Object-based Convolutional Neural Networks (OCNN)
Foreign object debris material recognition based on convolutional neural networks
... 2 Related work Material recognition, a fundamental problem in computer vision, has a wide range of applications. For example, an autonomous vehicle or a mobile robot can make decisions on whether a forthcoming terrain is ...
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Object Tracking in Games using Convolutional Neural Networks
... of object detection as applied in games. We designed a custom convolutional neural network detection model, ...reactively based only on the tracked locations of two ...
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Object Detection from Video Tubelets with Convolutional Neural Networks
... for object tracking and achieved impressive tracking accuracy [36, 22, ...an object-specific tracker by online selecting the most influential features from an ImageNet pre-trained CNN, which outperforms ...
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Real Time Object Detection for Unmanned Aerial Vehicles based on Cloud based Convolutional Neural Networks
... use Convolutional Neural Networks to allow UAVs to detect hundreds of object cat- ...state-of-the-art object detection algorithms, despite their very large computational ...cloud- ...
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Vehicle Detection Based on Convolutional Neural Networks
... recent object detection ...same neural network. Unlike region proposal-based networks YOLO uses the whole image instead of separate regions to make ...is based on GoogleLeNet where ...
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Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
... deep neural network with an object-based postprocessing, which ranked third in the second LiTS round at MICCAI ...The object-based analysis step using hand-crafted features allowed for ...
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Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition
... 41 𝜆 ∗ = arg min 𝜆∈Ψ 𝑓(𝜆) (3.4) The search is performed based on four basic operations: reflection, expansion, contraction, and shrinkage, as shown in Figure 3.4. Each is associated with a scalar coefficient of α ...
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Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
... 3.3 Concluding Remarks This section finished the tour of all the principal CNN based approaches past, present and future that treat general object detection in the tradi- tional settings. It has allowed to ...
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View and Illumination Invariant Object Classification Based on 3D Color Histogram Using Convolutional Neural Networks
... Introduction Object recognition is an active area of research for the last five decades [1] and efficient recognition of objects under varying illumination conditions is a problem yet to be ...Visual object ...
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Convolutional neural networks for efficient object detection on ultra low-power platforms
... cases based on convolutional neural networks as the fundamental tool to extract features and information out of various raw data ...requires neural network models with small memory ...
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Enhanced Object Detection with Deep Convolutional Neural Networks for Advanced Driving Assistance
... Recently convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered ...large ...
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Survey on Convolutional Neural Networks
... same object occurs in an image at more than one ...an object of interest. A similar DNN based regression approach was used in , however it could not scale up such that it would include multiple ...
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3D Convolutional Neural Network for Object Recognition
... 3D Object recognition is an important task in computer vision ...of convolutional neural networks for object recognition in 2D images, many researchers have designed convolution ...
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Moving object recognition using multi-view three-dimensional convolutional neural networks
... ð8Þ By maximizing the cost function, the MV3D-CNN model learns how to extract spatiotemporal features and fuse the view-related features into a deep FNN structure for the final recognition. The steps in the MV3D-CNN ...
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Deep Kernel based Convolutional Neural Networks for Image Recognition
... PERFORMANCE NEURAL NETWORKS FOR VISUAL OBJECT ...The networks to benchmark datasets are applied for digit recognition (MNIST), 3D object recognition (NORB), and natural images ...
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Cloud-based video analytics using convolutional neural networks.
... is based on con- volutional neural networks whose parameters are optimally tuned for accurate classification of objects from video ...target object with the pre-trained patterns is made which ...
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Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks
... of object-like representations: gen- eralization, transfer to new tasks, and interpretability, among ...a neural network architecture that effectively ad- dresses this large-image, many-object ...
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The representation of object drawings and sketches in deep convolutional neural networks
... of object recognition, deep convolutional neural networks trained on large databases of natural images have recently gained popularity in visual neuroscience [4], due to their - at times - ...
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Cyberbullying Intervention Based on Convolutional Neural Networks
... This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contribu- tions. First, we show that ...
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Quantum-based subgraph convolutional neural networks
... graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quan- tum walks, which we refer to as the quantum-based subgraph ...
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