Essays about: "dense semantic segmentation"
Showing result 1 - 5 of 7 essays containing the words dense semantic segmentation.
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1. Enhancement-basedSmall TargetDetection for InfraredImages
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Infrared small target detection is widely used in fields such as military and security. UNet, which is a classical semantic segmentation method proposed in 2015, has shown excellent performance and robustness. However, U-Net suffers from the problem of losing small targets in deep layers after multiple down-sampling operations. READ MORE
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2. Semi-Supervised Domain Adaptation for Semantic Segmentation with Consistency Regularization : A learning framework under scarce dense labels
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Learning from unlabeled data is a topic of critical significance in machine learning, as the large datasets required to train ever-growing models are costly and impractical to annotate. Semi-Supervised Learning (SSL) methods aim to learn from a few labels and a large unlabeled dataset. READ MORE
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3. Towards Visual-Inertial SLAM for Dynamic Environments Using Instance Segmentation and Dense Optical Flow
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Dynamic environments pose an open problem for the performance of visual SLAM systems in real-life scenarios. Such environments involve dynamic objects that can cause pose estimation errors. READ MORE
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4. Semantic Stixels fusing LIDAR for Scene Perception
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Autonomous driving is the concept of a vehicle that operates in traffic without instructions from a driver. A major challenge for such a system is to provide a comprehensive, accurate and compact scene model based on information from sensors. READ MORE
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5. Segmentation in Skeletal Scintigraphy Images using Convolutional Neural Networks
University essay from Lunds universitet/Matematik LTHAbstract : In this work we have addressed the task of segmentation in skeletal scintigraphy images with deep learning models, where we research different approaches to convert convolutional neural networks designed for classification tasks to powerful pixel wise predictors. We explore different network architectures where two primary research paths have been followed. READ MORE
