slowmoVideo is an OpenSource program that creates slow-motion videos from your footage.
Slow motion cinematography is the result of playing back frames for a longer duration than they were exposed. For example, if you expose 240 frames of film in one second, then play them back at 24 fps, the resulting movie is 10 times longer (slower) than the original filmed event….
Film cameras are relatively simple mechanical devices that allow you to crank up the speed to whatever rate the shutter and pull-down mechanism allow. Some film cameras can operate at 2,500 fps or higher (although film shot in these cameras often needs some readjustment in postproduction). Video, on the other hand, is always captured, recorded, and played back at a fixed rate, with a current limit around 60fps. This makes extreme slow motion effects harder to achieve (and less elegant) on video, because slowing down the video results in each frame held still on the screen for a long time, whereas with high-frame-rate film there are plenty of frames to fill the longer durations of time. On video, the slow motion effect is more like a slide show than smooth, continuous motion.
One obvious solution is to shoot film at high speed, then transfer it to video (a case where film still has a clear advantage, sorry George). Another possibility is to cross dissolve or blur from one frame to the next. This adds a smooth transition from one still frame to the next. The blur reduces the sharpness of the image, and compared to slowing down images shot at a high frame rate, this is somewhat of a cheat. However, there isn’t much you can do about it until video can be recorded at much higher rates. Of course, many film cameras can’t shoot at high frame rates either, so the whole super-slow-motion endeavor is somewhat specialized no matter what medium you are using. (There are some high speed digital cameras available now that allow you to capture lots of digital frames directly to your computer, so technology is starting to catch up with film. However, this feature isn’t going to appear in consumer camcorders any time soon.)
Spectralon is a teflon-based pressed powderthat comes closest to being a pure Lambertian diffuse material that reflects 100% of all light. If we take an HDR photograph of the Spectralon alongside the material to be measured, we can derive thediffuse albedo of that material.
The process to capture diffuse reflectance is very similar to the one outlined by Hable.
1. We put a linear polarizing filter in front of the camera lens and a second linear polarizing filterin front of a modeling light or a flash such that the two filters are oriented perpendicular to eachother, i.e. cross polarized.
2. We place Spectralon close to and parallel with the material we are capturing and take brack-eted shots of the setup7. Typically, we’ll take nine photographs, from -4EV to +4EV in 1EVincrements.
3. We convert the bracketed shots to a linear HDR image. We found that many HDR packagesdo not produce an HDR image in which the pixel values are linear. PTGui is an example of apackage which does generate a linear HDR image. At this point, because of the cross polarization,the image is one of surface diffuse response.
4. We open the file in Photoshop and normalize the image by color picking the Spectralon, filling anew layer with that color and setting that layer to “Divide”. This sets the Spectralon to 1 in theimage. All other color values are relative to this so we can consider them as diffuse albedo.
“Unless you have all the relevant spectral measurements, a colour rendition chart should not be used to perform colour-correction of camera imagery but only for white balancing and relative exposure adjustments.”
“Using a colour rendition chart for colour-correction might dramatically increase error if the scene light source spectrum is different from the illuminant used to compute the colour rendition chart’s reference values.”
“other factors make using a colour rendition chart unsuitable for camera calibration:
– Uncontrolled geometry of the colour rendition chart with the incident illumination and the camera.
– Unknown sample reflectances and ageing as the colour of the samples vary with time.
– Low samples count.
– Camera noise and flare.
– Etc…
“Those issues are well understood in the VFX industry, and when receiving plates, we almost exclusively use colour rendition charts to white balance and perform relative exposure adjustments, i.e. plate neutralisation.”
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.
The goals of lighting in 3D computer graphics are more or less the same as those of real world lighting.
Lighting serves a basic function of bringing out, or pushing back the shapes of objects visible from the camera’s view.
It gives a two-dimensional image on the monitor an illusion of the third dimension-depth.
But it does not just stop there. It gives an image its personality, its character. A scene lit in different ways can give a feeling of happiness, of sorrow, of fear etc., and it can do so in dramatic or subtle ways. Along with personality and character, lighting fills a scene with emotion that is directly transmitted to the viewer.
Trying to simulate a real environment in an artificial one can be a daunting task. But even if you make your 3D rendering look absolutely photo-realistic, it doesn’t guarantee that the image carries enough emotion to elicit a “wow” from the people viewing it.
