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# Tag Info

11

You kind of answered your own question. Object Recognition: which object is depicted in the image? input: an image containing unknown object(s) Possibly, the position of the object can be marked in the input, or the input might be only a clear image of (not-occluded) object. output: position(s) and label(s) (names) of the objects in the image The ...

5

First, about the USB barcode reader: at my previous job (QA at a label printer company), we had one from Scanology. It worked as a keyboard device, the scanned data is sent to the pc as if it were typed. So you don't need any special software. Code 39 has no error correction, but even a single misread line won't result in wrong data, because it simply won't ...

4

There are many different algorithms or theories to tackle your problem. As suggested in the comments, Kalman filters (in a regular or extended implementation) ore often tried in this case. If you are in a discrete world (I guess so from your question), you can try to solve your problem in a discrete setting with (for example) the Hungarian algorithm. If ...

4

There are two versions of optical flow(OF): Feature based (sparse) or dense. In the dense version OF is applied to all the image pixels, while in the sparse one, only certain characteristic feature points are tracked. However, both approaches depend on the tracking of pixel quantities. This is fundamentally different than tracking the whole patch, because in ...

3

I think you would use a 2D Matched Filter. You would convolve your image with a series of rectangles. The peaks in the resulting images would be the location of your books. You could do this quickly in by Fourier transforming your image and using the known function for a rectange in 2D Fourier space (its two sinc functions, multiplied).

3

You can use Caffe's deep convulutional net that was trained on the ImageNet DB to extract some very strong features: http://caffe.berkeleyvision.org/ The Caffe framework is implemented in C++ and runs very fast. It enables you to extract features that are much more powerful than LBP or HOG for example. Extract features from a training set and use them to ...

3

As far as I understood, by image derivation you mean extracting edges. I would recommend to filter the image by a relatively large Gaussian filter. If computational cost of image derivation is uncritical to your work, I would recommend using canny edge detector. It is less sensitive to noise and does not fool by noise, and finds weak edges along with strong ...

3

Multiscale object detection generally consists in smoothing the image with larger and larger kernels, to check which objects persist across different scales. So, zoom-out, mostly. You now can have access to the scale-space representation of the image, as shown below. One reason is that, without more knowledge, one does not zoom in beyond the pixel width. ...

3

What you are essentially doing is a matched filter. However, thanks to Hough transform, your filter (line) is oriented and therefore I would call it an oriented matched filter. For generating the Bresenham line and sampling the pixels you might want the use the OpenCV line iterator. The simple usage would be similar to: cv::LineIterator it(image, pt1, pt2, ...

3

In case you can shoot a video of the static scene than a blinking light would be the easiest as you could easily detect it by subtracting the n - 1 frame from the n frame until you see something with high values. If you take a still shot you can use 2 main ideas: If the colors of the scene are from a given plate, find a color very different in Hue and make ...

2

One of my friends did this for his undergraduate thesis. What he basically did was encode properties of each gesture. For example,in the first figure, take a rectangular mask over theportions of the hand. The parts where the skin meets the rectangular mask edge should be noted and marked. Then the relative positions of the larger edge and the smaller edge ...

2

According to the book Programming computer vision with Python an interesting approach is to use dense SIFT (a.k.a. HoG) features on your images, and feed these features to a classifier. I didn't try it myself, but it seems quite sound as an approach. Furthermore, the inventor of the HoG feature proposes the Flutter app that worked quite well in my tests, ...

2

For this special case, I recommend reading up on LAB Color model. And in regard to the LAB Color model, read up on Delta E. The distance between 2 colors. More details about color space can be found here: http://www.codeproject.com/Articles/613798/Colorspaces-and-Conversions I have never attempted the LAB color model via OpenCV as it's is a hassle to ...

2

The paper referenced in your link seems to be this one. Of particular interest there is Table 1 (included below). The accuracy rates aren't great, though they are better than other approaches.

2

I share your assumption about depth being useless here. The approach #1 based on point detectors seems also useless, because there is probably a difference in scale between your reference (learning) images and the representation of objects in the real pictures that is so big that point detection becomes useless. Information is not present at the same level. ...

2

You could try using the peak-to-sidelobe ratio, i.e. how many standard deviations above the mean is each point in the correlation output. psr = ${p - \mu }\over\sigma$ Typically you compute the mean and sigma in a window around each point excluding the region nearest to each point.

2

You should use elements which are a power of 2, for easy and faster computation. I'll say, start with 8x8, so you can only scale up. And if you have to look only at some portions of this image, then you can crop the original one.

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 ...

2

I have used Laplacian of a gaussian filtered image with sigma value of 3. I have thresholded the LoG image with a high-pass filter. I have used gray closing morphologic operator. I have filtered the blobs according to their area. There are several more ways to do it depending on the images, conditions(sea), illumination, ships etc. For this image, non-color ...

2

I have been working on a similar problem of detection of rectangular contours that may also be fragmented or incomplete. In my case the application is detection of so called livestock enclosures in remotely sensed image, see Web: https://www.mmsp.uni-konstanz.de/research/projects/completed-research-projects/detection-of-archaeological-sites-in-high-...

2

A long time ago I have come across a very good advice: research should be problem-oriented, not approach oriented. That means find a problem you want to solve, and then look for appropriate methods. Not the other way around. So, I would recommend starting with a specific problem, such as a real-world application. For example, maybe you want to build a ...

2

No, that's not it. Look at the last sentence of your quote: ... which may be enforced by scaling the data to some precision and truncating to integer values. So this is exactly what you do. Take all your feature vectors, multiply them by 1000 (or some other factor), and truncate to integer. Then the distance between any two unique feature vectors will ...

2

The thing to keep in mind here is that HOG is not invariant to in-plane rotation. A change in orientation of more than 10-15 degrees will probably throw it off. So if your head can have different orientations in the image, you would either need to train multiple detectors or use something other than HOG. By the way, there is a function extractHOGFeatures ...

2

The problem seems to be that the authors of the other papers did not include enough information for you (or anyone) to be able to reproduce their work. This could have been oversight, or the editor (or reviewers) may have removed it prior to publication. I'd suggest contacting the authors of the problematic paper and see if they'll answer your question. Be ...

2

I am no expert, but I would like to give some pointers which could help you to solve. Regarding the method you have mentioned/used, is for image registering/stitching. You are interpreting the results wrong. Calculating the percentage of feature match is not the intention of inliers, rather computing corresponding feature points in both images which could ...

2

The detection task is: Given an image $I$, does it contain a sub-image $S$ (or an object $S$)? There are several things that can happen: $I$ contains $S$ and the detector says $I$ contains $S$ This is a True Positive: the statement is true, and the statement is positive ($S$ is there) $I$ contains $S$ and the detector says $I$ DOES NOT contain $S$ This ...

2

As said in the comments an efficient way is to first detect letters, words and text with OCR. Then try to expand each text zone to its corresponding text bubble. Depending on the text bubble design there are different approaches. However, a solution that could work well and be robust would be to perform edge detection on the near surrounding of the ...

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

You don't. Only circles can be rotated by an arbitrary degree and still have the same shape. A 41x41 image is an axis-aligned square, and only 90 degree rotations keep the shape. And so rotating over 60 degrees means that the width and height needed to fit the square have increased by sqrt(3/2)..

1

Ideally, your training size should be a multiple of the HOG cell size. So if your cell is 8x8 your training size should be 16x16 or 24x24. And no, you cannot detect objects that are smaller than your training size.

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