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.
Results
Showing 1 to 20 of 23 for “"feature map"”.
-
Active perception in visual simultaneous localization and mapping by feature / map point selection
Made available in DSpace on 2020-03-02T22:15:17Z (GMT). No. of bitstreams: 3 SHANGGUAN-THESIS-2019.pdf: 2228824 bytes, checksum: 5b85d2af527e7f9f5ccd86676233811f (MD5) ZhengheShangguan_Thesis-Master.docx: 3626082 bytes, checksum: d163037c82f5c7bd6cd1d7b6167c7684 (MD5) LICENSE.txt: 4214 bytes, …
-
Semi-supervised universal yolov3-spp with GIoU loss for autonomous driving object detection under sunny and foggy weather
… built with a universal adapter that consists a “feature map”-based attention module and a weather-based attention module. The “feature map”-based attention module has a parallel of Squeeze-and-Excitation (SE) adapters, where each SE adapter extracts different features from each channel. The …
-
Target Detection Using a Wavelet-Based Fractal Scheme
… SAR imagery. Recently, a wavelet-based fractal feature set, similar to the proposed one, was compared with the EF feature for a general texture classification problem. The wavelet-based technique yielded a lower classification error than EF, which motivated the comparison between the two …
-
Joint spatial and layer attention for convolutional networks
… Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region …
-
Secure Networking and Efficient Computing in Unmanned Aerial Vehicle Assisted Green IoT Networks
… real-time performance, we propose a novel feature map compression method based on statistical analysis of feature map data. This method is rigorously tested through extensive experiments, which demonstrate significant enhancements in computing efficiency, thereby optimizing the performance …
-
Evaluating Hybrid Quantum-Classical Models for Image Classification in the NISQ Era
… with systematic variation of problem difficulty, feature dimensionality (4, 8 qubits), and training set size (n ∈{100, 250, 400, 500, 2000, 4000}). Statistical rigor was ensured through five random seeds per condition and comprehensive significance testing. During the testing with binary datasets, …
-
An Empirical and Theoretical Analysis of the Role of Depth in Convolutional Neural Networks
… of infinite width. In particular, we derive the feature map for the linear CNTK for arbitrary depths and identify the depth which minimizes the bias and variance terms of the excess risk. The findings of this thesis imply that increasing depth for interpolating convolutional networks can in fact …
-
Towards Resilient Models: A Deep Learning Odyssey through Mammographic Images
… transfer between views at the spatial feature map level, resulting in a substantial improvement in tumor detection. To further investigate the impact of Convolutional Neural Network (CNN) architectures for medical image segmentation, two novel CNN architectures, ConnectedUNets+ and …
-
Hierarchical learning : theory with applications in speech and vision
… object of interest is a recursively defined feature map, and its associated kernel. The class of models we consider exploit the fact that data in a wide variety of problems satisfy a decomposability property. Paralleling the primate visual cortex, hierarchies are assembled from alternating …
-
Neural networks for perpetual grouping
… lines based upon perceptually significant features can facilitate viewpoint independent recognition. The work presented here identifies simple grouping measures based on parallelism and connectivity and shows how it is possible to train multi-layer perceptrons (MLPs) to detect and determine …
-
Algorithm and Hardware Co-optimization for Image Segmentation in Wearable Ultrasound Devices: Continuous Bladder Monitoring
… image segmentation. Using interleaving feature map representation, skip connection compression, and extensive design space exploration, the accelerator does not require external memory or any co-processor, and consumes only 14.4μJ per 128 × 128 image segmentaiton. The lightweight bladder …
-
Towards Real-World Quantum Machine Learning
… and decouples qubit requirements from input feature dimension. Compared to prevailing QNN baselines, the CFF-QNN reduces both depth and CNOT count by over 50% while achieving strong performance on standard benchmarks (e.g., 91% accuracy on Wisconsin breast cancer and 85% on credit-card …
-
Improving the dependability of safety critical wireless sensor network scheduling using artificial intelligence
… across all metrics. Fifthly, the Self-Organising Feature Map (SOFM) algorithm surpasses the Bat algorithm with its straightforward approach to dimension reduction and classification. Sixthly, critical analyses of implemented algorithms (HMM, Bat, and SOFM) reveal similar patterns in coverage data. …
-
Computer-Aided Detection of Clinically Significant Prostate Cancer using Bi-Parametric Magnetic Resonance Imaging
… to facilitate reasoning on high-resolution features, thereby improving the efficiency of learning global information while effectively capturing the details of features. Then, the encoder part consisting of three independent residualblock based branches is used to extract modality-specific …
-
Nichtlineare Methoden in der trainingswissenschaftlichen Diagnostik : mit Untersuchungen aus dem Schwimmsport
Die trainingswissenschaftliche Diagnostik in den Kernbereichen Training, Wettkampf und Leistungsfähigkeit ist durch einen hohen Praxisbezug, eine ausgeprägte strukturelle Komplexität und vielseitige Wechselwirkungen der sportwissenschaftlichen Teilgebiete geprägt. Diese Eigenschaften haben in der …
-
Human motion prediction.
… used in the computer animation industry, feature films, pedestrian tracking, and surveillance. The capacity to understand and predict humans’ future movements is much sought-after, for it would have a range of practical applications in fields such as autonomous vehicles and interactive …
-
Person Re-identification and an Adversarial Attack and Defense for Person Re-identification Networks
… captured by smart phones, and are displayed on a map together with the time-stamps. </p><p>With the recent advances in deep neural networks (DNN), the state-of-the-art performance of person ReID has been improved significantly. However, latest works in adversarial machine learning have shown the …
-
Person Re-identification And An Adversarial Attack And Defense For Person Re-identification Networks
… captured by smart phones, and are displayed on a map together with the time-stamps. </p><p>With the recent advances in deep neural networks (DNN), the state-of-the-art performance of person ReID has been improved significantly. However, latest works in adversarial machine learning have shown the …
-
Towards scalable and specialized application error analysis
… at different target granularities in CNNs (feature map level and inference level), and show that the combination of both techniques is better than the sum of its parts. Our results show that the specialized, domain-specific error analysis and hardening techniques can achieve very high error …
-
Deep neural networks for medical image super-resolution
… image generator that decomposes the complex mapping problem into simpler sub-problems to avoid over-smoothing the structural information and introducing non-realistic high-frequency textures in super-resolved images. Moreover, it involves a lesion-focused training strategy and an advanced …
Page 1 of 2