Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"Superpixel"”.
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Superpixel Segmentation Systems: Design and Analysis
… method performs better than existing methods. Superpixel segmentation is an image segmentation in which each region (“superpixel”) preferably forms a portion of an object or scene component rather than the whole, where each superpixel is preferably homogeneous with respect to certain features …
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Superpixel Segmentation of Outdoor Webcams to Infer Scene Structure
… a lot of memory. This thesis explores the use of superpixels to address these issues. Superpixels, an approach to image segmentation, over-segment a scene but attempt to ensure that each segment lies on only one scene element. Applying superpixels to webcams reduces the e∩¼Çect of noise on …
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Sequential Recognition of Manipulation Actions Using Superpixel Group Mining
… action recognition by generating and mining superpixel groups in the videos. Manual annotations of objects and human body parts are not required in the method. We develop a new mid-level representation method called superpixel groups to capture object parts, human body parts and object …
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Deep heterogeneous superpixel neural networks for image analysis and feature extraction
… and interpretable object recognition. Superpixel-based methodologies have been used in conventional computer vision research where their efficient representation has superior effects. In contemporary computer vision research driven by deep neural networks, superpixel-based approaches …
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Map-guided hyperspectral image superpixel segmentation using semi-supervised partial membership latent Dirichlet allocation
Many superpixel segmentation algorithms which are suitable for the regular color images like images with three channels: red, green and blue (RGB images) have been developed in the literature. However, because of the high dimensionality of hyperspectral imagery, these regular superpixel …
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Superpixels for Hyperspectral Image Analysis
… methods that efficiently and robustly exploits superpixels for hyperspectral data. We study and quantify the efficacy of state-of-the-art superpixel generation algorithms for a variety of hyperspectral images. In this work, superpixel level analysis is proposed for two different hyperspectral …
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Techniques for Processing Airborne Imagery for Multimodal Crop Health Monitoring and Early Insect Detection
… visible spectrum images. The first method used a superpixel segmentation approach and achieved a recognition rate of 93.9%, although the processing time was high. The second method used an approach based upon texture and color and achieved a recognition rate of 95.2% while improving upon the …
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StructureTransfer: A Scene Parsing Framework via Graph Matching for Images and Point Clouds
… distribution for the objects. For images, superpixel segmentation is implemented and StructureTransfer is carried out. StructureTransfer is a model to find similar regions across scenes. The two pipelines converge at the inference step. Several novel potentials, representing point cloud …
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Multi-scale local explanation approach for image analysis using model-agnostic explainable artificial intelligence (XAI)
… explanation highlighting the most important superpixels affecting the CNN’s decision. This thesis aims to explore and develop a multi-scale scheme of LIME to explain decisions made by CNN models through heatmaps of coarse to finer scales. More precisely, when LIME highlights large superpixels …
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Tumour Localisation in Histopathology Images
… structural information in tissue by adopting superpixel properties in a rotation invariant manner, suitable for histopathology images. To incorporate essential contextual information, methods which utilise posterior tumour probabilities in an iterative manner are proposed. Results showed …
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Stereo Matching Based on Edge-Aware T-MST
… hybrid edge-prior which combines edge-prior and superpixel-prior to locate the potential disparity edges. Then a widely used Winner-Takes-All (WTA) strategy is performed to establish initial disparity map. An adaptive non-local refinement is also performed based on the stability of initial …
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Online 3D Reconstruction and Ground Segmentation using Drone based Long Baseline Stereo Vision System
… but computationally intensive process of use of superpixel clustering and plane fitting to increase resolution of disparity images to sub-pixel resolution is also presented. Results section provides accuracy of 3D reconstruction results. The presented process is able to generate application …
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Quantitative rice mapping with remote sensing image time series
… area in southwest New South Wales, Australia. A superpixel-based multi-kernel selection approach is proposed to correct the angular effects in multi-temporal remote sensing images. This correction is performed adaptively for different land cover types. In this way the unique bidirectional …
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Minimal Labels, Maximum Gain. Image Classification with Graph-Based Semi-Supervised Learning
… imaging. Firstly, we propose and design a superpixel contracted semi-supervised learning framework to classify hyperspectral images. This approach is built around the p=2 graph Laplacian and uses over-segmentation to greatly reduce the size of the graph as well as providing a regularizing …
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ATTACK AND DEFENSE IN SECURITY ANALYTICS
… attacks, we proposed two attack algorithms: superpixel attack and border attack, which trick the classifier with high confidence. The attack algorithm reveals the truth that machine learning classifiers and deep learning networks (DNNs) need defensive procedures to improve the robustness …
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Learning to Generate and Refine Object Proposals
… and learns a similarity network to guide the superpixel grouping process. We also learn a ranking network to predict the objectness score for each segment proposal. To address the third problem, we take a transformation-based approach to improve the quality of a given segment candidate pool …
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Learning to Generate and Refine Object Proposals
… and learns a similarity network to guide the superpixel grouping process. We also learn a ranking network to predict the objectness score for each segment proposal. To address the third problem, we take a transformation-based approach to improve the quality of a given segment candidate pool …
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Robust graph transduction
… so that the saliency values of all the superpixels are decided from simple superpixels to more difficult ones. The difficulty of a superpixel is judged by its informativity, individuality, inhomogeneity, and connectivity. As a result, our saliency detector generates manifest saliency …
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Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation
Light Detection And Ranging (LiDAR), as well as many other applications and sensors, involve segmenting sparse sets of points (point clouds) for which point density is the only discriminating feature. The segmentation of these point clouds is challenging for several reasons, including the fact that …
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Advances in medical infrared thermography
… eyes by using a mixture of Viola-Jones, KLT, and superpixel algorithms. The results produce specific core versus body extremities temperature patterns that can be used to simulate body response to sepsis. Additionally, we proposed a deep learning classification procedure for sepsis detection. The …