techniques and experiences developed and accumulated on the topic of motion from optical flow can be directly applied simply by treating the range images as normal intensity images I(xyt). In this reported work, it is demonstrated how to employ the new optical flow constraint equation to recover the 3D motion of a moving head from the sequences of range images, and furthermore, how to use an old trick to handle the case when the optical flow is large. It is shown, in the end, that the performance of the proposed approach is comparable with that of some of the state-of-the-art approaches that use range data to recover 3D motion parameters.

  • 2.
    Abedan Kondori, Farid
    et al.
    Umeå University.
    Yousefi, Shahrouz
    Umeå University.
    Li, Haibo
    Umeå University.
    Gesture Tracking for 3D Interaction in Augmented Environments2011In: Proceeding of The Swedish Symposium on Image Analysis (SSBA2011), Linköping, Sweden, 2011Conference paper (Other academic)
  • 3.
    Ekneling, Sanna
    et al.
    Stockholm University, Sweden.
    Sonestedt, Tilian
    Stockholm University, Sweden.
    Georgiadis, Abraham
    Manomotion AB, Sweden.
    Yousefi, Shahrouz
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Chana, Julio
    Manomotion AB, Sweden.
    Magestro: Gamification of the Data Collection Process for Development of the Hand Gesture Recognition Technology2018In: Adjunct Proceedings of the 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), IEEE, 2018, p. 417-418Conference paper (Refereed)
    Abstract [en]

    The work presented in this demo, explores the enhancement of the data collection and data annotation processes via gamilication. For the use case of Hand Tracking (HT) and Gesture Recognition (GR) we have created an Augmented Reality (AR) and Virtual Reality (VR) application that implements both the collection and annotation task. Similar to other popular "Simon Says" games such as Guitar hero, the game versions of the app were easily understood and used by users. Based on previous results, the game versions were widely adopted by the users because of their novelty and entertainment value.

  • 4.
    Georgiadis, Abraham
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Yousefi, Shahrouz
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Analysis of the user experience in a 3D gesture-based supported mobile VR game2017In: VRST '17 Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, ACM Publications, 2017, article id 47Conference paper (Refereed)
    Abstract [en]

    The work presented in this paper, explored the enhancement of User Experience (UX) by introducing a novel gesture-based controller in a mobile multiplayer Virtual Reality (VR) game. Using only the smartphone's RGB camera, the image input was used for both gesture analysis, capable of understanding user actions, as well as segmenting the real hand that was illustrated in the Virtual Environment (VE). Users were also able to share the VR space by cooperating in a survival-strategy scenario. The results from the user studies indicated that both the bare hand controller and the addition of another player in the VR scene, affected the experience for the participants. Users had a stronger feeling of presence in the VE when participated with an other user, and the visual representation of their hand in the VR world made the interactions seem more natural. Even though, there is still a number of limitations, this project nodes this approach capable of offering a natural and engaging solution of VR interaction, capable of rich UX while maintaining a low entry level for the end users.

  • 5.
    Kondori, Abedan Farid
    et al.
    Umeå University.
    Yousefi, Shahrouz
    KTH Royal Institute of Technology.
    Kouma, Jean-Paul
    Liu, Li
    Umeå University.
    Li, Haibo
    Umeå University.
    Direct hand pose estimation for immersive gestural interactionManuscript (preprint) (Other (popular science, discussion, etc.))
    Abstract [en]

    This paper presents a novel approach for performing intuitive gesture-based interactionusing depth data acquired by Kinect. The main challenge to enableimmersive gestural interaction is dynamic gesture recognition. This problemcan be formulated as a combination of two tasks; gesture recognition and gesturepose estimation. Incorporation of fast and robust pose estimation methodwould lessen the burden to a great extent. In this paper we propose a directmethod for real-time hand pose estimation. Based on the range images, a newversion of optical flow constraint equation is derived, which can be utilizedto directly estimate 3D hand motion without any need of imposing other constraints.Extensive experiments illustrate that the proposed approach performsproperly in real-time with high accuracy. As a proof of concept, we demonstratethe system performance in 3D object manipulation on two dierent setups;desktop computing, and mobile platform. This reveals the system capabilityto accommodate dierent interaction procedures. In addition, user studyis conducted to evaluate learnability, user experience and interaction quality in3D gestural interaction in comparison to 2D touch-screen interaction.

