174 research outputs found
Fast Poisson blending using multi-splines.
Abstract We present a technique for fast Poisson blending and gradient domain compositing. Instead of using a single piecewise-smooth offset map to perform the blending, we associate a separate map with each input source image. Each individual offset map is itself smoothly varying and can therefore be represented using a low-dimensional spline. The resulting linear system is much smaller than either the original Poisson system or the quadtree spline approximation of a single (unified) offset map. We demonstrate the speed and memory improvements available with our system and apply it to large panoramas. We also show how robustly modeling the multiplicative gain rather than the offset between overlapping images leads to improved results, and how adding a small amount of Laplacian pyramid blending improves the results in areas of inconsistent texture
This is not an apple! Benefits and challenges of applying computer vision to museum collections
The application of computer vision on museum collection data is at an experimental stage with predictions that it will grow in significance and use in the coming years. This research, based on the analysis of five case studies and semi-structured interviews with museum professionals, examined the opportunities and challenges of these technologies, the resources and funding required, and the ethical implications that arise during these initiatives. The case studies examined in this paper are drawn from: The Metropolitan Museum of Art (USA), Princeton University Art Museum (USA), Museum of Modern Art (USA), Harvard Art Museums (USA), Science Museum Group (UK). The research findings highlight the possibilities of computer vision to offer new ways to analyze, describe and present museum collections. However, their actual implementation on digital products is currently very limited due to the lack of resources and the inaccuracies created by algorithms. This research adds to the rapidly evolving field of computer vision within the museum sector and provides recommendations to operationalize the usage of these technologies, increase the transparency on their application, create ethics playbooks to manage potential bias and collaborate across the museum sector
Accidental Light Probes
Recovering lighting in a scene from a single image is a fundamental problem
in computer vision. While a mirror ball light probe can capture omnidirectional
lighting, light probes are generally unavailable in everyday images. In this
work, we study recovering lighting from accidental light probes (ALPs) --
common, shiny objects like Coke cans, which often accidentally appear in daily
scenes. We propose a physically-based approach to model ALPs and estimate
lighting from their appearances in single images. The main idea is to model the
appearance of ALPs by photogrammetrically principled shading and to invert this
process via differentiable rendering to recover incidental illumination. We
demonstrate that we can put an ALP into a scene to allow high-fidelity lighting
estimation. Our model can also recover lighting for existing images that happen
to contain an ALP.Comment: CVPR2023. Project website: https://kovenyu.com/ALP
Image Restoration by Matching Gradient Distributions
The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators
Complexity of Discrete Energy Minimization Problems
Discrete energy minimization is widely-used in computer vision and machine
learning for problems such as MAP inference in graphical models. The problem,
in general, is notoriously intractable, and finding the global optimal solution
is known to be NP-hard. However, is it possible to approximate this problem
with a reasonable ratio bound on the solution quality in polynomial time? We
show in this paper that the answer is no. Specifically, we show that general
energy minimization, even in the 2-label pairwise case, and planar energy
minimization with three or more labels are exp-APX-complete. This finding rules
out the existence of any approximation algorithm with a sub-exponential
approximation ratio in the input size for these two problems, including
constant factor approximations. Moreover, we collect and review the
computational complexity of several subclass problems and arrange them on a
complexity scale consisting of three major complexity classes -- PO, APX, and
exp-APX, corresponding to problems that are solvable, approximable, and
inapproximable in polynomial time. Problems in the first two complexity classes
can serve as alternative tractable formulations to the inapproximable ones.
This paper can help vision researchers to select an appropriate model for an
application or guide them in designing new algorithms.Comment: ECCV'16 accepte
- …