I want to calculate the DRI ranges of a camera using it's resolution, focal length , sensor pixel pitch and target real size. I have these values but I didn't find any equations to calculate DRI range values.
for example:
I have a camera with the below specifications
Resolution : 640x480;
Focal length : 70 mm;
sensor pixel pitch : 12 um;
Target size : 0.5 meter x 0.5 meter (width x height);
approx. DRI ranges for the above camera is:
Detection : 1940 meters;
Recognition : 490 meters;
Identification : 240 meters;
**the above values are generated using online DRI calculators
Thanks in advance.
Related
At first blush this presumably means -
(1) looking only at lower IR frequencies,
(2) select a IR frequency cut-off for low frequency buckets of the u/v FFT grid
(3) Once we have that, derive the power distribution - squares of amplitudes - for that IR range of frequency buckets the camera supports.
(4) Fit that distribution against the Rayleigh-Jones classical Black Box radiation formula:
(https://en.wikipedia.org/wiki/Rayleigh%E2%80%93Jeans_law#Other_forms_of_Rayleigh%E2%80%93Jeans_law)
(5) Assign a Temperature of 'best fit'.
The units for B(ν,T) are Power per unit frequency per unit surface area at equilibrium Temperature
Of course, this leaves many details out, such as (6) cancelling background, etc, but one could perhaps use the opposite facing camera to assist in that. Where buckets do not straddle the temperature of interest, (7) use a one-sided distribution to derive an inferred Gaussian curve to fit the Rayleigh-Jeans curve at that derived central frequency ν, for measured temperature T.
Finally (8) check if this procedure can consistently detect a high vs low surface temperature (9) check if it can consistently identify a 'fever' temperature (say, 101 Fahrenheit / 38 Celcius) pointing at a forehead.
If all that can be done, (10) Voila! a body fever detector
So those who are capable can fill us in on whether this is possible to do so for eventual posting at an app store as a free Covid19 safe body temperature app? I have a strong sense there's quite a few out there who can verify this in a week or two!
It appears that the analog signal assumed in (1) and (2) are not available in the Android digital Camera2 interface.
Android RAW image stream, that is uncompressed YUV, is already encoded Y green monochrome, and U,V are blue and red shifts from zero for converting green monochrome to color.
The original analog frequency / energy signal is not immediately accessible. So adaptation is not possible (yet).
I am trying to implement an OCR / OCV algorithm for inspecting printed text in black ink on a white background. The text size is ranging from 3 pt. to 6 pt. I tried first to capture images with a 5 MP monochrome camera using an 8 mm, 12 mm and 16 mm lens but I could not get the characters with good clarity. I repeated the same test with 10 MP camera also considering that higher pixel depth will give more information but I got same results.
I'm not sure, how I can get a clearer image. Whether a 5 MP / 10 MP is enough and if there is any way to determine the lens to be used in such application.
The FOV for inspection is about 300 x 250 mm and the working distance I considered from approx. 400 mm to 650 mm.
Due to copyright concerns I cannot post the image of the object under inspection.
Any help or direction is greatly appreciated. Thanks.
It's simple geometry. It is:
3pt =~ 1mm.
Assuming you want to have 10 pixels to cover each character, your IFOV needs to be:
IFOV =~ (font_width / 10) / distance = 0.1 / 650 =~ 0.15 milliradians / pixel.
For the work area width you mention, the horizontal field of view is:
FOV = 2 * atan((300 / 2) / 650) =~ 453 milliradians =~ 26 deg
So the minimal (horizontal) sensor resolution you'd need is:
Width = 453 / 0.15 = 3020 pixels.
Thus a 10MP sensor should be quite sufficient, and 5MP one may be adequate.
To choose the lens, from the above spec for the FOV, and the format (width, height) of your choice of sensor, you can work out by the same simple trigonometry the needed focal length. Finally, among all lenses matching that focal length that are available for your camera mount, you need to choose one that (a) can be focused at the distance of interest and (b) has an adequate Optical Transfer Function such that one line can be resolved at the above IFOV.
In practice, after running the math and looking at catalogs, you'll end up with several candidate lenses. My advice would then be to get samples and try them out on your on setup, and specifically with the particular lighting rig you'll be using, before making a final decision. Depending on your particular project, factors influencing the choice, in addition (obviously) to the cost of the lens + sensor combination, may be size/weight, sensitivity to environment conditions (temperature, humidity, vibrations), availability and lead time for sourcing, etc.
I am using NI PCI-1411 frame brabber card and the signal is RS-170 signal. According to the discussion on the website:https://www.cs.rochester.edu/~nelson/courses/vision/resources/video_signals.html.
