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ICIP 2020 is a fully virtual conference. The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website

Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent advances in point cloud representation learning. Point clouds can be interpreted as 2D manifolds in 3D space. Specifically, we fold a 2D grid onto a point cloud and we map attributes from the point cloud onto the folded 2D grid using a novel optimized mapping method. This mapping results in an image, which opens a way to apply existing image processing techniques on point cloud attributes.


Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter.


The acquisition of 3D MRIs is adversely affected by many degrading factors including low spatial resolution and noise. Image enhancement techniques are commonplace, but there are few proposals that address the increase of the spatial resolution and noise removal at the same time. An algorithm to address this vital need is proposed in this presented work. The proposal tiles the 3D image space into parallelepipeds, so that a median filter is applied in each parallelepiped. The results obtained from several such tilings are then combined by a subsequent median computation.


Novel view synthesis is the task of synthesizing an image of an object at an arbitrary viewpoint given one or a few views of the object. The output image of novel view synthesis exhibits a significant structural change from the input. Because of the large change, the skip connections or U-Net architecture, which can sustain the multi-level characteristics of the input images, cannot be directly utilized for the novel view synthesis. In this paper, we investigate several variations of skip connection on two widely used novel view synthesis modules, pixel generation and flow prediction.


Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications.


3D scanners generate irregularly distributed cloud of points in
most of the cases. Dealing with such data, often in the form of
triangular meshes, requires a pre-processing step to regularize
the triangle facets shape and size. In this paper, we propose
CSIOR, a novel mesh regularization technique which is capable
of producing quasi-equilateral triangles, and distinguished
by two novel features, namely, its intrinsic ordered aspect and
its preservation of the geometric texture of the surface (relief


Hyperspectral videos contain images with a large number of light wavelength indexed bands that can facilitate material
identification for object tracking. Most hyperspectral trackers use hand-crafted features rather than deep learning gener-
ated features for image representation due to limited training samples. To fill this gap, this paper introduces a band atten-
tion aware ensemble network (BAE-Net) for deep hyperspectral object tracking, which takes advantages of deep models