# Tag Info

60

Both SIFT and SURF authors require license fees for usage of their original algorithms. I have done some research about the situation and here are the possible alternatives: Keypoint detector: Harris corner detector Harris-Laplace - scale-invariant version of Harris detector (an affine invariant version also exists, presented by Mikolajczyk and Schmidt, ...

26

There is a relatively new method, you might want to look into: BRISK, Binary Robust Invariant Scalable Keypoints: In this paper we propose BRISK, a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK’s adaptive, high quality performance as in state-of-the-art algorithms, albeit at a ...

15

First of all, there's no such thing as 'template' in this paper - the word 'template(s)' has a different meaning in Computer Vision. The method used in this paper is relatively straight-forward. Let me break it down for you. There are three important things that you need to do when doing tasks such as object recognition, image matching, image stitching, ...

14

The best ideas that exactly tries to solve this problem is Hough Transform . Basically, the signal in hough space will be r, x, y co-ordinates. Here r stands for radius and x,y stands for center. Every points may belong to one or many circles. So in the Hough plane go through all possible circles where this point could belong to and just do a +1. This is ...

14

An interest point (key point, salient point) detector is an algorithm that chooses points from an image based on some criterion. Typically, an interest point is a local maximum of some function, such as a "cornerness" metric. A descriptor is a vector of values, which somehow describes the image patch around an interest point. It could be as simple as the ...

12

Don't trust anyone here, talk to a lawyer. The Legal world is subtly different from ours, if I may say. Depending on what you exactly want to do (and where, etc.), there may be a solution where you could use SURF or SIFT. I have been surprised in the past how seemingly strong licenses can be overcome.

