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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 117 for “"Semantic segmentation"”.
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SEMANTIC SEGMENTATION OF PRINT ADVERTISEMENTS
… are perceived primarily across three general semantic dimensions. These dimensions were named Evaluation, Potency and Activity. The relationship between the three dimensions and similar semantic dimensions reported in the Semantic Differential literature is discussed.</p><p>In this research, …
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Semantic Segmentation and 3D Reconstruction of Concrete Cracks
… is proposed. First, using deep learning-based semantic segmentation networks trained on a custom-dataset, crack pixels are identified. Moreover, techniques for improving the accuracy of such networks are developed and evaluated. Second, modifications are applied to the stereo camera’s …
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Automatic Semantic Segmentation Of Kidney Tumors In Computed Tomography Images
Semantic segmentation has emerged as a powerful tool for the computational analysis of medical imaging data, but its enormous need for manual effort has limited its adoption in routine clinical practice. Deep learning methods have begun to achieve impressive automatic semantic segmentation …
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Learn to Generalize and Adapt across Domains in Semantic Segmentation
L'abstract è presente nell'allegato / the abstract is in the attachment
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TOWARDS AN EFFICIENT SEMANTIC SEGMENTATION PIPELINE FOR 3D ELECTRON MICROSCOPY DATA.
… a difficult computer vision task, namely the segmentation of anisotropic 3D electron microscopy image volumes. Deep neural networks tend to struggle in this scenario due to the lack of sufficient training data and the 3 dimensional nature of the images, as such we develop a novel …
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Self-Supervised Learning Method for Semantic Segmentation of LiDAR Point Clouds
Semantic segmentation has shown a significant success for achieving comprehensive scene understanding in real-time perception and urban modeling. Over the recent years, there have been significant advancements in semantic segmentation for LiDAR point clouds, largely the adopting of deep learning …
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Image recognition, semantic segmentation and photo adjustment using deep neural networks
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01
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Solving the Traveling Salesman Problem via Semantic Segmentation with Convolutional Neural Networks
… Heuristic (HIH) that converts a road network semantic map into a truncated distance matrix that can be passed to a traditional TSP solution algorithm. The HIH can be further augmented in the image domain with our proposed novel Convolutional Neural Network (CNN). Our proposed CNN takes as …
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Active Reinforcement Learning for the Semantic Segmentation of Images Captured by Mobile Sensors
… employed to attain acceptable performance on semantic segmentation. To perform well, many supervised learning algorithms require a large amount of annotated data. Furthermore, real-world datasets are frequently severely unbalanced, resulting in poor detection of underrepresented classes. The …
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Multi-task semantic segmentation of damage and materials for visual inspection of civil infrastructure
… corrosion) structural damage. In this approach, semantic segmentation (i.e., assignment of each pixel in the image with a material and damage label) is employed, where the interdependence between material and damage is incorporated through shared filters learned through multi-objective …
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G A N mask R-CNN : instance semantic segmentation benefits from generative adversarial networks
In designing instance segmentation ConvNets that reconstruct masks, segmentation is often taken as its literal definition -assigning label to every pixel- for defining the loss functions. That is, using losses that compute the difference between pixels in the predicted (reconstructed) mask and the …
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High-Resolution Additive Manufacturing Error Prediction and Compensation Through 3D CNN Leveraging Semantic Segmentation
Additive manufacturing (AM) is a relatively new domain of manufacturing processes that began with its first patent in 1986. Since then, AM processes quickly grew in popularity due to their flexibility, superior efficiency in high mix low volume manufacturing settings, and lower material costs …
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Dynamically Instance-Guided Adaptation: A Backward-free Approach for Test-Time Domain Adaptive Semantic Segmentation
Semantic segmentation models often fail when deployed in new target domains due to domain shifts. Test-Time Domain Adaptation for Semantic Segmentation (TTDA-Seg) aims to adapt models efficiently during inference without target labels, but existing methods struggle with efficiency (requiring …
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Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation
… problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work. State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain …
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Semantic segmentation architecture based on Atrous Spatial Pyramid Pooling and convolutional based attention module for mapping of burned areas in satellite images
… efforts. Mapping burned areas can be framed as a semantic segmentation problem. Existing state-of-the-art convolutional neural network (CNN) architectures, such as U-Net and DeepLab have been trained to semantically segment burned areas. Initially successful in natural language processing tasks, …
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Automatic Classification and Segmentation of Patterned Martian Ground Using Deep Learning Techniques
… distinguishing different polygon types and semantic segmentation of polygon regions. Due to time and resource constraints, transfer learning is employed on state-of-the-art deep learning networks. Convolutional neural network model architectures are compared for the binary and multiclass …
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Liquid News - A Semantic-Relational Model for Enhanced Understanding
… leveraging machine-learning-based analysis and semantic navigational aids. Semantic segmentation and unsupervised clustering are the core machine-learning tasks underpinning Liquid News. Thus far, many state-of-the-art (SoTA) large language models provide building blocks for both tasks. However, …
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Learning with Limited Labeled Data: Techniques and Applications
… propose a semi-supervised approach for few-shot semantic segmentation task. Existing solutions for few-shot semantic segmentation cannot easily be applied to utilize image-level weak annotations. We propose a class-prototype augmentation method to enrich the prototype representation by utilizing …
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