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 42 for “"object classification"”.
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Exploring social tagging graph for web object classification
We study web object classification problem with the novel exploration of social tags. Automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing, browsing, searching, and mining these objects. The explosive growth of heterogeneous …
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A system for unsupervised color based object classification
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Improving Object Classification in X-ray Luggage Inspection
… method to remove thickness effect of objects has been developed to improve the system performance. The back scattering and forward scattering signals are functions of solid angles between the object and detectors. A given object may be randomly placed anywhere on the conveyor belt, …
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Continual Learning of Object Classification in the Real World
… have brought remarkable performance in the object classification task but only when all the training data of classes to be learned are available at the same time. However, real-world data continually evolve through time, resulting in ever-changing learning configurations, e.g., new classes …
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A data-driven approach to object classification through fog
Identifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images …
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H3DNET: A Deep Learning Framework for Hierarchical 3D Object Classification
… the fields such as speech recognition and image classification because of the ability to learn multiple levels of features from raw data. However, 3D deep learning is relatively new but in high demand with their great research values. Current research and usage of deep learning for 3D data suffer …
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Image segmentation and object classification for automatic detection of tuberculosis in sputum smears
… image segmentation methods were compared and object classification was implemented using various two-class classifiers, for images obtained using a microscope with 100x objective lens magnification. The bacillus identification route established for the 100x images, was applied to images …
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Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM
… toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and …
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A search for supersymmetry with the ATLAS detector, and the use of machine learning techniques for object classification in high energy physics
… nets on calorimeter data for particle-type classification, particle energy regression, and shower generation.
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Addressing Occlusion in Panoptic Segmentation
… occlusion. In this work, we propose a novel object classification method based on compositional modeling and explore its effect in the context of the newly introduced panoptic segmentation task. The panoptic segmentation task combines both semantic and instance segmentation to perform …
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Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles
… and disturbances in buildings and dynamic object classification for perception in autonomous vehicles. Part one of this study proposes a general, scalable method to learn controloriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive …
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Interpretable neural networks via alignment and dpstribution Propagation
… the one-shot setting, unsupervised learning, and classification with missing data. The first setting of limited data that we tackle is when there are only few examples per object type. During object classification, an attention mechanism can be used to highlight the area of the image that the …
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Towards practical neural network meta-modeling
… for sequential decision problems. On the task of object classification, the Q-learning agent outperforms all human crafted models that are similar to those in the search space. By analysing the underlying weights of the agent, we are also able to uncover some of the design principles that the …
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Classification of Marine Vessels in a Littoral Environment Using a Novel Training Database
<p>Research into object classification has led to the creation of hundreds of databases for use as training sets in object classification algorithms. Datasets made up of thousands of cars, people, boats, faces and everyday objects exist for general classification techniques. However, no …
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Learning distributions of transformations from small datasets for applied image synthesis
… augmentation method for improving few-shot object classification performance, using a new dataset of collectible cards with fine-grained differences. We also apply our method to medical image segmentation, enabling the training of a supervised segmentation system using just a single labeled …
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Design of a Co-Orbital Threat Identification System
… The design is comprised of the development of a classification hierarchy and the selection of machine learning models that will enable the identification of anomalous object behavior. The hierarchy is based on previous examples applied to object classification while reconsidering the assumption …
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Learning from minimally labeled data with accelerated convolutional neural networks
<p>The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. …
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Comparing visual features for morphing based recognition
This thesis presents a method of object classification using the idea of deformable shape matching. Three types of visual features, geometric blur, C1 and SIFT, are used to generate feature descriptors. These feature descriptors are then used to find point correspondences between pairs of images. …
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Probabilistic models for multi-view semi-supervised learning and coding
This thesis investigates the problem of classification from multiple noisy sensors or modalities. Examples include speech and gesture interfaces and multi-camera distributed sensor networks. Reliable recognition in such settings hinges upon the ability to learn accurate classification models in the …
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An automated vision system for detection and counting of uneaten food pellets in a fish sea cage
… image preprocessing is done, followed by object detection, object classification, and object tracking and counting. Original algorithms were developed for this project to automatically threshold images, track objects in consecutive frames, and count the objects entering the view area of …
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