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Full-Text Articles in Engineering
Mouldingnet: Deep-Learning For 3d Object Reconstruction, Tobias Burns, Barak Pearlmutter, John B. Mcdonald
Mouldingnet: Deep-Learning For 3d Object Reconstruction, Tobias Burns, Barak Pearlmutter, John B. Mcdonald
Session 2: Deep Learning for Computer Vision
th the rise of deep neural networks a number of approaches for learning over 3D data have gained popularity. In this paper, we take advantage of one of these approaches, bilateral convolutional layers to propose a novel end-to-end deep auto-encoder architecture to efficiently encode and reconstruct 3D point clouds. Bilateral convolutional layers project the input point cloud onto an even tessellation of a hyperplane in the (d Å1)-dimensional space known as the permutohedral lattice and perform convolutions over this representation. In contrast to existing point cloud based learning approaches, this allows us to learn over the underlying geometry of the …
Deep Cnn Frameworks For Comparison For Malaria Diagnosis, Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain
Deep Cnn Frameworks For Comparison For Malaria Diagnosis, Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain
Session 2: Deep Learning for Computer Vision
Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.
Place Recognition In Challenging Conditions, Saravanabalagi Ramachandran, John Mcdonald
Place Recognition In Challenging Conditions, Saravanabalagi Ramachandran, John Mcdonald
Session 2: Deep Learning for Computer Vision
Place recognition in a visual SLAM system helps build and maintain a map from multiple traversals of the same environment while closing loops to correct drift accumulated over time. Despite the marked success in visual place recognition research over the past decade, it remains a challenging problem in the context of variations caused due to different times of the day, weather, lighting and seasons. In this paper, we address this problem by progressively training convolutional neural networks in a siamese fashion to generate embeddings that encode semantic and visual features for sequence-aligned image pairs taken at different timescales and viewpoints. …
Deep Convolutional Neural Networks For Estimating Lens Distortion Parameters, Sebastian Lutz, Mark Davey, Aljosa Smolic
Deep Convolutional Neural Networks For Estimating Lens Distortion Parameters, Sebastian Lutz, Mark Davey, Aljosa Smolic
Session 2: Deep Learning for Computer Vision
In this paper we present a convolutional neural network (CNN) to predict multiple lens distortion parameters from a single input image. Unlike other methods, our network is suitable to create high resolution output as it directly estimates the parameters from the image which then can be used to rectify even very high resolution input images. As our method it is fully automatic, it is suitable for both casual creatives and professional artists. Our results show that our network accurately predicts the lens distortion parameters of high resolution images and corrects the distortions satisfactory.