41,893 research outputs found
A Computational Study Of The Role Of Spatial Receptive Field Structure In Processing Natural And Non-Natural Scenes
The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory
An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data
Variable and higher pulse repetition frequencies (PRFs) are increasingly
being used to meet the stricter requirements and complexities of current
airborne and spaceborne synthetic aperture radar (SAR) systems associated with
higher resolution and wider area products. POLYPHASE, the proposed resampling
scheme, downsamples and unifies variable PRFs within a single look complex
(SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to
an effective lower PRF. A sparsity condition of the received SAR data ensures
that the uniformly resampled data approximates the spectral properties of a
decimated densely sampled version of the received SAR data. While experiments
conducted with both synthetically generated and real airborne SAR data show
that POLYPHASE retains comparable performance to the state-of-the-art BLUI
scheme in image quality, a polyphase filter-based implementation of POLYPHASE
offers significant computational savings for arbitrary (not necessarily
periodic) input PRF variations, thus allowing fully on-board, in-place, and
real-time implementation
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Real-life control tasks involve matters of various substances---rigid or soft
bodies, liquid, gas---each with distinct physical behaviors. This poses
challenges to traditional rigid-body physics engines. Particle-based simulators
have been developed to model the dynamics of these complex scenes; however,
relying on approximation techniques, their simulation often deviates from
real-world physics, especially in the long term. In this paper, we propose to
learn a particle-based simulator for complex control tasks. Combining learning
with particle-based systems brings in two major benefits: first, the learned
simulator, just like other particle-based systems, acts widely on objects of
different materials; second, the particle-based representation poses strong
inductive bias for learning: particles of the same type have the same dynamics
within. This enables the model to quickly adapt to new environments of unknown
dynamics within a few observations. We demonstrate robots achieving complex
manipulation tasks using the learned simulator, such as manipulating fluids and
deformable foam, with experiments both in simulation and in the real world. Our
study helps lay the foundation for robot learning of dynamic scenes with
particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video:
https://www.youtube.com/watch?v=FrPpP7aW3L
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