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POMat of lines of different lengths
 

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  1. POMat of lines of different lengths
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    <p>I want to find the similarity between two Mat of descriptors. Then, I have two matrices (Mat ) to be matched using one of matching methods, i.e: FlannBasedMatcher matcher,</p> <pre><code>Matcher.match(descriptors1, descriptors2, matches) </code></pre> <p>But actually my own descriptors of each line of Mat don’t have always the same lengths. For example: my matrix is structured as:</p> <blockquote> <p>Elem1: desc_1 desc_2 .. desc_n</p> <p>1: 22 33 44 13 33</p> <p>2: 12 1 13 10</p> <p>3: 11 33 55 16 15 13 33</p> </blockquote> <p>Should I have to padd by zeros?, where I’m sure that the similarity measure will be affected by this padding. OR I can leave them as they are? Here we have a problem of creating a Mat of lines with different length?, which is not possible as I know! Can any one suggest a solution? Or may be another method to find the corresponding lines</p> <p><strong>additional explanation:</strong></p> <p>My descriptor is an invariant value, calculated from four collinear points (cross ratio). I start detecting some key points from the image, and then I regroup all collinear points. I get sets of regrouped collinear points of different sizes. Let say one of those set is composed of n = 4 points, then I’ll get one invariant descriptor value, if n = 5 points, I’ll get 5 invariant descriptors, the formula for calculating all possible descriptors between each 4 points of a set composed of 5 points is:</p> <pre><code> 5! C(5,4) = -------------- 4! (5 - 4)! </code></pre> <p>So, let’s consider that I have 3 regrouped points’ sets, I’ll have a matrix (Mat) of 3 lines, and each line will contain its invariant descriptors, depending on the number of collinear points found for each set. So each line of Mat may have different number of descriptors (which represent the number of cols) I hope that can help you to get my idea. </p>
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    1. COHow are you creating descriptors with different lengths? This shouldn't be possible if you are using OpenCV's descriptor extractors.
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    2. CONo, I'm not using OpenCV's descriptor extractors, I'm using my own method, wihch is totally different from OpenCV's descriptors
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    3. COOkay. I am still puzzled by how you end up with feature vectors of different lengths. A description of your method would lend a lot of insight. Specifically, what do different descriptor lengths mean?
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