# Tag Info

## Hot answers tagged camera

8

I decided to post this answer here because a while back, this came up as the top result in Google and its suggestions helped me. So I decided to share my experience too. Having spent countless hours trying to get the best stereo calibration on a Kinect, I shared my tips and findings in a blog post here. Although it is geared towards stereo calibration and ...

7

No, the data in the USB cable is digital. Namely there is either an error in the data or the data is the same at any place along the cable.

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Here is a list of 'best practices' for camera calibration which I originally posted here: https://calib.io/blogs/knowledge-base/calibration-best-practices Choose the right size calibration target. Large enough to properly constrain parameters. Preferably it should cover approx. half of the total area when seen fronto-parallel in the camera images. Perform ...

4

Here you have two options. 1) Color calibrate your cameras (radiometric calibration) to the same reference. Then try matching. There are many academic works on this topic, one of them being: http://research.microsoft.com/en-us/um/people/yasumat/papers/rankcalib_PAMI12_preprint.pdf 2) Use a vignetting invariant (or color invariant) feature descriptor. For ...

4

If you know the exact response of the camera, you can convert the brightness samples of each pixel to a linear intensity scale and perform the averaging there. That will make your whole problem intensity linear and should solve all your problems. However, I would strongly recommend using a more advanced exposure algorithm. For example you could introduce a ...

4

This is due to the optimization problem being rather high-dimensional (around 11 parameters). With only a single observation of the calibration board, there would be multiple possible combinations of parameters explaining the observed feature point locations (unless a very constrained camera-model is used). Only a sufficient number of sufficiently ...

3

No, it is not a good idea. Typically, you want to limit the volume of space relative to the camera in which you want to do your measurements, and you want to use a single calibration target appropriate for that volume. In theory, you can calibrate with any number of different patterns. In practice, however, most implementations assume that the pattern is ...

2

If X are known 3D points, then the translation is given simply by: $$T=\frac{\sum_{i=1}^{n}{X'_{i}-RX_{i}}}{n}$$ In case of points being in projected coordinates, then more work have to be done. You can express the 3D points in homogenous coordinates: $$X=(x, y, z, w)$$ where $w$ is 1 by default. Then both the translation and rotation can be joined via ...

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http://r0k.us/graphics/kodak/ does link to Kodak PhotoCD contents with 2048x3072 images. It is not what is called "raw" as those are using 4:2:0 subsampling (so not lossless at all) and xvYCC transfer function (almost like BT.709 gamma). Those were indeed produced by scanning KODACOLOR Gold 100 (35 mm) film and some other types of 35 mm, all types ...

2

The characteristics of the shadow are as follows: It is always dark regardless of the color of the object or the color of the light used to make a shadow It only shows a dark outline of the object It is formed in the opposite direction to the source of light The size of the shadow depends upon the distance between the source of light and the opaque object ...

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Shadow has very specific properties that makes it very clear way of making it distinguishable from the regular object. A lot of work in the area of background subtraction and surveillance has been using this to eliminate the shadows or to avoid them being mistaken as the actual object (or human). As observed by Daniel Grest To distinguish the shadows ...

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The edges of the shadows are crisper where they are nearer to the shadow caster. Also, since the sun is so far away, beams of light are essentially parallel when they reach us, which means that shadows from sunlight are basically orthographic projections of the shadow casters. Also, if you knew an objects dimensions and saw its shadow on something that you ...

2

You are correct: to calibrate a camera you need a correspondence between 3D world points and 2D image points. The problem is that the 3D points cannot be co-planar, so people were building 3D calibration rigs, e. g. a box made of checkerboards. One image of a rig like that would be enough to calibrate, but those rigs are hard to build, because you have to ...

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Yes, the center pixel and focal length in pixels will change, as described in the link above. However, if you learn distortion parameters (radial and tangential) then they shouldn't change as resolution changes because they operate on the projective image plane (before multiplying by camera matrix) instead of pixel coordinates (after multiplying by camera ...

2

Try reducing exposure time if your camera allows that. With the short exposure time the conveyor belt should be well-lit. If that is not enough, a common solution is to use a flash which is synchronized to the camera. If a sync signal is not readily available from the camera and you can't trigger the camera with your own sync signal, you probably could find ...

