5

Though the Matched Filter is the best tool detection of a known signals under AWGN it should work well here as well. To say something about the probabilities the question is, do you know something about the energy of the received signals? If you do, you should easily say something about the probabilities. Pay attention that if the assumption is a signal ...


4

If your templates are all based on some kind of text you may use some kind of OCR to match the text itself and not only by features. Regarding features, you may read: A Comparative Analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK. Specifically have aloo at the sections: It seems your feature extractor usually use corners while you need more general ...


4

I suspect your problem occurs due to some scaling issues. Basically you need to normalize your research image to the pattern template by subtracting the mean value of the template. And it is better calculate the ratio of correlation to the standard deviation of both images. I don't know which programming language you are using. I wrote a Matlab code for you ...


4

This would be a cumbersome way to detect heart beats (or the QRS complex), if that is what you are trying to do ultimately. A little bit about what you are trying to do currently: Your observations are correct and to these I would like to add that no two heart beats are the same and therefore, strictly speaking, your template will be aligning just with ...


4

I have a PRN generator that I have validated with live captured signals that is available on the Mathworks Exchange site at this address and equally runs in Octave (Update: I also pasted the core of this in a code block below): https://www.mathworks.com/matlabcentral/fileexchange/14670-gps-c-a-code-generator The two tap coder is as given in the diagram in ...


3

What's the maximum residual frequency offset allowable when doing the despreading of the CA code? 500 Hz? Or, what's the SNR loss due as a function of frequency missmatch due to residual Doppler? The maximum frequency offset is dependent on SNR required for acquisition as the roll-off of correlation versus frequency offset is due entirely to the duration of ...


3

If your template or kernel is small, then straight convolution might be the fastest approach. There's a crossover point when performing convolution in the frequency domain is faster than straight time/spatial domain convolution and it can be hardware dependent, but usually when the kernel (template) approaches 1/4-1/2 the size of the image frequency domain ...


3

Yes, this should be enough for a basic isolated word recognition system. Probably not something for a commercial product, but good enough for a university project or demo... It would be better to ask the user to record a word and match against this, rather than attempt to match against a large database of utterances of the same word by different speakers. ...


2

Normalized is preferred to regular correlation to handle variation in brightness, or intensity, between images if nothing else. Regarding your second comment, the correlation is going to be lower if the shape rotates (unless it happens to be symmetrical in all four directions). There are other features you can consider that are rotation invariant as well. ...


2

I personally like this one. It is nicely designed for occlusions and large displacements. But, recent trends in deep learning lead to better results in optical flow such as this and this.


2

Vladimir: You're trying to detect a certain sample pattern (a certain sequence of sample values) within a longer discrete sequence of input samples. This sounds liked a "matched filter" problem to me. If the pattern of samples you're trying to detect is the samples in the red shaded region of your diagram then performing convolution seems like a smart thing ...


2

A simple count of the pixels where the binary color is matching between the image to be recognized and the images of the ten reference digits might be good enough. For best results, it is advisable to create the reference images using exactly the same method as that for the target image. If it turns out that binarization doesn't work repeatably enough, you ...


2

I have referred to Stage I.D of this tutorial. Hope this helps. http://www.robots.ox.ac.uk/~vgg/practicals/instance-recognition/index.html#stage-id-improving-sift-matching-using-a-geometric-transformation When the features have scale and orientation assigned (e.g. SIFT features have these properties), you can compute similarity transform between each ...


2

There is a big difference: The Hough Transform maps the input space to a parameter space, where the search takes place. This way, the run-time of the algorithm is independent of the degree of the spatial search space. Correlation based methods are rather more brute force in that sense as they search explicitly for all transformations. Of course, there are ...


2

That's hard. I'm not even sure I can do this myself before I actually try. It is definitely not something that can be described in a DSP.SE answer. About canny edge even if it is precise you will just get an image like a line drawing as an output. The point is what's next. There are many things to try the are many theory about pattern matching from the edge ...


1

I believe Haar Cascades(used by Viola-Jones) are inherently scale-invariant. Also severely deprecated by modern Neural Networks, but I know nothing about those. It also doesn't do any OCR - if you need that you would need to run a separate algorithm on the extracted sub-image.


1

As far as I can tell from the graph, the variance of the signal goes up substantially under "oscillation" conditions. So, monitor the variance over a rolling window. High variance indicates oscillation. To choose the window width, consider: if the window is too short, the computed variance will be too noisy if the window is too long, the monitor will be ...


1

To increase the speed, I think the dimensionality of the feature descriptors should be reduced. SIFT feature descriptor is of 128 dimensions. So you can try PCA-SIFT which reduces the descriptor number to 20. It uses Principal Component Analysis to the SIFT descriptors. You can also try using Speeded Up Robust Features (SURF). The feature descriptors ...


1

Assuming that the tablature is well defined; let's say it's a MIDI file complete with pitch bend commands. you can make a synthesizer where each note is actually a tracking comb filter tuned to the midi note (and offset by the pitch bend command) with notches at each integer harmonic (including the fundamental). if those tracking comb filters processes a ...


1

You already converted to binary - if that proves to work well, then you can do a shrinkage operation (I'm quite sure there should be such in OpenCV but not completely) until you get one point per segment. Measure coordinates of these points and do pairwise comparison to array of coordinates from a set of models of the digits of a 7 segment display. ( first ...


1

What about an algorithm in image domain? For every pixel, check into the direction you desire to know (up-down in your images above, I guess) if there is a pixel with "the same" value (i.e. a value inside an acceptance band, of course. Let's say +/- 10%) as the pixel under investigation. If so, go further into that direction. Count every pixel that you find....


1

Okay, Feature Extract and Matching is the way to go for a beginner. Other methods such as relative pose estimation, triangulation, 3d matching etc. would be much more complex. Your object is metallic and thus shiny. Also, without correct lighting it is quite difficult to match features. But still, as much as I could see from that picture, you have plentiful ...


1

3. The polar image has the black areas, which are parts that can't be mapped to the original image because it would be outside of its boundaries. However, these areas have nearly disappeared in the log polar image. Why is that? Look at the second (polar) and third (log-polar) images. What's different about them? In the 3rd image, stuff that was ...


Only top voted, non community-wiki answers of a minimum length are eligible