  • 6.
    Kondori, Farid Abedan
    et al.
    Umeå University.
    Yousefi, Shahrouz
    Umeå University.
    Smart Baggage in Aviation2011In: 2011 IEEE International Conferences on Internet of Things, and Cyber, Physical and Social Computing / [ed] Feng Xia, Zhikui Chen, Gang Pan, Laurence T. Yang, Jianhua Ma, Los Alamitos: IEEE Press, 2011, p. 620-623Conference paper (Refereed)
    Abstract [en]

    Nowadays, the Internet has dramatically changed the way people take the normal course of actions. By the recent growth of the Internet, connecting different objects to users through mobile phones and computers is no longer a dream. Aviation industry is one of the areas which have a strong potential to benefit from the Internet of Things. Among many problems related to air travel, delayed and lost luggage are the most common and irritating. Therefore, this paper suggests anew baggage control system, where users can simply track their baggage at the airport to avoid losing them. Attaching a particular pattern on the bag, which can be detected and localized from long distance by an ordinary camera, users are able to track their baggage. The proposed system is much cheaper than previous implementations and does not require sophisticated equipment.

  • 7. Kondori, Farid Abedan
    et al.
    Yousefi, Shahrouz
    KTH, Medieteknik och interaktionsdesign, MID.
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Real 3D interaction behind mobile phones for augmented environments2011In: Multimedia and Expo (ICME), 2011 IEEE International Conference on, IEEE Press, 2011, p. 1-6Conference paper (Refereed)
  • 8.
    Kondori, Farid Abedan
    et al.
    Umeå University.
    Yousefi, Shahrouz
    Umeå University.
    Li, Haibo
    Umeå University.
    Sonning, Samuel
    Umeå University.
    3D head pose estimation using the Kinect2011In: 2011 International Conference on Wireless Communications and Signal Processing (WCSP), IEEE Press, 2011, p. 1-4Conference paper (Refereed)
    Abstract [en]

    Head pose estimation plays an essential role for bridging the information gap between humans and computers. Conventional head pose estimation methods are mostly done in images captured by cameras. However accurate and robust pose estimation is often problematic. In this paper we present an algorithm for recovering the six degrees of freedom (DOF) of motion of a head from a sequence of range images taken by the Microsoft Kinect for Xbox 360. The proposed algorithm utilizes a least-squares minimization of the difference between the measured rate of change of depth at a point and the rate predicted by the depth rate constraint equation. We segment the human head from its surroundings and background, and then we estimate the head motion. Our system has the capability to recover the six DOF of the head motion of multiple people in one image. The proposed system is evaluated in our lab and presents superior results.

  • 9.
    Kondori, Farid Abedan
    et al.
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Yousefi, Shahrouz
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Liu, Li
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Active human gesture capture for diagnosing and treating movement disorders2013In: Proceeding of The Swedish Symposium on Image Analysis (SSBA2013), Gothenburg, Sweden, 2013Conference paper (Other academic)
    Abstract [en]

    Movement disorders prevent many people fromenjoying their daily lives. As with other diseases, diagnosisand analysis are key issues in treating such disorders.Computer vision-based motion capture systems are helpfultools for accomplishing this task. However Classical motiontracking systems suffer from several limitations. First theyare not cost effective. Second these systems cannot detectminute motions accurately. Finally they are spatially limitedto the lab environment where the system is installed. In thisproject, we propose an innovative solution to solve the abovementionedissues. Mounting the camera on human body, webuild a convenient, low cost motion capture system that canbe used by the patient in daily-life activities. We refer tothis system as active motion capture, which is not confinedto the lab environment. Real-time experiments in our labrevealed the robustness and accuracy of the system.

  • 10.
    Kondori, Farid Abedan
    et al.
    Umeå university.
    Yousefi, Shahrouz
    Linnaeus University, Faculty of Technology, Department of Media Technology. KTH, Medieteknik och interaktionsdesign, MID.
    Liu, Li
    Li, Haibo
    KTH.
    Head Operated Electric Wheelchair2014In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, IEEE Press, 2014, p. 53-56Conference paper (Refereed)
    Abstract [en]

    Currently, the most common way to control an electric wheelchair is to use joystick. However, there are some individuals unable to operate joystick-driven electric wheelchairs due to sever physical disabilities, like quadriplegia patients. This paper proposes a novel head pose estimation method to assist such patients. Head motion parameters are employed to control and drive an electric wheelchair. We introduce a direct method for estimating user head motion, based on a sequence of range images captured by Kinect. In this work, we derive new version of the optical flow constraint equation for range images. We show how the new equation can be used to estimate head motion directly. Experimental results reveal that the proposed system works with high accuracy in real-time. We also show simulation results for navigating the electric wheelchair by recovering user head motion.

