81,639 research outputs found

    Vision based 3D Gesture Tracking using Augmented Reality and Virtual Reality for Improved Learning Applications

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    3D gesture recognition and tracking based augmented reality and virtual reality have become a big interest of research because of advanced technology in smartphones. By interacting with 3D objects in augmented reality and virtual reality, users get better understanding of the subject matter where there have been requirements of customized hardware support and overall experimental performance needs to be satisfactory. This research investigates currently various vision based 3D gestural architectures for augmented reality and virtual reality. The core goal of this research is to present analysis on methods, frameworks followed by experimental performance on recognition and tracking of hand gestures and interaction with virtual objects in smartphones. This research categorized experimental evaluation for existing methods in three categories, i.e. hardware requirement, documentation before actual experiment and datasets. These categories are expected to ensure robust validation for practical usage of 3D gesture tracking based on augmented reality and virtual reality. Hardware set up includes types of gloves, fingerprint and types of sensors. Documentation includes classroom setup manuals, questionaries, recordings for improvement and stress test application. Last part of experimental section includes usage of various datasets by existing research. The overall comprehensive illustration of various methods, frameworks and experimental aspects can significantly contribute to 3D gesture recognition and tracking based augmented reality and virtual reality.Peer reviewe

    Tracking of secondary and temporary objects in structural concrete work

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    Previous research has shown that “Scan-vs-BIM ” object recognition systems, that fuse 3D point clouds from Terrestrial Laser Scanning (TLS) or digital photogrammetry with 4D project BIM, provide valuable information for tracking structural works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structures only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking such elements would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. This paper presents three different techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds. Two of the techniques are tested using real-life data collected from a reinforced concrete building construction site. The preliminary experimental results show that it is feasible to recognize secondary and temporary objects in TLS point clouds with good accuracy; but it is envisaged that superior results could be achieved by using additional cues such colour and 3D edge information

    3D Object Representations for Recognition.

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    Object recognition from images is a longstanding and challenging problem in computer vision. The main challenge is that the appearance of objects in images is affected by a number of factors, such as illumination, scale, camera viewpoint, intra-class variability, occlusion, truncation, and so on. How to handle all these factors in object recognition is still an open problem. In this dissertation, I present my efforts in building 3D object representations for object recognition. Compared to 2D appearance based object representations, 3D object representations can capture the 3D nature of objects and better handle viewpoint variation, occlusion and truncation in object recognition. I introduce three new 3D object representations: the 3D aspect part representation, the 3D aspectlet representation and the 3D voxel pattern representation. These representations are built to handle different challenging factors in object recognition. The 3D aspect part representation is able to capture the appearance change of object categories due to viewpoint transformation. The 3D aspectlet representation and the 3D voxel pattern representation are designed to handle occlusions between objects in addition to viewpoint change. Based on these representations, we propose new object recognition methods and conduct experiments on benchmark datasets to verify the advantages of our methods. Furthermore, we introduce, PASCAL3D+, a new large scale dataset for 3D object recognition by aligning objects in images with 3D CAD models. We also propose two novel methods to tackle object co-detection and multiview object tracking using our 3D aspect part representation, and a novel Convolutional Neural Network-based approach for object detection using our 3D voxel pattern representation. In order to track multiple objects in videos, we introduce a new online multi-object tracking framework based on Markov Decision Processes. Lastly, I conclude the dissertation and discuss future steps for 3D object recognition.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120836/1/yuxiang_1.pd

    Model based methods for locating, enhancing and recognising low resolution objects in video

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    Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process

    3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation

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    This paper proposes a simple and efficient method for the reconstruction and extraction of geometric parameters from 3D tubular objects. Our method constructs an image that accumulates surface normal information, then peaks within this image are located by tracking. Finally, the positions of these are optimized to lie precisely on the tubular shape centerline. This method is very versatile, and is able to process various input data types like full or partial mesh acquired from 3D laser scans, 3D height map or discrete volumetric images. The proposed algorithm is simple to implement, contains few parameters and can be computed in linear time with respect to the number of surface faces. Since the extracted tube centerline is accurate, we are able to decompose the tube into rectilinear parts and torus-like parts. This is done with a new linear time 3D torus detection algorithm, which follows the same principle of a previous work on 2D arc circle recognition. Detailed experiments show the versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing, Sep 2015, Genova, Italy. 201

    Real-Time 3D Object Recognition for Automatic Tracker Initialization

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    We propose a vision based real-time object recognition system, that provides object identification and 3D position data for the automatic initialization of a 3D tracking system. A-priori information is generated using the models of objects which may be present in the image. During recognition this data is accessed, using the position of corner features in the video image for indexing. A voting scheme is used to recognize objects and their positions in a single run. Upon recognition, a 3D object tracker can be automatically provided with initialization data
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