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1	Pilot Tone-Aided Detection for Cognitive Radio Applications Hattab, Ghaith 22 April 2014 (has links) Feature-based spectrum sensing techniques have emerged as good balance between energy-based techniques and coherent-based techniques, where the former require minimal prior information of the observed signal, and the latter have robust detection performance when the observed signal is very weak. In this thesis, we focus on pilot tone-aided detection as a feature-based detection class. We propose an improved pilot tone-aided spectrum sensor that utilizes the presence of the pilot tone and the overall energy of the received signal. We show that the optimal Neyman-Pearson detector is a weighted summation of a feature-based component and an energy-based component. The former provides coherent gains at the low signal-to-noise ratio (SNR) regime, whereas the latter provides non-coherent gains at moderate SNR levels. The proposed detector intelligently adapts its weights based on the SNR of the observed signal and the power allocation factor of the pilot tone. This helps it attain significant performance gains compared with the conventional pilot tone-aided detectors. In addition, we present suboptimal detectors that reduce the computational complexity. For instance, we demonstrate that moment estimators are effective techniques for spectrum sensing. Motivated by insights gained from the derivations of these moment estimators, we present a selective mean-variance estimator that performs well in the absence of the prior knowledge about the pilot tone. Moreover, we analyze the impact of two model uncertainties on the detection performance of the proposed detector: Noise uncertainty and imperfect pilot-matching. We show that unlike the energy detector, the proposed detector does not suffer from the SNR wall under the noise uncertainty model due to the coherent gains embedded in the feature-based component. Also, unlike existing pilot tone-aided detectors, the proposed detector is resilient against imperfect synchronization due to the non-coherent gains embedded in computing the overall energy of the signal. Also, we show that the proposed detector achieves the lowest sample complexity, leading to tangible improvements to the aggregate throughput of the secondary user. Extensive simulation and analytical results are provided to verify these conclusions. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2014-04-15 15:31:27.253 feature detectors SNR Wall spectrum sensing cognitive radio
2	A Comparative Performance Evaluation Of Scale Invariant Interest Point Detectors For Infrared And Visual Images Emir, Erdem 01 December 2008 (has links) (PDF) In this thesis, the performance of four state-of-the-art feature detectors along with SIFT and SURF descriptors in matching object features of mid-wave infrared, long-wave infrared and visual-band images is evaluated across viewpoints and changing distance conditions. The utilized feature detectors are Scale Invariant Feature Transform (SIFT), multiscale Harris-Laplace, multiscale Hessian-Laplace and Speeded Up Robust Features (SURF) detectors, all of which are invariant to image scale and rotation. Features on different blackbodies, human face and vehicle images are extracted and performance of reliable matching is explored between different views of these objects each in their own category. All of these feature detectors provide good matching performance results in infrared-band images compared with visual-band images. The comparison of matching performance for mid-wave and long-wave infrared images is also explored in this study and it is observed that long-wave infrared images provide good matching performance for objects at lower temperatures, whereas mid-wave infrared-band images provide good matching performance for objects at higher temperatures. The matching performance of SURF detector and descriptor for human face images in long-wave infrared-band is found to be outperforming than other detectors and descriptors.
3	Camera Motion Blur And Its Effect On Feature Detectors Uzer, Ferit 01 September 2010 (has links) (PDF) Perception, hence the usage of visual sensors is indispensable in mobile and autonomous robotics. Visual sensors such as cameras, rigidly mounted on a robot frame are the most common usage scenario. In this case, the motion of the camera due to the motion of the moving platform as well as the resulting shocks or vibrations causes a number of distortions on video frame sequences. Two most important ones are the frame-to-frame changes of the line-of-sight (LOS) and the presence of motion blur in individual frames. The latter of these two, namely motion blur plays a particularly dominant role in determining the performance of many vision algorithms used in mobile robotics. It is caused by the relative motion between the vision sensor and the scene during the exposure time of the frame. Motion blur is clearly an undesirable phenomenon in computer vision not only because it degrades the quality of images but also causes other feature extraction procedures to degrade or fail. Although there are many studies on feature based tracking, navigation, object recognition algorithms in the computer vision and robotics literature, there is no comprehensive work on the effects of motion blur on different image features and their extraction. In this thesis, a survey of existing models of motion blur and approaches to motion deblurring is presented. We review recent literature on motion blur and deblurring and we focus our attention on motion blur induced degradation of a number of popular feature detectors. We investigate and characterize this degradation using video sequences captured by the vision system of a mobile legged robot platform. Harris Corner detector, Canny Edge detector and Scale Invariant Feature Transform (SIFT) are chosen as the popular feature detectors that are most commonly used for mobile robotics applications. The performance degradation of these feature detectors due to motion blur are categorized to analyze the effect of legged locomotion on feature performance for perception. These analysis results are obtained as a first step towards the stabilization and restoration of video sequences captured by our experimental legged robotic platform and towards the development of motion blur robust vision system.
4	Detektory a deskriptory oblastí v obrazu / Region Detectors and Descriptors in Image Žilka, Filip January 2016 (has links) This master’s thesis deals with an important part of computer vision field. Main focus of this thesis is on feature detectors and descriptors in an image. Throughout the thesis the simplest feature detectors like Moravec detector will be presented, building up to more complex detectors like MSER or FAST. The purpose of feature descriptors is in a mathematical description of these points. We begin with the oldest ones like SIFT and move on to newest and best performing descriptors like FREAK or ORB. The major objective of the thesis is comparison of presented methods on licence plate localization task.
5	Asynchronous Event-Feature Detection and Tracking for SLAM Initialization Ta, Tai January 2024 (has links) Traditional cameras are most commonly used in visual SLAM to provide visual information about the scene and positional information about the camera motion. However, in the presence of varying illumination and rapid camera movement, the visual quality captured by traditional cameras diminishes. This limits the applicability of visual SLAM in challenging environments such as search and rescue situations. The emerging event camera has been shown to overcome the limitations of the traditional camera with the event camera's superior temporal resolution and wider dynamic range, opening up new areas of applications and research for event-based SLAM. In this thesis, several asynchronous feature detectors and trackers will be used to initialize SLAM using event camera data. To assess the pose estimation accuracy between the different feature detectors and trackers, the initialization performance was evaluated from datasets captured from various environments. Furthermore, two different methods to align corner-events were evaluated on the datasets to assess the difference. Results show that besides some slight variation in the number of accepted initializations, the alignment methods show no overall difference in any metric. Overall highest performance among the event-based trackers for initialization is HASTE with mostly high pose accuracy and a high number of accepted initializations. However, the performance degrades in featureless scenes. CET on the other hand shows mostly lower performance compared to HASTE. Visual SLAM SLAM Traditional cameras event camera events neuromorphic camera silicon retina dynamic vision sensor Camera motion Illumination variation Rapid camera movement Visual quality degradation Challenging environments Search and rescue Event cameras Temporal resolution Dynamic range Event-based SLAM Asynchronous feature detectors Trackers Pose estimation accuracy Initialization performance Datasets Corner-event alignment HASTE tracker Pose accuracy Featureless scenes CET tracker Initialization acceptance Performance comparison Signal Processing Signalbehandling Robotics Robotteknik och automation Control Engineering Reglerteknik

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