  • 11.
    Kondori, Farid Abedan
    et al.
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Yousefi, Shahrouz
    Linnaeus University, Faculty of Technology, Department of Media Technology. KTH Royal Institue of Technology, Department of Media Technology and Interaction Design, School of Computer Science and Communication.
    Ostovar, Ahmad
    Umeå universitet, Institutionen för datavetenskap.
    Liu, Li
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    KTH Royal Institue of Technology, Department of Media Technology and Interaction Design, School of Computer Science and Communication.
    A Direct Method for 3D Hand Pose Recovery2014In: Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE Press, 2014, p. 345-350Conference 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 learn ability, user experience and interaction quality in 3D interaction in comparison to 2D touch-screen interaction.

  • 12.
    Martin, Manu
    et al.
    ManoMotion AB, Sweden.
    Nguyen, Thang
    ManoMotion AB, Sweden.
    Yousefi, Shahrouz
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Li, Bo
    ManoMotion AB, Sweden.
    Comprehensive features with randomized decision forests for hand segmentation from color images in uncontrolled indoor scenarios2019In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 78, no 15, p. 20987-21020Article in journal (Refereed)
    Abstract [en]

    Hand segmentation is an integral part of many computer vision applications, especially gesture recognition. Training a classifier to classify pixels into hand or background using skin color as a feature is one of the most popular methods for this purpose. This approach has been highly restricted to simple hand segmentation scenarios since color feature alone provides very limited information for classification. Meanwhile there have been a rise of segmentation methods utilizing deep learning networks to exploit multi-layers of complex features learned from image data. Yet a deep neural network requires a large database for training and a powerful computational machine for operations due to its complexity in computations. In this work, the development of comprehensive features and optimized uses of these features with a randomized decision forest (RDF) classifier for the task of hand segmentation in uncontrolled indoor environments is investigated. Newly designed image features and new implementations are provided with evaluations of their hand segmentation performances. In total, seven image features which extract pixel or neighborhood related properties from color images are proposed and evaluated individually as well as in combination. The behaviours of feature and RDF parameters are also evaluated and optimum parameters for the scenario under consideration are identified. Additionally, a new dataset containing hand images in uncontrolled indoor scenarios was created during this work. It was observed from the research that a combination of features extracting color, texture, neighborhood histogram and neighborhood probability information outperforms existing methods for hand segmentation in restricted as well as unrestricted indoor environments using just a small training dataset. Computations required for the proposed features and the RDF classifier are light, hence the segmentation algorithm is suited for embedded devices equipped with limited power, memory, and computational capacities.

  • 13.
    Tewele, Mhretab Kidane
    et al.
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Yousefi, Shahrouz
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Supporting video conference communication using a vision-based human facial synthesis approach2015In: Proceedings of IEEE SAI Intelligent Systems Conference (IntelliSys), 2015, London: IEEE conference proceedings, 2015, p. 807-812Conference paper (Refereed)
    Abstract [en]

    Facial expressions plays an important role in human communication. In many cases where anonymity is a high priority, the identity of a person needs to be hidden or replaced by another one. Recent advancements in facial expression analysis technology have been used in order to duplicate facial human expressions with an avatar. However, a 3D avatar does not convey the same feeling as a real human face does. This paper documents an exploratory study investigating how vision-based facial analysis can be used to match someones facial expressions and head movements with pre-recorded video segments of another person. As a result of these efforts the identity of that person can be replaced with another person in real-time for videoconference supported communication. The proposed technical solutions have been implemented to support real-t

  • 14.
    Yousefi, Shahrouz
    KTH, Medieteknik och interaktionsdesign, MID.
    3D Gesture Recognition and Tracking for Next Generation of Smart Devices: Theories, Concepts, and Implementations2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The rapid development of mobile devices during the recent decade has been greatly driven by interaction and visualization technologies. Although touchscreens have signicantly enhanced the interaction technology, it is predictable that with the future mobile devices, e.g., augmentedreality glasses and smart watches, users will demand more intuitive in-puts such as free-hand interaction in 3D space. Specically, for manipulation of the digital content in augmented environments, 3D hand/body gestures will be extremely required. Therefore, 3D gesture recognition and tracking are highly desired features for interaction design in future smart environments. Due to the complexity of the hand/body motions, and limitations of mobile devices in expensive computations, 3D gesture analysis is still an extremely diffcult problem to solve.