11

I will try to avoid math, because math and "how to do it" tutorials can be easily found. So, I start by pointing out one VERY important thing: One does not compute Harris for a single pixel, but for a vicinity (a patch of image) around that pixel! Let $I(i)_{xx}, I(i)_{xy} ...$ be your derivatives for a point $i_0$, then, $H = \left[ \begin{array}{cc} \... 9 As far as alternatives to SIFT/SURF go, the question you linked provides very good answers. There were two more questions I could read out: "how could I build a useful (e.g. rotation invariant) feature descriptor"? "regarding the statement from the linked question, how does he accomplish free rotational invariance?" Building feature descriptors This is a ... 9 Can you try a different feature detector? FAST may be, erm, faster, and a higher frame rate will make matching easier (assuming your features are moving a lot between frames) Looks like you are trying to use the grayscale region around the identified feature point to match from frame to frame. This is likely to be poor, especially if there is lots of ... 9 I think it is kind'a similar to soft and hard thresholding using in wavelet de-noising. Have you come across this topic? pywt has already an in-built function for this purpose. Please take a closer look at this code and try to play with it: import pywt import matplotlib.pyplot as plt import numpy as np ts = [2, 56, 3, 22, 3, 4, 56, 7, 8, 9, 44, 23, 1, 4, 6,... 8 I would rather look into KAZE / AKAZE, which perform equally good with significant speed-up. The deformation cases are also tolerated. OpenCV has recently obtained an implementation through GSoC 2014. You can find it here. Its OpenCV tutorial is also present here. 8 Image keypoints are a key feature in many Image and Video processing softwares, both industrial and academic. The principle behind is always the same: detect some meaningful points in some images; [optional] compute a stable description of the image part surrounding each keypoint; match keypoints from an image (the template) to another (the query). Now, ... 8 Here is what I did for a client (What you are asking is the same). Assuming that you have access to certain type of a pattern on the image (or the center of the hole), you could always detect the template to obtain the location of a possible unwarp: Note that in the transformed image, two region of interests are defined and the region within which we would ... 8 Some Features: Mean. Variance. Skewness. Kurtosis. Dominant 3 frequencies in the DFT. Energy of the 3 dominant frequencies. Max Value. Min Value. Median. Usually I'd compute them in running windows. Another great information is the Histogram of the Derivative. Or just all the above of the Derivative. 7 I would have a look at the so called "bag of words" or "visual words" approach. It is increasingly used for image categorization and identification. This algorithm usually starts by detecting robust points, such as SIFT points, in an image. The region around these found points (the 128 bit SIFT descriptor in your case) is used. In the most simple form, one ... 7 The 1D gabor filter has the following form in the frequency domain: $$G_{b(\sigma,\omega_0)}(\omega) = \text{exp}\left(-\frac{\sigma^2}{2}(\omega - \omega_0)^2\right)$$ The 1D log-gabor filter is: $$G_{l(\sigma,\omega_0)}(\omega) = \text{exp}\left(-\frac{\ln^2(\omega/\omega_0)}{2\ln^2(\sigma)}\right)$$ Log-gabor filters are used because they have 0 DC ... 5 Harris Corner detector tries to quantify the local intensity changes at all the directions for each pixel. The figure below illustrates the basic idea clearly: So$I(x+u,y+v)$indicates the pixel intensities of all the neighborhood pixels around$(x,y)$. The window function is applied for feature localization. For most often used Gaussian function, the ... 5 In the robot navigation problem, the localization problem refers to the real time estimation of its position and orientation under various backgrounds. This is usually achieved by some natural landmark selection (laser points, camera views, etc.), and the features in the image (corners, tiny lines with different orientations, etc.). So the localizability ... 4 Most likely your images look different from the ones in the lectures because of scaling. Note that the result of the convolution with a Laplacian filter will have positive and negative values. What the resulting image looks like depends on the data type of the array, and on the range to which the values are scaled. For example, if you store your filtered ... 4 I am currently working on CBIR using Component Trees, which should be a relatively new idea. Some expected advantages of using Component Trees to describe images would be: The Component Tree representation of an image would not depend so much on the deformations (even projective) to the image Examining different levels of the tree would allow comparisons ... 4 MSER (Maximally stable extremal regions) are regions, not points. And they're invariant to affine transformation. But it's not a segmentation method, strictly speaking Informally speaking, the idea is to find blobs at various thresholds, then select the blobs that have the least change in shape/area over a range of thresholds. These regions should be stable ... 4 Another way to get rotational invariance for free, is to choose objects that are rotationally invariant. For instance, a circle or a ring is invariant to rotations. Feature extractor: Run edge detection. For each neighborhood of NxN pixels, calculate edge direction and magnitude 2D histogram. Find all points that have high total magnitude, and high angular ... 4 (IANAL...) If you only want detectors: Harris is probably OK. According to http://users.fmrib.ox.ac.uk/~steve/susan/, SUSAN is out of patent now. I've not seen any claims that FAST is patented. Descriptors are harder... Histograms of Oriented Gradients might be worth considering - again I've not seen any claims of patent on the original form. 4 There are two different concepts: If you think as your signal as a single random variable$X$that is emitting values, then what you want is to calculate the Entropy of the random variable http://en.wikipedia.org/wiki/Entropy_estimation If you are considering the entire random signal or stochastic process, then you have to estimate the autocorrelation ... 4 In addition to the features mentioned so far I would like to mention measures of complexity such as: Shannon Entropy LZ Complexity Fractal Dimension There are also Fourier Descriptors (as hinted by Drazick already) and their equivalent in Wavelet Analysis and of course simple histogram bins which would return how frequently each gear is engaged en route. ... 3 As alternative to SIFT/SURF/Other you can also use FFT phase correlation, if frames transformed by mostly translations (rotation/perspective is small). You can also apply phase correlation to regions of image iteratively for better precision. http://en.wikipedia.org/wiki/Phase_correlation 3 I may be wrong if i have not understood the question! I am trying to give a rather elementary introduction here. I can refine things and be more rigorous as suited. What you are looking for is that of 100 (or 1000) patches, which patch is the most representative patch of all. For simplicity if the size of a patch is 1x1. So it is just a scalar. In this ... 3 If I understand correctly what you are asking -- In general, the feature is found at the same scale as the SIFT detector says, but in David Lowe's SIFT, the image is pre-smoothed with sigma: 0.5, so, you need to "subtract" this amount of smoothing from the sigma, so the "real" scale could be: sqrt(sigma^2 - 0.5^2) where sigma is the scale the feature was ... 3 The$\sigma_I$determines the scale level at which the Harris corners are computed. Coarser scales (higher values of$\sigma_I$) correspond to larger corners. The$\sigma_D$is effectively the window size, over which the derivatives are summed to generate the entries of the matrix. If$\sigma_D\$ is too small, then the detector will be seriously affected by ...

3

For a quantized or digital signal, you can get a upper bound on an estimate of information complexity or randomness by attempting to compress the data and/or the data's spectrum using a large variety of compression algorithms.

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