Making 3D renderings photo-realistic can be hard. Putting deep emotions in them can be even harder. However, if you plan out your lighting strategy for the mood and emotion that you want your rendering to express, you make the process easier for yourself.
Each light source can be broken down in to 4 distinct components and analyzed accordingly.
· Intensity
· Direction
· Color
· Size
The overall thrust of this writing is to produce photo-realistic images by applying good lighting techniques.
1 to 100% Stepless Dimming, 1500 Lux Brightness at 3.3′
LCD Info Screen. Powered by an L-series battery, D-Tap, or USB-C
Because the light has a variable color range of 3200 to 9500K, when the light is set to 5500K (daylight balanced) both sets of LEDs are on at full, providing the maximum brightness from this fixture when compared to using the light at 3200 or 9500K.
The LCD screen provides information on the fixture’s output as well as the charge state of the battery. The screen also indicates whether the adjustment knob is controlling brightness or color temperature. To switch from brightness to CCT or CCT to brightness, just apply a short press to the adjustment knob.
The included cold shoe ball joint adapter enables mounting the light to your camera’s accessory shoe via the 1/4″-20 threaded hole on the fixture. In addition, the bottom of the cold shoe foot features a 3/8″-16 threaded hole, and includes a 3/8″-16 to 1/4″-20 reducing bushing.
When collecting hdri make sure the data supports basic metadata, such as:
Iso
Aperture
Exposure time or shutter time
Color temperature
Color space Exposure value (what the sensor receives of the sun intensity in lux)
7+ brackets (with 5 or 6 being the perceived balanced exposure)
In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allow a greater dynamic range of luminances (a Photometry measure of the luminous intensity per unit area of light travelling in a given direction. It describes the amount of light that passes through or is emitted from a particular area, and falls within a given solid angle) between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to represent more accurately the wide range of intensity levels found in real scenes ranging from direct sunlight to faint starlight and to the deepest shadows.
The two main sources of HDR imagery are computer renderings and merging of multiple photographs, which in turn are known as low dynamic range (LDR) or standard dynamic range (SDR) images. Tone Mapping (Look-up) techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Photography
In photography, dynamic range is measured in Exposure Values (in photography, exposure value denotes all combinations of camera shutter speed and relative aperture that give the same exposure. The concept was developed in Germany in the 1950s) differences or stops, between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.
The human response to brightness is well approximated by a Steven’s power law, which over a reasonable range is close to logarithmic, as described by the Weber�Fechner law, which is one reason that logarithmic measures of light intensity are often used as well.
HDR is short for High Dynamic Range. It’s a term used to describe an image which contains a greater exposure range than the “black” to “white” that 8 or 16-bit integer formats (JPEG, TIFF, PNG) can describe. Whereas these Low Dynamic Range images (LDR) can hold perhaps 8 to 10 f-stops of image information, HDR images can describe beyond 30 stops and stored in 32 bit images.
An exposure stop is a unit measurement of Exposure as such it provides a universal linear scale to measure the increase and decrease in light, exposed to the image sensor, due to changes in shutter speed, iso and f-stop.
+-1 stop is a doubling or halving of the amount of light let in when taking a photo
1 EV (exposure value) is just another way to say one stop of exposure change.
Same applies to shutter speed, iso and aperture.
Doubling or halving your shutter speed produces an increase or decrease of 1 stop of exposure.
Doubling or halving your iso speed produces an increase or decrease of 1 stop of exposure.
The intricate relationship between the eyes and the brain, often termed the eye-mind connection, reveals that vision is predominantly a cognitive process. This understanding has profound implications for fields such as design, where capturing and maintaining attention is paramount. This essay delves into the nuances of visual perception, the brain’s role in interpreting visual data, and how this knowledge can be applied to effective design strategies.
This cognitive aspect of vision is evident in phenomena such as optical illusions, where the brain interprets visual information in a way that contradicts physical reality. These illusions underscore that what we “see” is not merely a direct recording of the external world but a constructed experience shaped by cognitive processes.
Understanding the cognitive nature of vision is crucial for effective design. Designers must consider how the brain processes visual information to create compelling and engaging visuals. This involves several key principles:
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