    This thesis aims to introduce new concepts, theories and technologies for natural and intuitive interaction in future augmented environments. Contributions of this thesis support the concept of bare-hand 3D gestural interaction and interactive visualization on future smart devices. The introduced technical solutions enable an e ective interaction in the 3D space around the smart device. High accuracy and robust 3D motion analysis of the hand/body gestures is performed to facilitate the 3D interaction in various application scenarios. The proposed technologies enable users to control, manipulate, and organize the digital content in 3D space.

  • 15.
    Yousefi, Shahrouz
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    3D photo browsing for future mobile devices2012In: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, ACM Press, 2012, p. 1401-1404Conference paper (Refereed)
    Abstract [en]

    By introducing the interactive 3D photo/video browsing and exploration system, we propose novel approaches for handling the limitations of the current 2D mobile technology from two aspects: interaction design and visualization. Our contributions feature an effective interaction that happens in the 3D space behind the mobile device's camera. 3D motion analysis of the user's gesture captured by the device's camera is performed to facilitate the interaction between users and multimedia collections in various applications. This approach will solve a wide range of problems with the current input facilities such as miniature keyboards, tiny joysticks and 2D touch screens. The suggested interactive technology enables users to control, manipulate, organize, and re-arrange their photo/video collections in 3D space using bare-hand, marker-less gesture. Moreover, with the proposed techniques we aim to visualize the 2D photo collection, in 3D, on normal 2D displays. This process is automatically done by retrieving the 3D structure from single images, finding the stereo/multiple views of a scene or using the geo-tagged meta-data from huge photo collections. By using the design and implementation of the contributions of this work, we aim to achieve the following goals: Solving the limitations of the current 2D interaction facilities by 3D gestural interaction; Increasing the usability of the multimedia applications on mobile devices; Enhancing the quality of user experience with the digital collections.

  • 16.
    Yousefi, Shahrouz
    et al.
    Linnaeus University, Faculty of Technology, Department of Media Technology. KTH, Medieteknik och interaktionsdesign, MID.
    Abedan Kondori, Farid
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Bare-hand Gesture Recognition and Tracking through the Large-scale Image Retrieval2014Conference paper (Refereed)
  • 17.
    Yousefi, Shahrouz
    et al.
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Abedan Kondori, Farid
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Tracking fingers in 3D space for mobile interaction2010In: The Second International Workshop on Mobile Multimedia Processing (WMMP 2010) in conjunction with The 20th International Conference on Pattern Recognition (ICPR 2010) / [ed] Jiang, Ma & Rohs, Citeseer , 2010, p. 72-79Conference paper (Refereed)
    Abstract [en]

    Number of mobile devices such as mobile phones or PDAs has been dramatically increased over the recent years. New mobile devices are equipped with integrated cameras and large displays which make the interaction with device easier and more efficient. Although most of the previous works on interaction between humans and mobile devices are based on 2D touch-screen displays, camera-based interaction opens a new way to manipulate in 3D space behind the device in the camera's field of view.In this paper, our gestural interaction relies heavily on particular patterns from local orientation of the image called Rotational Symmetries. This approach is based on finding the most suitable pattern from the large set of rotational symmetries of different orders which ensures a reliable detector for fingertips and human gesture. Consequently, gesture detection and tracking can be used as an efficient tool for 3D manipulation in various applications in computer vision and augmented reality.

