8 research outputs found

    Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

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    This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

    Computational Depth from Defocus via Active Quasi-random Pattern Projections

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    Depth information is one of the most fundamental cues in interpreting the geometric relationship of objects. It enables machines and robots to perceive the world in 3D and allows them to understand the environment far beyond 2D images. Recovering the depth information of the scene plays a crucial role in computer vision, and hence has a strong connection with many applications in the fields such as robotics, autonomous driving and computer-human interfacing. In this thesis, we proposed, designed, and built a comprehensive system for depth estimation from a single camera capture by leveraging the camera response to the defocus effect of the projected pattern. This approach is fundamentally driven by the concept of active depth from defocus (DfD) which recovers depth by analyzing the defocus effect of the projected pattern at different depth levels as appeared in the captured images. While current active DfD approaches are able to provide high accuracy, they rely on specialized setups to obtain images with different defocus levels, making it impractical for a simple and compact depth-sensing system with a small form factor. The main contribution of this thesis is the use of computational modelling techniques to characterize the camera defocus response of the projection pattern at different depth levels, a new approach in active DfD that enables rapid and accurate depth inference in the absence of complex hardware and extensive computing resources. Specifically, different statistical estimation methods are proposed to approximate the pixel intensity distribution of the projected pattern as measured by the camera sensor, a learning process that essentially summarizes the defocus effect to a handful of optimized, distinctive values. As a result, the blurring appearance of the projected pattern at each depth level is represented by depth features in a computational depth inference model. In the proposed framework, the scene is actively illuminated with a unique quasi-random projection pattern, and a conventional RGB camera is used to acquire an image of the scene. The depth map of the scene can then be recovered by studying the depth feature in the captured image of the blurred projection pattern using the proposed computational depth inference model. To verify the efficacy of the proposed depth estimation approach, quantitative and qualitative experiments are performed on test scenes with different structural characteristics. The results demonstrate that the proposed method can produce accurate depth reconstruction results with high fidelity and has strong potential as a cost effective and computationally efficient mean of generating depth maps

    Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

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    This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches

    Fusion multivariater Bildserien am Beispiel eines Kamera-Arrays

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    Die automatische Sichtprüfung spielt eine wesentliche Rolle in der Automatisierungstechnik, etwa zur Qualitätssicherung. Dabei müssen oft heterogene Informationen simultan erfasst werden. Eine Lösungsmöglichkeit bieten Kamera-Arrays mit Kameras, deren Aufnahmeparameter sich individuell konfigurieren lassen. Diese Arbeit stellt Methoden vor, um die erhaltenen multivariaten Bildserien zur simultanen Bestimmung der Gestalt und der spektralen Eigenschaften einer Szene zu fusionieren

    Design of large polyphase filters in the Quadratic Residue Number System

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    Depth From Defocused Motion

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    Motion in depth and/or zooming causes defocus blur. This work presents a solution to the problem of using defocus blur and optical flow information to compute depth at points that defocus when they move. We first formulate a novel algorithm which recovers defocus blur and affine parameters simultaneously. Next we formulate a novel relationship (the blur-depth relationship) between defocus blur, relative object depth and three parameters based on camera motion and intrinsic camera parameters. We can handle the situation where a single image has points which have defocused, got sharper or are focally unperturbed. Moreover, our formulation is valid regardless of whether the defocus is due to the image plane being in front of or behind the point of sharp focus.The blur-depth relationship requires a sequence of at least three images taken with the camera moving either towards or away from the object. It can be used to obtain an initial estimate of relative depth using one of several non-linear methods. We demonstrate a solution based on the Extended Kalman Filter in which the measurement equation is the blur-depth relationship. The estimate of relative depth is then used to compute an initial estimate of camera motion parameters. In order to refine depth values, the values of relative depth and camera motion are then input into a second Extended Kalman Filter in which the measurement equations are the discrete motion equations. This set of cascaded Kalman filters can be employed iteratively over a longer sequence of images in order to further refine depth. We conduct several experiments on real scenery in order to demonstrate the range of object shapes that the algorithm can handle. We show that fairly good estimates of depth can be obtained with just three images

    Large Scale Inverse Problems

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    This book is thesecond volume of a three volume series recording the "Radon Special Semester 2011 on Multiscale Simulation &amp Analysis in Energy and the Environment" that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. This collection of survey articles focusses on the large inverse problems commonly arising in simulation and forecasting in the earth sciences

    Temperature aware power optimization for multicore floating-point units

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