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Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision.

We introduce deep transform learning – a new
tool for deep learning. Deeper representation is learnt by
stacking one transform after another. The learning proceeds in
a greedy way. The first layer learns the transform and features
from the input training samples. Subsequent layers use the
features (after activation) from the previous layers as training
input. Experiments have been carried out with other deep
representation learning tools – deep dictionary learning,
stacked denoising autoencoder, deep belief network and PCANet

A linear, or 1D, camera is a type of camera that sweeps a linear sensor array over the scene, rather than capturing the scene using a single impression on a 2D sensor array.
They are often used in satellite imagery, industrial inspection, or hyperspectral imaging.
In satellite imaging calibration is often done through a collection of ground points for which the 3D locations are known.
In other applications, e.g. hyperspectral imaging, such known points are not available and annotating many different points is onerous.

Large scale images allow pathologists to perform reviews, using
computer workstations instead of microscopes. This trend raises
a wide range of issues related to the management of these massive
datasets. In particular, efficient solutions for data storage and processing
have to be developed in order to deliver increasingly reliable
and faster analyses. In addition, the improvement of workflows also
requires the reinforcement of visualization capabilities. In this paper,
we present a new virtual microscopy (VM) approach for interactivetime

We target at solving the problem of automatic image segmentation. Although 1D contour and 2D surface cues have been widely utilized in existing work, 3D depth information of an image, a necessary cue according to human visual perception, is however overlooked in automatic image segmentation. In this paper, we study how to fully utilize 1D contour, 2D surface, and 3D depth cues for image segmentation. First, three elementary segmentation modules are developed for these cues respectively.

We target at solving the problem of automatic image segmentation. Although 1D contour and 2D surface cues have been widely utilized in existing work, 3D depth information of an image, a necessary cue according to human visual perception, is however overlooked in automatic image segmentation. In this paper, we study how to fully utilize 1D contour, 2D surface, and 3D depth cues for image segmentation. First, three elementary segmentation modules are developed for these cues respectively.

The binary partition tree (BPT) is a hierarchical data-structure that
models the content of an image in a multiscale way. In particular,
a cut of the BPT of an image provides a segmentation, as a partition
of the image support. Actually, building a BPT allows for
dramatically reducing the search space for segmentation purposes,
based on intrinsic (image signal) and extrinsic (construction metric)
information. A large literature has been devoted to the construction
on such metrics, and the associated choice of criteria (spectral, spatial,

We introduce a novel functionality for wavelet-based irregular mesh codecs which allows for prioritizing at the encoding side a region-of-interest (ROI) over a background (BG), and for transmitting the encoded data such that the quality in these regions increases first. This is made possible by appropriately scaling wavelet coefficients. To improve the decoded geometry in the BG, we propose an ROI-aware inverse wavelet transform which only upscales the connectivity in the required regions. Results show clear bitrate and vertex savings.