  • 18.
    Yousefi, Shahrouz
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Kidane, Mhretab
    ManoMotion AB, Stockholm.
    Delgado, Yeray
    ManoMotion AB, Sweden.
    Chana, Julio
    ManoMotion AB, Sweden.
    Reski, Nico
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    3D Gesture-Based Interaction for Immersive Experience in Mobile VR2016In: 2016 23rd International Conference on Pattern Recognition (ICPR)Cancún Center, Cancún, México, December 4-8, 2016, Cancun: IEEE Press, 2016, , p. 6p. 2122-2127Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a novel solution for real-time 3D hand gesture analysis using the embedded 2D camera of a mobile device. The presented framework is based on forming a large database of hand gestures including the ground truth information of hand poses and details of finger joints in 3D. For a query frame captured by the mobile device's camera in real time, the gesture analysis system finds the best match from the database. Once the best match is found, the corresponding ground truth information will be used for interaction in the designed interface. The presented framework performs an extremely efficient gesture analysis (more than 30 fps) in flexible lighting condition and complex background with dynamic movement of the mobile device. The introduced work is implemented in Android and tested in Gear VR headset.

  • 19.
    Yousefi, Shahrouz
    et al.
    Umeå University.
    Kondori, Farid Abedan
    Umeå University.
    Li, Haibo
    Umeå University.
    3D Gestural Interaction for Stereoscopic Visualization on Mobile Devices2011In: Computer Analysis of Images and Patterns: 14th International Conference, CAIP 2011, PT 2 / [ed] Pedro Real, Daniel DiazPernil, Helena MolinaAbril, Ainhoa Berciano, Walter Kropatsch, Berlin: Springer, 2011, p. 555-562Conference paper (Refereed)
    Abstract [en]

    Number of mobile devices such as smart phones or Tablet PCs has been dramatically increased over the recent years. New mobile devices are equipped with integrated cameras and large displays which make the interaction with device more efficient. Although most of the previous works on interaction between humans and mobile devices are based on 2D touch-screen displays, camera-based interaction opens a new way to manipulate in 3D space behind the device in the camera's field of view. In this paper, our gestural interaction heavily relies on particular patterns from local orientation of the image called Rotational Symmetries. This approach is based on finding the most suitable pattern from a large set of rotational symmetries of different orders which ensures a reliable detector for hand gesture. Consequently, gesture detection and tracking can be hired as an efficient tool for 3D manipulation in various applications in computer vision and augmented reality. The final output will be rendered into color anaglyphs for 3D visualization. Depending on the coding technology different low cost 3D glasses will be used for viewers.

  • 20.
    Yousefi, Shahrouz
    et al.
    Umeå University.
    Kondori, Farid Abedan
    Umeå University.
    Li, Haibo
    Umeå University.
    3D Visualization of Monocular Images in Photo Collections2011In: Proceeding of the Swedish Symposium on Image Analysis (SSBA2011), Linköping, Sweden, 2011Conference paper (Refereed)
  • 21.
    Yousefi, Shahrouz
    et al.
    Umeå University.
    Kondori, Farid Abedan
    Umeå University.
    Li, Haibo
    Umeå University.
    3D Visualization of Single Images using Patch Level Depth2011In: Signal Processing and Multimedia Applications (SIGMAP), 2011 Proceedings of the International Conference on, IEEE Press, 2011, p. 61-66Conference paper (Refereed)
    Abstract [en]

    In this paper we consider the task of 3D photo visualization using a single monocular image. The main idea is to use single photos taken by capturing devices such as ordinary cameras, mobile phones, tablet PCs etc. and visualize them in 3D on normal displays. Supervised learning approach is hired to retrieve depth information from single images. This algorithm is based on the hierarchical multi-scale Markov Random Field (MRF) which models the depth based on the multi-scale global and local features and relation between them in a monocular image. Consequently, the estimated depth image is used to allocate the specified depth parameters for each pixel in the 3D map. Accordingly, the multi-level depth adjustments and coding for color anaglyphs is performed. Our system receives a single 2D image as input and provides a anaglyph coded 3D image in output. Depending on the coding technology the special low-cost anaglyph glasses for viewers will be used.

