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Found 8 papers, 1 papers with code
Semi-Synthetic Dataset Augmentation for Application-Specific Gaze Estimation
Although the number of gaze estimation datasets is growing, the application of appearance-based gaze estimation methods is mostly limited to estimating the point of gaze on a screen.
Development of Machine learning algorithms to identify the Cobb angle in adolescents with idiopathic scoliosis based on lumbosacral joint efforts during gait (Case study)
The lumbosacral (L5-S1) joint efforts during six gait cycles of participants were used as features to feed training algorithms.
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration
Current research in convolutional neural networks (CNN) focuses mainly on changing the architecture of the networks, optimizing the hyper-parameters and improving the gradient descent.
Saliency Enhancement using Gradient Domain Edges Merging
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the boundaries of an image and salient object detection which finds the important object in an image.
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks
The objective of this paper is to show that saliency convolutional neural networks (CNN) can be improved by using a Green's function convolution (GFC) to extrapolate edges features into salient regions.
Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution
The objective of this paper is to present a novel fast and robust method of solving the image gradient or Laplacian with minimal error, which can be used for gradient domain editing.
Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism
The objective of this paper is to present a novel convolution kernels, based on principles of electromagnetic potentials and fields, for a general use in computer vision and to demonstrate its usage for shape and stroke analysis.
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications
Hence, it becomes possible to generate a continuous space of probability based only on the edge information, thus bridging the gap between the edge-based methods and the region-based methods.
