I want to extract an object such as a man ,a car or something like that from an image.The image is just an ordinary iamge, not medical image or other types for specific purpose.
I have searched for a long time and found that the automatic image segmentation algorithms just segment the image into a set of regions or gives out the contour in the image,not a semantic object. so I turned to the interactive image segmentation algorithms and I found some popular algorithms like interactive graph cuts and SIOX and so on. I think these algorithms just meet my demand.
Further more, I also downloaded two interactive image segmentation tool,the first one is the interactive segmentation tool, the second one is the interactive segmentation tool-box.
So my quesions are
1.if the interactive image segmentation algorithm is the right solution for my task since the performance is the most important.
2.and if I want to use the automatic image segmentation algorithm, what should I do next?
Any suggestion will be approciated.
If you want to pick out a object from a single static image just by a few scribbles. I recommend you have a read of
'Closed-form solution to image matting'
or 'Spectral matting',
or 'lazy snapping'
but as in my tests, the last doesn't perform as well as the first two methods when dealing with subtle objects like hairs.
However you can find their source matlab codes very easily from google.
But the first two method are't so pleasant to use actually, I think you'll need to do lots of modification to make them easy to use. It's main problem IMHO, is it requires very decent scribbles on the image, that's if you draw some extra scribbles or at wrong positions, you'll ruin your object cutting .
Apart from these, you may try 'bayesian matting, possion matting, etc.' which all request some helping image called trimap, and it's hard to draw really.
Extracting objects from an image, specially a picture is not that easy as you think, you may want to take a look at the OpenCV project.
OpenCV
Other than OpenCV, I would suggest looking at ITK. It is very popular in medical image analysis projects, because there it is known that semi-automatic segmentation tools provide the best results. I think that the methods apply to natural images as well.
Try looking at tools like livewire segmentation, and level-set based image segmentation. ITK has several demos that allow you to play with these tools on your own images. The demo application such as this is part of the open source distribution, but it can be downloaded directly from the itk servers (look around for instructions)
If this is a business case, you'd better look for companies specialized in "video content analysis". I mean it: reliable people and vehicle detection aren't a single man's project.
Genreral purpose segmentation tools won't do the trick because they have no notion of what a man or a car look like. All they are deemed to do is to find uniform regions in an image.
It is quite late but there is an algorithm called connected component labeling, which you may find useful.
here is wiki link of the algorithm
Related
Where would I find the exact algorithm for generating a Data Matrix ECC200 bar code image that would eventually be printed? I've seen plenty of general explanations but nothing sufficient enough to write code from. I realize of course that there are libraries for this but I'd like to reinvent this particular wheel.
The datamatrix symbology is defined by the standard ISO/IEC 16022 available at:
https://www.iso.org/standard/44230.html
If you poke around the web a bit, you may find a copy....
Well what i want is place i can find the list of all the possible filters used in image processing and how they can be used. Image processing toolbox in MATLAB is one alternative but not very fond of it
Please suggest some links
thanks
Here are a few resources for image noise removal methods. These are mostly research based. The second link has working C++ code that can be downloaded.
Recent trends in denoising - 2007
NL Means denoising
A comprehensive list of the more standard methods (that can be easily implemented using OpenCV's inbuilt functions if you're not fond of Matlab) are here. Right off the cuff (without knowing the type of noise you are dealing with) I'd say go for the Bilateral filter. It's edge-preserving in most cases and comes without any implementation headache in most computer vision libraries.
Is it possible to write a program that can extract a melody/beat/rhythm provided by a specific instument in a wave (or other music format) file made up of multiple instruments?
Which algorithms could be used for this and what programming language would be best suited to it?
This is a fascinating area. The basic mathematical tool here is the Fourier Transform. To get an idea of how it works, and how challenging it can be, take a look at the analysis of the opening chord to A Hard Day's Night.
An instrument produces a sound signature, just the same way our voices do. There are algorithms out there that can pick a single voice out of a crowd and identify that voice from its signature in a database which is used in forensics. In the exact same way, the sound signature of a single instrument can be picked out of a soundscape (such as your mixed wave) and be used to pick out a beat, or make a copy of that instrument on its own track.
Obviously if you're thinking about making copies of tracks, i.e. to break down the mixed wave into a single track per instrument you're going to be looking at a lot of work. My understanding is that because of the frequency overlaps of instruments, this isn't going to be straightforward by any means... not impossible though as you've already been told.
There's quite an interesting blog post by Comparisonics about sound matching technologies which might be useful as a start for your quest for information: http://www.comparisonics.com/SearchingForSounds.html
To extract the beat or rhythm, you might not need perfect isolation of the instrument you're targeting. A general solution may be hard, but if you're trying to solve it for a particular piece, it may be possible. Try implementing a band-pass filter and see if you can tune it to selects th instrument you're after.
Also, I just found this Mac product called PhotoSounder. They have a blog showing different ways it can be used, including isolating an individual instrument (with manual intervention).
Look into Karaoke machine algorithms. If they can remove voice from a song, I'm sure the same principles can be applied to extract a single instrument.
Most instruments make sound within certain frequency ranges.