It said"The aspect (width to height) ratio for typical RS-170 signal rectangle is 4:3. The vertical resolution of video is limited to 485 pixels, as determined by the number of scan lines. The RS-170 standard specifies the aspect ratio (ratio of vertical/horizontal dimensions) of the video display as 3:4"
The CCD I use is Hitachi KP-M1AN.Its number of pixels are 768(H)*494(V) and the pixels size is 11.64(um) * 13.5(um),sensing area is 8.91*6.67mm,Horizontal/Vertical TV resolution is 570/485
Here are my questions:
1.Now I gave a square object to CCD (The CCD is Hitachi KP-M1AN.CCD pixels size is 11.64(um)*13.5(um) and the ).According to the pixel size on CCD, the horizontal pixel numbers should be different with vertical pixel numbers.however, I notice the pixel numbers at both directions are same. So I wonder what is the "real" pixels size now after the ratio change ?
For example, here is a square object is 143um*143um and CCD pixel size is 11um*13um.And according to pixel size it must be 13 pixels * 11 pixels. But what I see now is 12 pixels * 12 pixels.
2.According to question 1, if the pixels size change owning to the ratio change.How does the NI software change the pixel size (I mean extend it or compress it.)
I need some help trying to figure out the Volume Voxel Per Meter and Resolution settings in Kinect Fusion...mostly how, and if at all, they interact with Depth Threshold settings in the Kinect Fusion Explorer program please...because I don't get if the depth threshold minimum is increased and maximum is reduced, does that smaller range increases the overall precision of the scanned volume, or does it stay the same?
Say I set the Kinect Fusion's depth threshold minimum to 2m and the maximum to 3m, thus setting the scanned range to 3m-2m=1m, does then the volume voxels per meter setting of say 256 and a resolution of also 256 mean that I would get a voxel depth precision of 1m/256=0.003m=0.3cm (a third of a centimeter)? Or is the resolution applicable only to the complete Kinect depth range instead of the one set via depth threshold? Also, how's width and height affected by depth threshold settings, and how to calculate precision in those two remaining axis?
Thanks in advance
P.S.
If the volume voxel resolution is set to maximum for all three axis (768x768x768) what is the minimum amount of GPU memory needed to make Kinect Fusion work?
Answering an old topic; because there is no other answer:
A. Simple Answer:
Depth threshold settings simply decide what region of the depth map are you interested in. Any value below min depth threshold and above max depth threshold is simply replaced with 0 during depth map generation.
B. Detailed Answer:
Volume Voxel per meter: This is the mm value depth represented by a single voxels . So 1000mm/256 (voxel_per_meter) = ~3.9 mm/voxel
( See:PCL documentation )
Voxel Resolution: The number of voxels in the volume you are constructing.
So;
Voxel Resolution / Voxel per m = Volume of Reconstruction volume (in meters)
EG: 512 voxels / 256 vpm = 2.0m (The volume of the reconstruction cube, given that the number of voxels per side of the cube are the same - each axis can be independently defined.)
If you have the Kinect SDK installed; see the descriptions of the following variables:
minDepthClip = FusionDepthProcessor.DefaultMinimumDepth;
maxDepthClip = FusionDepthProcessor.DefaultMaximumDepth;
voxelsPerMeter; voxelsX; voxelsY; voxelsZ;
So; these values are not dependent (or vice versa) on the depth threshold value.
A good example of using the depth threshold values is in the great video by Daniel Shiffman ([Kinect & Processing])
I am currently trying to figure out a way to calcute the size of a given object with kinect
since I have the following data
angular field of view of the lens
distance
and width in pixels from a 800*600 resolution
I believe this can be possible to calculate. Does anyone has math skills to give me a little help?
With some trigonometry, it should be possible to approximate.
If you draw a right trangle ABC, with the camera at one of the legs (A), and the object at the far end (edge BC), where the right angle is (C), then the height of the object is going to be the height of leg BC. the distance to the pixel might be the distance of leg AC or AB. The Kinect sensor specifications are going to regulate that. If you get distance to the center of a pixel, then it will be AC. if you have distances to pixel corners then the distance will be AB.
With A representing the angle at the camera that the pixel takes up, d is the distance of the hypotenuse of a right angle and y is the distance of the far leg (edge BC):
sin(A) = y / d
y = d sin(A)
y is the length of the pixel projected into the object plane. You calculate it by multiplying the sin of the angel by the distance to the object.
Here I confess I do not know the API of the kinect, and what level of detail it provides. You say you have the angle of the field of vision. You might assume each pixel of your 800x600 pixel grid takes up an equal angle of your camera's field of vision. If you do, then you can break up that field of vision into equal pieces to measure the linear size of your object in each pixel.
You also mentioned that you have the distance to the object. I was assuming that you have a distance map for each pixel of the 800x600 grid. If this is incorrect, some calculations can be done to approximate a distance grid for the pixels involving the object of interest if you make some assumptions about the object being measured.