# Tag Info

8

well, have you ever seen a rotating barber's poll? It looks like the stripes are moving up (optical flow), but of course the motion of the thing is a rotation in the horizontal plane. The rationale really is that the optical flow is based on how the image changes. And if you just compared two frames from the animation above, you'd clearly say that the blue ...

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Your expression $A=\sqrt{a_x^2+a_y^2+a_z^2}$ calculates the length of the 3-dimensional vector with coordinates $(a_x, a_y, a_z)$. So, it is the magnitude of the acceleration. Your expression $a=\Delta_v/\Delta_t$ also gives magnitude of acceleration, but in a slightly different context. This comes from measuring speed at different times, and then you ...

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You are apparently in the context of no-reference, reference-free or blind image quality assessment. The topic is quite active, and I am not sure people have already a completely accepted framework for that. Multiple distortions may affect images: random noise, compression artifact, static blur, motion blur, etc. They require different metrics (benchmark ...

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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 ...

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Building on the answer by sansuiso, the non-zero flow outside the box is due to the regularization (gradient) term, which causes smearing. This is a known weakness of the classic Horn & Schunck approach. A common remedy is to make the $\lambda$ parameter spatially variable, and allow it to go to zero at edges between (piecewise-constant) motion regions. ...

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It's normal and due to your test setup. Let's try to understand a bit. The algorithm is trying to minimize an objective function defined as: $$F(u, I_0, I) = \int \| I - I_0(u(x)) \|_1 + \lambda \| \nabla u \|_ dx,$$ where $I_0$ and $I$ are the images, $x$ is an image point (ie a pixel) and $I_0(u)$ a shortcut for "$I_0$ as remapped using the optical ...

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It will call your function each time any motion event gets fired. This can affect the timing of css animations responding to sensor input. Try getting the orientation values and updating your transformations from within an interval.

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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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Take the first model and train your model noise matrix $\bf{Q}$ to account for the variable acceleration. A popular model is the Singer model from 1990 (see [1]). [1] Singer, R. A., “Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-6, no. 4, .pp. 473-483, July ...

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Use predictive coding. If your predictor manages to guess current position based on previous recorded positions with reasonable accuracy — you can omit the recording of current position. Same predictor will also help you to reconstruct the original path. What predictor you should use is based on the tracked object's nature. You may use a Kalman filter for ...

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Motion can be described using displacement with time, velocity or acceleration. To get these quantities from the accelerometer data the following link may help. http://www.chrobotics.com/library/accel-position-velocity

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Any tiny offset error will cause a double integration to fly off quadratically in time. Even if you manage to calibrate out this error for one sample test setup, any drift in offset due to change in temperature, orientation, age, power fluctuations, etc., will eventually render that calibration useless. Continuous recalibration (airspeed, compass, optical ...

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