Are there any kind of formula for this?
I have been looking for another sites or papers. Also I have watched webinars from Copernicus, but i couldnt find anything about snow albedo formula for sentinel images.
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I am looking to calculate snow-depth for a timeseries of timelapse images in R. In each image there is a snow stake that represents a certain length from which we can get snow depth from. I wish to instead of reading the depth form each image, automate this process.
My idea is to first draw a reference line over the length of the snow stake, so we know how long the snow stake is in pixels. Then to, as it snows, reduce or grow that line based on the snow line, or automatically redraw the line to where the snow starts. I am wondering, a)whether this can be done, b)if any one has tried something similar to this and c)whether someone can point me in the right direction on where to start the process.
Currently I have tried drawing the line and then using edge detection to automatically redraw the line for every picture and then measure that line, but there is too much noise and our snow stakes don't stand out enough. I have also been using the magick package but haven't found a viable solution yet. Let me know if you have any ideas! Thanks!
Here is an example of a snow stake site in summer.
I'm developing a image editor by webgl. I did some research with the existing apps which contains image dehaze effect, FotoJet and Polarr. Here are the example images exported by FotoJet and Polarr.
Original:
FotoJet:
Polarr:
So, what's the dehaze algorithm they used? I think their algorithm is different, can anyone help me figure out the algorithms?
I find the algorithm of Kaiming He at http://kaiminghe.com/cvpr09/. I think it's the solution.
The FotoJet and Polarr don't implement the perfect result as Kaiming He's Demo. I'll try it by myself.
First of all, thanks for reading my question. I'm beginner in computer vision.
I read a lot but I didn't find any solution.
I have an image and I want to detect logo/logos on it.
Also, I have a whole of images with different logos, all image containing a logo on it and nothing more.
Can you help me with any idea of how to detect logo/logos on an image when I have a whole (thousands) of training sets (known logos set)?
It can be done by using the SURF or SIFT feature detection algorithm for few known logos, by matching the given image with all of the others but I have a huge dataset, and I can't match with all other images.
To try all images in the dataset takes toooooo much time :)
Can be useful any SDK? (it can be even for mobile phones or for desktop also).
Or can I use some multiple algorithms for it?
I found an interesting paper about this question with a SIGMA algorithm, but I can't find any description for these algorithms (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5495345).
I think to detect the features on the images is OK (SIFT, maybe SURF).
But I think the problem is with the big number of known images/logos.
I think it should be stored in a special way.
Ex. made a tree somehow from the thousand of known logos, or to separate them in groups.
Is it possible to do this task?
I appreciate any help.
The thousands of training sets is useful only to test your algorithm, it will not help to analyze a new image.
I made a bit of pattern recognition in the past, I would start this way: look for sharp edges (sharp color transitions too). So an edge filter and statistical analysis about features all located in the same corner. The result of the algorithm will be a number that you will use with your training set.
Since you are doing original reserch be prepared for a long work. If a SDK with a function "ImageHasLogo()" exists yet, you will find it on Google.
I'm going to match the sketch face (drawing photo) in to the color photo. so for the research i want to find out what are the challenges that matching sketch drawing in to color faces. for now i have find out that
resolution pixel difference
texture difference
distance difference
and color (not much effect)
I want to know (in technical terms) what are other challenges and what are available OPEN CV and JAVA CV method and algorithms to overcome that challenges?
Here is some example of the sketches and the photos that are known to match them:
This problem is called multi-modal face recognition. There has been a lot of interest in comparing a high quality mugshot (modality 1) to low quality surveillance images (modality 2), another is frontal images to profiles, or pictures to sketches like the OP is interested in. Partial Least Squares (PLS) and Tied Factor Analysis (TFA) have been used for this purpose.
A key difficulty is computing two linear projections from the image in modality 1 (and modality 2) to a space where two points being close means that the individual is the same. This is the key technical step. Here are some papers on this approach:
Abhishek Sharma, David W Jacobs : Bypassing Synthesis: PLS for
Face Recognition with Pose, Low-Resolution and Sketch. CVPR
2011.
S.J.D. Prince, J.H. Elder, J. Warrell, F.M. Felisberti, Tied Factor
Analysis for Face Recognition across Large Pose Differences, IEEE
Patt. Anal. Mach. Intell, 30(6), 970-984, 2008. Elder is a specialist in this area and has a variety of papers on the topic.
B. Klare, Z. Li and A. K. Jain, Matching forensic sketches to
mugshot photos, IEEE Pattern Analysis and Machine Intelligence, 29
Sept. 2010.
As you can understand this is an active research area/problem. In terms using OpenCV to overcome the difficulties, let me give you an analogy: you need to build build a house (match sketches to photos) and you're asking how will having a Stanley hammer (OpenCV) will help. Sure, it will probably help. But you'll also need a lot of other resources: wood, time/money, pipes, cable, etc.
I think that James Elder's old work on the completeness of the edge map (using reconstruction by solving the Laplace equation) is quite relevant here. See the results at the end of this paper: http://elderlab.yorku.ca/~elder/publications/journals/ElderIJCV99.pdf
You could give Eigenfaces a try, though i never tested them with sketches i think they could a least be a good starting point for your research.
See Wiki: http://en.wikipedia.org/wiki/Eigenface and the Tutorial for OpenCV: http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html (including not only Eigenfaces!)
OpenCV can be used for feature extraction and machine learning required for this task. I guess you can start with the papers in the answers above, start with some basic features and prototype a classifier with OpenCV.
I guess you might also want to detect and match feature points on the faces. If you use this approach, you will have to do the feature point detectors on your own (training the Viola-Jones detector in OpenCV with your own data is an option).
I am looking for software (preferably free) that runs on either windows or osx that can take a list cubic images (broken into front, back, up, down, left, right) and turn them into a video. I am also looking for software to create fisheye projections from these images.
Have had a bit of a look around and can't seem to find anything that really suits my purposes. Please Help!
Thanks.
I saw your question and I was curious as to whether there might be a Gimp plugin for this. I did not find one, but my search turned up this: http://www.clickheredesign.com.au/software/ I have not tried it but it appears to be what you are looking for. You could also potentially roll your own in scheme or python script-fu for Gimp.