320 research outputs found
Neural Spectro-polarimetric Fields
Modeling the spatial radiance distribution of light rays in a scene has been
extensively explored for applications, including view synthesis. Spectrum and
polarization, the wave properties of light, are often neglected due to their
integration into three RGB spectral bands and their non-perceptibility to human
vision. Despite this, these properties encompass substantial material and
geometric information about a scene. In this work, we propose to model
spectro-polarimetric fields, the spatial Stokes-vector distribution of any
light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric
Fields (NeSpoF), a neural representation that models the physically-valid
Stokes vector at given continuous variables of position, direction, and
wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory
efficiency, and preserves physically vital signals, factors that are crucial
for representing the high-dimensional signal of a spectro-polarimetric field.
To validate NeSpoF, we introduce the first multi-view
hyperspectral-polarimetric image dataset, comprised of both synthetic and
real-world scenes. These were captured using our compact
hyperspectral-polarimetric imaging system, which has been calibrated for
robustness against system imperfections. We demonstrate the capabilities of
NeSpoF on diverse scenes
UGPNet: Universal Generative Prior for Image Restoration
Recent image restoration methods can be broadly categorized into two classes:
(1) regression methods that recover the rough structure of the original image
without synthesizing high-frequency details and (2) generative methods that
synthesize perceptually-realistic high-frequency details even though the
resulting image deviates from the original structure of the input. While both
directions have been extensively studied in isolation, merging their benefits
with a single framework has been rarely studied. In this paper, we propose
UGPNet, a universal image restoration framework that can effectively achieve
the benefits of both approaches by simply adopting a pair of an existing
regression model and a generative model. UGPNet first restores the image
structure of a degraded input using a regression model and synthesizes a
perceptually-realistic image with a generative model on top of the regressed
output. UGPNet then combines the regressed output and the synthesized output,
resulting in a final result that faithfully reconstructs the structure of the
original image in addition to perceptually-realistic textures. Our extensive
experiments on deblurring, denoising, and super-resolution demonstrate that
UGPNet can successfully exploit both regression and generative methods for
high-fidelity image restoration.Comment: Accepted to WACV 202
Neural 360 Structured Light with Learned Metasurfaces
Structured light has proven instrumental in 3D imaging, LiDAR, and
holographic light projection. Metasurfaces, comprised of sub-wavelength-sized
nanostructures, facilitate 180 field-of-view (FoV) structured light,
circumventing the restricted FoV inherent in traditional optics like
diffractive optical elements. However, extant metasurface-facilitated
structured light exhibits sub-optimal performance in downstream tasks, due to
heuristic pattern designs such as periodic dots that do not consider the
objectives of the end application. In this paper, we present neural 360
structured light, driven by learned metasurfaces. We propose a differentiable
framework, that encompasses a computationally-efficient 180 wave
propagation model and a task-specific reconstructor, and exploits both
transmission and reflection channels of the metasurface. Leveraging a
first-order optimizer within our differentiable framework, we optimize the
metasurface design, thereby realizing neural 360 structured light. We
have utilized neural 360 structured light for holographic light
projection and 3D imaging. Specifically, we demonstrate the first 360
light projection of complex patterns, enabled by our propagation model that can
be computationally evaluated 50,000 faster than the Rayleigh-Sommerfeld
propagation. For 3D imaging, we improve depth-estimation accuracy by
5.09 in RMSE compared to the heuristically-designed structured light.
Neural 360 structured light promises robust 360 imaging and
display for robotics, extended-reality systems, and human-computer
interactions
Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset
Image datasets are essential not only in validating existing methods in
computer vision but also in developing new methods. Most existing image
datasets focus on trichromatic intensity images to mimic human vision. However,
polarization and spectrum, the wave properties of light that animals in harsh
environments and with limited brain capacity often rely on, remain
underrepresented in existing datasets. Although spectro-polarimetric datasets
exist, these datasets have insufficient object diversity, limited illumination
conditions, linear-only polarization data, and inadequate image count. Here, we
introduce two spectro-polarimetric datasets: trichromatic Stokes images and
hyperspectral Stokes images. These novel datasets encompass both linear and
circular polarization; they introduce multiple spectral channels; and they
feature a broad selection of real-world scenes. With our dataset in hand, we
analyze the spectro-polarimetric image statistics, develop efficient
representations of such high-dimensional data, and evaluate spectral dependency
of shape-from-polarization methods. As such, the proposed dataset promises a
foundation for data-driven spectro-polarimetric imaging and vision research.
Dataset and code will be publicly available
Self-care use patterns in the UK, US, Australia, and Japan: a multinational web-based survey
AbstractBackgroundThe trend toward patient- or consumer-centered healthcare has been accelerated by advances in technology, consumer empowerment, and a shift from infectious to chronic diseases. The purpose of this study was to examine the growing self-care market by analyzing self-care patterns.MethodsWe conducted a cross-sectional, web-based survey involving adults from nine major cities in the UK, the USA, Australia, and Japan. This study examined the extent and frequency of self-care, self-care expenditure, sources of self-care information, and reasons for self-care in each country.ResultsThe results showed that the prevalence of self-care was highest in Japan (54.9%), followed by the UK (43.1%), the USA (42.5%), and Australia (40.4%). The primary reason for practicing self-care was “to manage my healthcare myself” (cited by 45.7%, 59.5%, 49.2%, and 4.1% of participants in Australia, Japan, the UK, and the USA, respectively). Significant linear associations were observed between age and the prevalence of self-care in all countries (p<0.05), indicating that self-care prevalence decreased with age in the UK, the USA, and Australia, and increased with age in Japan. The frequency with which self-care was practiced was positively correlated with age in the USA (p<0.05), Australia (p<0.01), and Japan (p<0.05). In addition to acquaintances, internet search engines and information obtained from pharmacies were considered reliable and widely used sources of self-care information.ConclusionWhen developing self-care products or services, healthcare providers and policymakers should consider self-care patterns
Unsupervised Grouping of Local Components for Object Segmentation
In this paper, we propose a novel object segmentation method for image understanding. Due to challenges such as variations in object size, orientation, illumination etc. object segmentation is extraordinarily difficult task in the domain of image understanding. It is well-founded concept that a small portion of the pixel set in an image contributes most in image description. Based on this concept, we hypothesize that an image consists of many components or parts each of which represent a small local area in the image and they are very meaningful in visual perception. For object segmentation, we propose spatial segmentation method on such prototypical components of images. Given an image this segmentation method acts as coarse to fine search for object(s) iteratively. The proposed method demonstrate its excellence in localizing objects in various complex backgrounds, multiple objects in a single image even if they have variation in size, orientation, lighting conditions etc. The detection efficiency of our object detector on our self-collected image set which consists of images from six different object categories climbs up to 93% in average.
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