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National Repository of Grey Literature 3 records found  Search took 0.00 seconds. 
Multi-Modal Restricted Boltzmann Machines
 Svoboda, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This thesis explores how multi-modal Restricted Boltzmann Machines (RBM) can be used in content-based image tagging. This work also cointains brief analysis of modalities that can be used for multi-modal classification. There are also described various RBMs, that are suitable for different kinds of input data. A design and implementation of multimodal RBM is described together with results of preliminary experiments.
Detailed record
Evaluation Of The Neural Network Object Detection In Multi-Modal Images
 Ligocki, Adam
This paper studies the information gain of various data domains that are commonly usedin the modern Advanced Driving Assistant Systems (ADAS) to develop robust systems that wouldincrease traffic safety. We could see a fast growth of many Deep Convolutional Neural Networks(DCNN) based solutions during the last several years. These methods are state-of-the-art in objectdetection and semantic scene segmentation. We created a small annotated dataset of synchronizedRGB, grayscale, thermal, and depth map images and used the modern DCNN framework tool toevaluate the object detection robustness of different data domains and their information gain processunderstanding the surrounding environment of the semi-autonomous driving agent.
Detailed record
Multi-Modal Restricted Boltzmann Machines
 Svoboda, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This thesis explores how multi-modal Restricted Boltzmann Machines (RBM) can be used in content-based image tagging. This work also cointains brief analysis of modalities that can be used for multi-modal classification. There are also described various RBMs, that are suitable for different kinds of input data. A design and implementation of multimodal RBM is described together with results of preliminary experiments.
Detailed record

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