2

There is no matrix that maps a pixel in camera 1 to the corresponding pixel in camera 2. This is because the location of the corresponding pixel depends on the 3-D location of the corresponding point in the world. What you have instead is the Fundamental matrix, which maps a pixel in camera 1 to a line in camera 2, called the epipolar line.

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There is a lot of variability because distortion can be compensated for optically and lens designs differ. Some lenses are marketed as rectilinear, most not. You would be making the distortion worse for some lenses by doing any correction advised by focal length alone. For focal lengths shorter than about 30 mm, the distortion seems dominantly barrel, ...

2

What could cause such a shape? Could it be due to the laser source which could not be perfectly single-mode? Or could it be due to the sensor? There is nothing particularly wrong with the "shape", but there are a few things you can do on the sensor and data processing side, to improve the extraction of an accurate profile. Your "biggest" problem is ...

2

This is a long topic to fully explain. I will try to write shortly, so please excuse the brevity. Standard computer vision projection (ignoring distortion like Houdini) follows: $$\mathbf{x} = \lambda \mathbf{K}[\mathbf{R}\mathbf{X} +\mathbf{t} ]$$ $\mathbf{R}$ is a $3x3$ orthogonal matrix, $\mathbf{t}$ is a $3x1$ translation vector. Camera position $\... 2 If I understand you correctly, what you are asking for is called camera response function (CRF) and in general it is nonlinear and depends on camera device. For fixed device denote its CRF by$f$. Then$f$maps the set of possible scene irradiances (or illuminances)$\mathcal{I}$at a given spatial location to the set of possible pixel intensity values$\...

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At the time when the original question was asked, there was no hardware technology to physically construct an oversampled binary image sensor. Two years later - Technology is now slowly moving towards the construction of an array of millions (or billions) of sub-diffraction limit sized pixels that are required to realize such an oversampled binary sensor in ...

1

The success of your camera depends on too many factors! and they're out of the scope of this page. If the dynamic range requirements of your application are not too demanding then you could take advantage of the fast sampling rate and try to reconstruct a low quality image (by the same principle by which sigma delta modulation works, eg reconstructing a PCM ...

1

The term you are looking for is Optical Flow and it is a very actively researched field along with (the also relevant here) Simultaneous Localisation and Mapping or SLAM Getting a robust velocity estimate from one or more cameras is not going to be "easy" as a lot of processing needs to be carried out to reduce uncertainties from a number of factors that ...

1

It's possible, but it takes some sophisticated processing to do it effectively in a general environment. First of all, you can only detect motion relative to what you can see in the field of view. This requires good foreground/background separation to eliminate objects that might themselves be moving relative to the background (which is presumably what you ...

1

You usually don't use webcams for machine vision applications, but an industrial-grade camera. You need a shorter exposure time. You need to make sure that during exposure the part moves by less than one pixel or so. Reducing exposure time will reduce the amount of light, which you will need to compensate by increasing the aperture, as long as possible ...

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So here are the answers to the questions: It's a good question. First, your calibration grid should somehow be "coded". For example, the OpenCV checkerboard pattern is a rectangle and the points are sorted from upper left to lower right. This way, you find the exact correspondences between your 3D model and 2D points. For multiple views, the origin doesn'...

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Your linearization gamma calculation does not take into account the correction matrix. You could do a combined numerical optimization of the pre-correction-matrix gamma adjustment, the correction matrix, and the post-correction-matrix gamma adjustment, preferably using a colorimetric error metric. There are 11 variables to optimize (or up to 14 if you wand ...

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Shadows, despite what I have always assumed, are black or nearly so. In my studies with the LED light and the digital camera, I have discovered that the shadow, is, in fact, the color of the surface onto which is is found. Experimentally, I have, on a pure white ground that is lit with a rather weak, warm, ambient light of around 5000K, placed a subject, ...

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More formally: 1) The saturation component of the shadow should be low. 2) Under the Lambertian assumption, an input image I arises from a product of two intrinsic images: the reflectance image and the illumination image. Invariance to illuminant colour and intensity means that such images are free of shadows, as well, to a good degree. Since shadows are ...

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