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Found 13 papers, 1 papers with code
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Motivated by the lack of asymmetry in the Riemannian metric of the embedding space, this paper extends the MDS problem to a natural asymmetric generalisation of Riemannian manifolds called Finsler manifolds.
Metric Convolutions: A Unifying Theory to Adaptive Convolutions
By returning to a metric perspective for images, now seen as two-dimensional manifolds equipped with notions of local and geodesic distances, either symmetric (Riemannian metrics) or not (Finsler metrics), we provide a unifying principle: the kernel positions are samples of unit balls of implicit metrics.
A model is worth tens of thousands of examples
Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data.
From Compass and Ruler to Convolution and Nonlinearity: On the Surprising Difficulty of Understanding a Simple CNN Solving a Simple Geometric Estimation Task
Neural networks are omnipresent, but remain poorly understood.
Seeing Things in Random-Dot Videos
Humans possess an intricate and powerful visual system in order to perceive and understand the environing world.
Probabilistic Gathering Of Agents With Simple Sensors
Two types of motion are considered: when no peers are detected behind them, either the agents perform unit jumps forward, or they start to move with unit speed while continuously sensing their back half-plane, and stop whenever another agent appears there.
Multiagent Systems
Probabilistic Pursuits on Graphs
Furthermore, assuming a certain natural probability distribution over the move choices of the agents on the grid (in case there are multiple shortest paths between an agent and its predecessor), the walks converge to the uniform distribution over all shortest paths from $s$ to $t$.
Real-Time Depth Refinement for Specular Objects
The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research.
Postprocessing of Compressed Images via Sequential Denoising
In this work we propose a novel postprocessing technique for compression-artifact reduction.
Depth Perception in Autostereograms: 1/f-Noise is Best
An autostereogram is a single image that encodes depth information that pops out when looking at it.
RGBD-Fusion: Real-Time High Precision Depth Recovery
The popularity of low-cost RGB-D scanners is increasing on a daily basis.
Sparsity Based Methods for Overparameterized Variational Problems
Two complementary approaches have been extensively used in signal and image processing leading to novel results, the sparse representation methodology and the variational strategy.
Motion-Compensated Coding and Frame-Rate Up-Conversion: Models and Analysis
In this paper, we study the effect of frame-rate and compression bit-rate on block-based ME and MC as commonly utilized in inter-frame coding and frame-rate up conversion (FRUC).
