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In this paper, we propose a hybrid approach combining traditional texture analysis methods with deep learning for the automatic detection and measurement of abdominal contour from 2-D fetal ultrasound images. Following a learning-based procedure for region of interest (ROI) localization to segment the abdominal boundary, we show that convolutional neural networks (CNNs) outperform other state-of-the-art...
Super-resolution of the alpha matte and the foreground object from a video are jointly attempted in this paper. Instead of super-resolving them independently, we treat super-resolution of the matte and foreground in a combined framework, incorporating the matting equation in the image degradation model. We take multiple adjacent frames from a low-resolution video with non-global motion for increasing...
Matting and super-resolution of frames from an image sequence have been studied independently in the literature. We propose a unified formulation to solve both inverse problems by assimilating matting within the super-resolution model. We adopt a multi-frame approach which uses data from adjacent frames to increase the resolution of the matte as well as foreground.
A new approach for image matting is proposed based on the Kalman filter, to extract the matte and original foreground, despite the presence of noise in the observed image. Different filter formulations with a discontinuity-adaptive Markov random field prior are proposed for handling additive white Gaussian noise and film-grain noise.
In this paper, we propose a video matting method with simultaneous noise reduction based on the Unscented Kalman filter (UKF). This recursive approach extracts the alpha mattes and denoised foregrounds from noisy videos, in a unified framework. No assumptions are made about the type of motion of the camera or of the foreground object in the video. Moreover, user-specified trimaps are required only...
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