  • 22.
    Yousefi, Shahrouz
    et al.
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Kondori, Farid Abedan
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Camera-based gesture tracking for 3D interaction behind mobile devices2012In: International journal of pattern recognition and artificial intelligence, ISSN 0218-0014, Vol. 26, no 8, article id 1260008Article in journal (Refereed)
    Abstract [en]

    Number of mobile devices such as Smartphones or Tablet PCs has been dramatically increased over the recent years. New mobile devices are equipped with integrated cameras and large displays that make the interaction with the device easier and more efficient. Although most of the previous works on interaction between humans and mobile devices are based on 2D touch-screen displays, camera-based interaction opens a new way to manipulate in 3D space behind the device in the camera's field of view. In this paper, our gestural interaction relies on particular patterns from local orientation of the image called rotational symmetries. This approach is based on finding the most suitable pattern from a large set of rotational symmetries of diffrerent orders that ensures a reliable detector for fingertips and user's gesture. Consequently, gesture detection and tracking can be used as an efficient tool for 3D manipulation in various virtual/augmented reality applications.

  • 23.
    Yousefi, Shahrouz
    et al.
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Kondori, Farid Abedan
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    Umeå universitet, Institutionen för tillämpad fysik och elektronik.
    Experiencing real 3D gestural interaction with mobile devices2013In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 34, no 8, p. 912-921Article in journal (Refereed)
    Abstract [en]

    Number of mobile devices such as smart phones or Tablet PCs has been dramatically increased over the recent years. New mobile devices are equipped with integrated cameras and large displays which make the interaction with the device more efficient. Although most of the previous works on interaction between humans and mobile devices are based on 2D touch-screen displays, camera-based interaction opens a new way to manipulate in 3D space behind the device, in the camera's field of view. In this paper, our gestural interaction heavily relies on particular patterns from local orientation of the image called Rotational Symmetries. This approach is based on finding the most suitable pattern from a large set of rotational symmetries of different orders that ensures a reliable detector for hand gesture. Consequently, gesture detection and tracking can be hired as an efficient tool for 3D manipulation in various applications in computer vision and augmented reality. The final output will be rendered into color anaglyphs for 3D visualization. Depending on the coding technology, different low cost 3D glasses can be used for the viewers. (C) 2013 Elsevier B.V. All rights reserved.

  • 24.
    Yousefi, Shahrouz
    et al.
    KTH, Medieteknik och interaktionsdesign, MID.
    Kondori, Farid Abedan
    Umeå University.
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Interactive 3D Visualization on a 4K Wall-Sized Display2014In: Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA), 2014, p. 1-4Conference paper (Refereed)
  • 25.
    Yousefi, Shahrouz
    et al.
    Umeå University.
    Kondori, Farid Abedan
    Umeå University.
    Li, Haibo
    Umeå University.
    Robust correction of 3D geo-metadata in photo collections by forming a photo grid2011In: WCSP2011: IEEE International Conference on Wireless Communications and Signal Processing, IEEE Press, 2011, p. 1-5Conference paper (Refereed)
    Abstract [en]

    In this work, we present a technique for efficient and robust estimation of the exact location and orientation of a photo capture device in a large data set. The provided data set includes a set of photos and the associated information from GPS and orientation sensor. This attached metadata is noisy and lacks precision. Our strategy to correct this uncertain data is based on the data fusion between measurement model, derived from sensor data, and signal model given by the computer vision algorithms. Based on the retrieved information from multiple views of a scene we make a grid of images. Our robust feature detection and matching between images result in finding a reliable transformation. Consequently, relative location and orientation of the data set construct the signal model. On the other hand, information extracted from the single images combined with the measurement data make the measurement model. Finally, Kalman filter is used to fuse these two models iteratively and enhance the estimation of the ground truth(GT) location and orientation. Practically, this approach can help us to design a photo browsing system from a huge collection of photos, enabling 3D navigation and exploration of our huge data set.

  • 26.
    Yousefi, Shahrouz
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Kondori, Farid Abedan
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Stereoscopic visualization of monocular images in photo collections2011In: Wireless Communications and Signal Processing (WCSP), 2011 International Conference on, IEEE Press, 2011, p. 1-5Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a novel approach for 3D video/photo visualization using an ordinary digital camera. The idea is to turn any 2D camera into 3D based on the data derived from a collection of captured photos or a recorded video. For a given monocular input, the retrieved information from the overlapping photos can be used to provide required information for performing 3D output. Robust feature detection and matching between images is hired to find the transformation between overlapping frames. The transformation matrix will map images to the same horizontal baseline. Afterwards, the projected images will be adjusted to the stereoscopic model. Finally, stereo views will be coded into 3D channels for visualization. This approach enables us making 3D output using randomly taken photos of a scene or a recorded video. Our system receives 2D monocular input and provides double layer coded 3D output. Depending on the coding technology different low cost 3D glasses will be used for viewers.

