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Mysterious animation wins best illusion of 2011 – Motion silencing illusion
Read more: Mysterious animation wins best illusion of 2011 – Motion silencing illusionThe 2011 Best Illusion of the Year uses motion to render color changes invisible, and so reveals a quirk in our visual systems that is new to scientists.
https://en.wikipedia.org/wiki/Motion_silencing_illusion
“It is a really beautiful effect, revealing something about how our visual system works that we didn’t know before,” said Daniel Simons, a professor at the University of Illinois, Champaign-Urbana. Simons studies visual cognition, and did not work on this illusion. Before its creation, scientists didn’t know that motion had this effect on perception, Simons said.
A viewer stares at a speck at the center of a ring of colored dots, which continuously change color. When the ring begins to rotate around the speck, the color changes appear to stop. But this is an illusion. For some reason, the motion causes our visual system to ignore the color changes. (You can, however, see the color changes if you follow the rotating circles with your eyes.)
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Photography Basics : Spectral Sensitivity Estimation Without a Camera
Read more: Photography Basics : Spectral Sensitivity Estimation Without a Camerahttps://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/
A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.
To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.
Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.
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Victor Perez – The Color Management Handbook for Visual Effects Artists
Read more: Victor Perez – The Color Management Handbook for Visual Effects ArtistsDigital Color Principles, Color Management Fundamentals & ACES Workflows
LIGHTING
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Convert between light exposure and intensity
Read more: Convert between light exposure and intensityimport math,sys def Exposure2Intensity(exposure): exp = float(exposure) result = math.pow(2,exp) print(result) Exposure2Intensity(0) def Intensity2Exposure(intensity): inarg = float(intensity) if inarg == 0: print("Exposure of zero intensity is undefined.") return if inarg < 1e-323: inarg = max(inarg, 1e-323) print("Exposure of negative intensities is undefined. Clamping to a very small value instead (1e-323)") result = math.log(inarg, 2) print(result) Intensity2Exposure(0.1) -
Open Source Nvidia Omniverse
Read more: Open Source Nvidia Omniverseblogs.nvidia.com/blog/2019/03/18/omniverse-collaboration-platform/
developer.nvidia.com/nvidia-omniverse
An open, Interactive 3D Design Collaboration Platform for Multi-Tool Workflows to simplify studio workflows for real-time graphics.
It supports Pixar’s Universal Scene Description technology for exchanging information about modeling, shading, animation, lighting, visual effects and rendering across multiple applications.
It also supports NVIDIA’s Material Definition Language, which allows artists to exchange information about surface materials across multiple tools.
With Omniverse, artists can see live updates made by other artists working in different applications. They can also see changes reflected in multiple tools at the same time.
For example an artist using Maya with a portal to Omniverse can collaborate with another artist using UE4 and both will see live updates of each others’ changes in their application.
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