I want to start learning about algorithms so I began reading The Algorithm Design Manual by Steven Skiena because it is recommended in some threads I read in SO. However, I just stopped here
and can't understand most of it because many explanations are represented in figures or images and my screen reader recognize but can't read them.
For example,
"The nearest neighbor rule is very efficient, for it looks at each pair of points
tex2html_wrap_inline23349 //That's how my screen reader read it, I asume it's an image.
at most twice, once when adding
tex2html_wrap_inline23351 //image
to the tour, the other when adding
tex2html_wrap_inline23353 //another image
Against all these positives there is only one problem. This algorithm is completely wrong."
This is really frustrating for me because I'm beginning to enjoy this though I can understand why those images help a lot of readers.
So is their a way to understand this thing without seeing the figures? Or should I read another book?
Thanks in advance and happy new year everyone.
Considering these algorithms are dealing with geometrical analysis, I am afraid it would be difficult to understand them without the images, and even more difficult to replace these images with an equivalent textual description.
Related
I'm currently trying to build a music generator. In order to improve my deal with patterns in music, I have read this article, which states that "This algorithm (exon-chaining algorithm) can be modified to accommodate the pattern selection problem by replacing the weight of an interval with its duration".(page 9).
However, I'm having trouble understanding the meaning of the exon-chaining problem. I have looked for this problem in many different presentations and articles but still couldn't find satisfying information. I would really appreciate it if someone could explain it to me.
Thanks in advance.
I have a big problem detecting objects within an image - I know this topic was already highly discusses in many forums, but I spend the last 4 days searching for an answer and was not able.
In fact: I have a picture from a branch (http://cl.ly/image/343Y193b2m1c). My goal is to count every single needle in this picture. So I have to face several problems:
Separate the branch with its needles from the background (which in this case is no problem).
Select the borders of the needles. This is a huge problem; I tried different ways including all edges() functions but the problem is always the same - the borders around the needles are not closed and - which leads to the last problem:
Needles are overlapping! This leads in "squares between the needles" which are, if I use imfill() or equal formula, filled in instead of the needles. And: the places where the needles are concentrated (many needles at one place) are nearly impossible to distinguish.
I tried watershed, I tried to enhance the contrast, Kmeans clustering, I tried imerose, imdilate and related functions with subsequent edge detection. I tried as well to filter and smooth the picture a bit in order to "unsharp" the needles a bit so that not every small change in color is recognized as a border (which is another problem).
I am relatively new to matlab, so I dont know what I have to look for. I tried to follow the MatLab tutorial used for Nuclei detection - but with this I just can get all the green objects (all needles at once).
I hope this questions did not came up before - if yes, I apologize deeply for the double post. If anybody has an idea what to do or what methods to use, it would be awesome and would safe this really bad beginning of the week.
Thank you very much in advance,
Phillip
Distinguishing overlapping objects is very, very hard, particularly if you do not know how many objects you have to distinguish. Your brain is much better at distinguishing overlapping objects than any segmentation algorithm I'm aware of, since it is able to integrate a lot of information that is difficult to encode. Therefore: If you're not able to distinguish some of the features yourself, forget about doing it via code.
Having said that, there may be a way for you to be able to get an approximate count of the needles: If you can segment the image pixels into two classes: "needle" versus "not needle", and you know how much area in your picture is covered by a needle (it may help to include a ruler when you take the picture), you can then divide number of "needle"-pixels by the number of pixels covered by a single needle to estimate the total number of needles in the image. This will somewhat underestimate the needle count due to overlaps, and it will underestimate more the denser the needles are (due to more overlaps), but it should allow you to compare automatically between branches with lots of needles and branches with few needles, as well as to identify changes in time, should that be one of your goals.
I agree with #Jonas = you got yourself one HUGE problem.
Let me make a few suggestions.
First, along #Jonas' direction, instead of getting an accurate count, another way of getting a rough estimate is by counting the tips of the needles. Obviously, not all the tips are clearly visible. But, if you can get a clear mask of the branch it might be relatively easy to identify the tips of the needles using some of the morphological operations you mentioned yourself.
Second, is there any way you can get more information? For example, if you could have depth information it might help a little in distinguishing the needles from one another (it will not completely solve the task but it may help). You may get depth information from stereo - that is, taking two pictures of the branch while moving the camera a bit. If you have a Kinect device at your disposal (or some other range-camera) you can get a depth map directly...
I need to implement algorithm which for input has picture ( jpeg ) and create new picture like output, but only with bodies ( background is removed completely ). Input picture is picture with people from vacation and I need to recognize human bodies and remove background. Can someone suggest me what algorithm to use, what book to buy to learn that algorihms ?
Check this link it will perfectly answer your question of removing the background and performing further processing
neural networks are particuarly useful for this kind of task, but the theory is a universe, if you're doing it from scratch ... that's a lot of work
This is a segmentation problem. In the general case, segmenting images is a hard research problem (I just spent five years doing a doctorate on segmenting greyscale medical images, for example) and the way you go about it is strongly tied to the type of images with which you have to deal. The best advice I can give is to go and read the appropriate literature on segmenting colour images (e.g. use Google Scholar). In terms of books, this one's a good general-purpose introduction to image processing:
http://www.amazon.co.uk/Digital-Image-Processing-Rafael-Gonzalez/dp/0130946508/ref=sr_1_7?ie=UTF8&qid=1326236038&sr=8-7
Searching for "segmenting people in colour images" on Google seems to turn up some good links, incidentally.