  • 27.
    Yousefi, Shahrouz
    et al.
    KTH Royal Institute of Technology, Sweden.
    Li, Haibo
    KTH Royal Institute of Technology, Sweden.
    3D Hand Gesture Analysis through a Real-time Gesture Search Engine2015In: International Journal of Advanced Robotic Systems, ISSN 1729-8806, E-ISSN 1729-8814, Vol. 12, article id 67Article in journal (Refereed)
    Abstract [en]

    3D gesture recognition and tracking are highly desired features of interaction design in future mobile and smart environments. Specifically, in virtual/augmented reality applications, intuitive interaction with the physical space seems unavoidable and 3D gestural interaction might be the most effective alternative for the current input facilities such as touchscreens. In this paper, we introduce a novel solution for real-time 3D gesture-based interaction by finding the best match from an extremely large gesture database. This database includes the images of various articulated hand gestures with the annotated 3D position/orientation parameters of the hand joints. Our unique matching algorithm is based on the hierarchical scoring of the low-level edge-orientation features between the query frames and database and retrieving the best match. Once the best match is found from the database in each moment, the pre-recorded 3D motion parameters can instantly be used for natural interaction. The proposed bare-hand interaction technology performs in real-time with high accuracy using an ordinary camera.

  • 28.
    Yousefi, Shahrouz
    et al.
    KTH Royal Institute of Technology.
    Li, Haibo
    KTH Royal Institute of Technology.
    3D Interaction through a Real-time Gesture Search Engine2015In: Computer Vision - ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II, Springer, 2015, p. 199-213Conference paper (Refereed)
    Abstract [en]

    3D gesture recognition and tracking are highly desired features of interaction design in future mobile and smart environments. Specifically, in virtual/augmented reality applications, intuitive interaction with the physical space seems unavoidable and 3D gestural interaction might be the most effective alternative for the current input facilities such as touchscreens. In this paper, we introduce a novel solution for realtime 3D gesture-based interaction by finding the best match from an extremely large gesture database. This database includes the images of various articulated hand gestures with the annotated 3D position/orientation parameters of the hand joints. Our unique matching algorithm is based on the hierarchical scoring of the low-level edge-orientation features between the query frames and database and retrieving the best match. Once the best match is found from the database in each moment, the pre-recorded 3D motion parameters can instantly be used for natural interaction. The proposed bare-hand interaction technology performs in real-time with high accuracy using an ordinary camera.

  • 29.
    Yousefi, Shahrouz
    et al.
    Linnaeus University, Faculty of Technology, Department of Media Technology. KTH, Medieteknik och interaktionsdesign, MID.
    Li, Haibo
    KTH, Medieteknik och interaktionsdesign, MID.
    Abedan Kondori, Farid
    Real-time 3D Gesture Recognition and Tracking System for Mobile Devices2014Patent (Other (popular science, discussion, etc.))
  • 30.
    Yousefi, Shahrouz
    et al.
    KTH Royal Institute of Technology, SE-100 44, Stockholm.
    Li, Haibo
    KTH Royal Institute of Technology, SE-100 44, Stockholm.
    Liu, Li
    Umea University.
    3D Gesture Analysis Using a Large-Scale Gesture Database2014In: Advances in Visual Computing: 10th International Symposium, ISVC 2014, Las Vegas, NV, USA, December 8-10, 2014, Proceedings, Part I / [ed] George Bebis et al, Springer, 2014, p. 206-217Conference paper (Refereed)
    Abstract [en]

    3D gesture analysis is a highly desired feature of future interaction design. Specifically, in augmented environments, intuitive interaction with the physical space seems unavoidable and 3D gestural interaction might be the most effective alternative for the current input facilities. This paper, introduces a novel solution for real-time 3D gesture analysis using an extremely large gesture database. This database includes the images of various articulated hand gestures with the annotated 3D position/orientation parameters of the hand joints. Our unique search algorithm is based on the hierarchical scoring of the low-level edge-orientation features between the query input and database and retrieving the best match. Once the best match is found from the database in real-time, the pre-calculated 3D parameters can instantly be used for gesture-based interaction.

1 - 30 of 30
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