If you write a tunable bandpass filter - a filter that only lets a certain frequency range through - it'll be about as close as you're likely to get. It will not be anywhere near perfect; you're asking for black magic. The only way to perfectly extract a single instrument from a track is to have an audio sample of the track without that instrument, and do a difference of the two waveforms.
C, C++, Java, C#, Python, Perl should all be able to do all of this with the right libraries. Which one is "best" depends on what you already know.
It's possible in principle, but very difficult - an open area of research, even. You may be interested in the project paper for Dancing Monkeys, a step generation program for StepMania. It does some fairly sophisticated beat detection and music analysis, which is detailed in the paper (linked near the bottom of that page).
For my job i've been using a Java version of ARToolkit (NyARTookit). So far it proven good enough for our needs, but my boss is starting to want the framework ported in other platforms such as web (Flash, etc) and mobiles. While i suppose i could use other ports, i'm increasingly annoyed by not knowing how the kit works and beyond that, from some limitations. Later i'll also need to extend the kit's abilities to add stuff like interaction (virtual buttons on cards, etc), which as far as i've seen in NyARToolkit aren't supported.
So basically, i need to replace ARToolkit with a custom mark detector (and in case of NyARToolkit, try to get rid of JMF and use a better solution via JNI). However i don't know how these detectors work. I know about 3D graphics and i've built a nice framework around it, but i need to know how to build the underlying tech :-).
Does anyone know any sources about how to implement a marker-based augmented reality application from scratch? When searching in google i only find "applications" of AR, not the underlying algorithms :-/.
'From scratch' is a relative term. Truly doing it from scratch, without using any pre-existing vision code, would be very painful and you wouldn't do a better job of it than the entire computer vision community.
However, if you want to do AR with existing vision code, this is more reasonable. The essential sub-tasks are:
Find the markers in your image or video.
Make sure they are the ones you want.
Figure out how they are oriented relative to the camera.
The first task is keypoint localization. Techniques for this include SIFT keypoint detection, the Harris corner detector, and others. Some of these have open source implementations - i think OpenCV has the Harris corner detector in the function GoodFeaturesToTrack.
The second task is making region descriptors. Techniques for this include SIFT descriptors, HOG descriptors, and many many others. There should be an open-source implementation of one of these somewhere.
The third task is also done by keypoint localizers. Ideally you want an affine transformation, since this will tell you how the marker is sitting in 3-space. The Harris affine detector should work for this. For more details go here: http://en.wikipedia.org/wiki/Harris_affine_region_detector
I need to display profiling information pulled from a deeply embedded CPU, presenting it in a way which other developers on my team will be able to act upon. The profiling data is a snapshot of a cycle counter at the entry and exit of every function, so we have a call graph annotated with sub-microsecond timing accuracy. I'd prefer not to just dump out function names and timing like gprof, I'm looking for something easier to understand and act upon.
Has anyone worked with a particularly good profiling tool (on any platform), which made it easy to identify areas of the code to drill into? I'm looking for an inspirational example to follow for how to display the call graph, but if there is good tool with an input format I can massage my data to I'll use it. I could use Windows, Linux, or MacOS X to run the visualization tool.
A profiling article on IBM DeveloperWorks led me to GraphViz, with a profiling example on their site. Barring another suggestion here, I'll use GraphViz and mimic their profiling example.
Another neat tool to visualize profiling data is the gprof2dot.py python script.
It can be used to visualize several different formats: "This is a Python script to convert the output from prof, gprof, oprofile, Shark, AQtime, and python profilers into a dot graph." This is what the output look like:
(source: googlecode.com)
I use Kprof
http://kprof.sourceforge.net/
it is old, but I never found a better tool to inspect the results from gprof.
How about "GTKWave"?
But you have to insert the probe in your code.
Valgrind does profiling (and more), and there are GUIs for visualization.
I suggest you drop gprof+graphviz for OProfileUI, unless you don't have a choice.
JetBrains dotTrace (has a trial demo you can play with). It organizes the call stacks and can easily find the trouble spots. Has a lot of filtering capabilities as well. Very easy to navigate and find what you're looking for.
IE 8b2 offers a simple display of the call tree for javascript that I believe is much more useful than the GraphViz chart.
The GraphViz chart is wonderful for visualizing the call tree but makes it very difficult to visualize timing issues (IMHO the more important data).
**Edit: I thought it is worth pointing out that all of the tools suggested use a grid based tree to visualize the call tree. This allows you to see the calling structure without downplaying the timing data as I believe you do with the GraphViz chart.*
You can use Senseo, a plugin for Eclipse. It shows you the performance, memory allocation, objects created, time spent, actual methods invoked, hover over method signatures or calls, call context tree, package explorer and more.
I've written a browser-based visualization tool, profile_eye, which operates on the output of gprof2dot.
gprof2dot is great at grokking many profiling-tool outputs, and does a great job at graph-element placement. The final rendering is a static graphic, which is often very cluttered.
Using d3.js it's possible to remove much of that clutter, through relative fading of unfocused elements, tooltips, and a fisheye distortion.
For comparison, see profile_eye's visualization of the canonical example used by gprof2dot.