I have a question for you: you want to implement this using an algorithm? If so, then it might require a lot of things to be done (provided you are new to the field of image processing).
Otherwise you may try using masking techniques in image editing software like Adobe Photoshop (that would hardly take 15 mins, depending upon how well you know it)
A good book to start with image processing techniques is: "Digital Image Processing" by Gonzalez and Woods; it starts from the basics, and explains stuff in depth.
Still it may take a lot of time to develop an algorithm to do this job. I recommend you use some library for the same. OpenCV(opensource computer vision) is an excellent choice. The library itself comes with demos which include programs for face detection etc. The inbuilt functions provide a variety of features (edge detection/Feature identification and extraction, you may have to use this) Here's the link
http://opencv.willowgarage.com/wiki/
The link provides a lot of reference material that you can make use of! :)
Start with facial recognition software and algorithms; they have been the most refined over the years and as long as all of your bodies have heads, you can use exif data to figure image capture orientation (of course you can't completely rely on that), sample the facial skin to get skin tone ranges, and find the attached body. Anything that is not head and body should be deleted. This process assumes that a person has roughly the same skin tone on their face as their body and the camera flash isn't washing this out. You could grab the flash duration and some other attributes from exif and adjust your ranges accordingly.
A lot of software out there can recognize faces (look at iPhoto for example), so you'll have to use the face as a reference point, along with skin tone, to find your body edges. You result isn't going to be perfect, but as long as your approach is sound, you'll end up with something useful.
And release your software as open source when you're done so I can use it... :)
You can download a free PDF of the book Computer Vision by Richard Szeliski from the author's website. Not only do you have a free book on algorithms, but it's a book that addresses this specific problem.
http://szeliski.org/Book/
You'll see this image at the top of that page of the author's website.
Used copies of the hardcover are available for about $62 if you check addall.com. If you spent some time doing image processing, you'll appreciate having a paper copy of at least one good general reference book.
Its tough but not impossible. I can't give you any code but Peter Norvig had a great talk on the value of data and in the talk he shows how he was able to take a picture of lake and remove all the houses blocking the image and have the lake expanded with boats,etc..
The computer basically learned how lakes look and boats go on lakes and then removed the houses and placed it there. He explains his process(but no code or anything).
Here it is:
Peter Norvig - The Unreasonable Effectiveness of Data
http://www.youtube.com/watch?v=yvDCzhbjYWs
I am a beginner in image processing. I want to write an application in C++ or in C# for
Searching an image in a list of images
Searching for a particular feature (for e.g. face) in a list of images.
Can anybody suggest where should I start from?
What all should I learn before doing this?
Where can I find the correct information regarding this?
In terms of the second one, you should start off with learning how to solve the decision problem of whether a square patch contains a face (or whatever kind of object you are interested in). For that, I suggest you study a little bit of machine learning, the AdaBoost algorithm, Haar features, and Viola-Jones.
Once you know how to do that, the trick is really just to take a sliding window across your image, feeding the contents of that window into your detector. Then you shrink your main input image and repeat the process until your input image has gotten smaller than the minimum size input for your detector. There are, of course, several clever ways to parallelize the computation and speed it up, but the binary detector is really the interesting part of the process.
You may find some of the material linked from the CSE 517: Machine Learning - Syllabus helpful in getting into machine learning and understanding AdaBoost. You will certainly find the Viola-Jones paper of interest.
I want to write an algorithm that can take parts of a picture and match them to another picture of the same object.
For example, If I gave the computer a picture of a vase and a picture of a scene with the vase in it, I'd expect it to determine where in the image the vase is.
How would I begin to develop an algorithm like this?
The final usage for this algorithm will be an application that for example with a picture of somebody's face could tell if they were in a crowd of people. This algorithm would eventually be applied to video streams.
edit: I'm not expecting an actual solution to this problem as I don't hope to solve it anytime soon. The real question was how do you define something like this to a computer so that you could make an algorithm to do it.
Thanks
A former teacher of mine wrote his doctorate thesis on a similar sort of problem, except his input was a detailed 3D model of something, which he would use to find that object in 2D images. This is a VERY non-trivial problem, there is no single 'answer', certainly nothing that would fit the Stack Overflow format.
My best answer: gather a ton of money and hire a very experienced programmer.
Best of luck to you.
The first problem you describe and the second are both quite different.
A major part of each is solved by the numerous machine vision libraries available. You may need a combination of techniques to achieve any success at either task.
In the first one, you would need something that generically recognizes objects. Probably i'd use a number of algorithms in concert to identify the foreground object in the model image and then do some kind of weighted comparison of the partitioned target image.
In the second case, examining faces, is a much more difficult problem relative to the general recognizer above. Faces all look the same, or nearly so. The things that a general recognizer would notice aren't likely to be good for differentiating faces. You need an algorithm already tuned to facial recognition. Fortunately this is a rapidly maturing field and you can probably do this as well as the first case, but with a different set of functions.
The simple answer is, find a mathematical way to describe faces, that can account for angles and partial missing data, then refine and teach it.
Apparently apple has done something like this, however, it still makes mistakes and has to be taught as it moves forward.
I expect it will be more about the math, than about the programming.
I think you will find this to be quite a challenge. This is an extremely difficult problem and is one of the many areas of computing that fall under the domain of artificial intelligence (AI). Facial recognition would certainly be the most popular variant of this problem and in spite of what you may read in the media, any claimed success are not what they are made out to be. I think the closest solutions involve neural nets and they require very clear and carefully selected images usually.
You could try reading here though. Good luck!