Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 8 years ago.
Improve this question
I wanna experiment k-means clustering method on different kind of images, so I am trying to find different kind of images used in image segmentation such as MRI images.I want to gather some more categories.
Any suggestion would be gratefully appreciated.
Although this is not the correct place for asking your question, to help you ,Image segmentation has a wide range of application including segmenting Satellite imagery
and Medical Imaging images, Texture Recognition, Facial Recognition System, Automatic Number Plate Recognition, and a lot of other machine vision applications.
Related
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 4 years ago.
Improve this question
Hello everyone I'm building a license plate detection model in Tensorflow. I built a function that chooses a license plate at random from a collection of ~5000 plates and puts it in a random place in on a random background and saves the coordinates. At first I thought to generate about 40K images this way and train the network on with the generated data. But wouldn't it be a good idea to just continiously keep generating new data to feed to the network and basically eliminate any chance of it getting overfitted?
This is an excellent way to train it on how to spot the discontinuities around a superimposed yellow / white / blue rectangle, but maybe not such a great way of teaching it to spot a real license plate. If you've got a good way of procedurally generating images then great! but be warned.
It might spot the wrong pattern.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 8 years ago.
Improve this question
Hi i want to predict health level(High,medium,low) in leaf using image processing and data mining.So far i thought using extract colors from leaf using Bayes algorithm to predict healthy of leaf. and data mining part have completed now.but i need extra features for prediction.we only used orchid leaf.So i can't use vain structure.Can anyone help me to what are the other features can be extracted from leaf for identify health level of leaf.Any idea or comments help me to improve my project. Thanks
There are many possible approaches to a problem like this. One common method is the bag-of-features model. Take a look at this example using the Computer Vision System Toolbox in MATLAB.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 8 years ago.
Improve this question
Now I'm doing human face gender recognition using javacv, The correctness is not very satisfactory for only about 70%. The training set I'm using is some asia human face images I get from internet, and I both used geometry info based training & classification and appearance based info.
The geometry info is produced by choosing some face feature points ratios such like faceheight/facewidth etc and trained by a fisher recognizor, while the appearance info is trained by a LBP face recognizor.
My question is: what is the key point to improve the correctness of gender recognition? Can someone share some experiences?
Thanks~
Though I don't consider myself to be the expert in CV, but I have dabbled a bit to point you to the right literature.
Look at this paper for a survey of work used in human gender recognition from face images:
http://arxiv.org/pdf/1204.1611.pdf
Also look at following papers:
http://www.cse.unr.edu/~bebis/GenderRecognitionIWSSIP12.pdf
http://tdlc.ucsd.edu/research/publications/Nestor_Tarr_Gender_Recognition.pdf
http://www.ijarcce.com/upload/2013/june/43-Hadeel%20Alrashed%20-facial%20gender%20recognition%20using%20eyes%20images.pdf
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 8 years ago.
Improve this question
Please, help me to write Kinect program to recognize objects and their 3D coordinates. And measure their distance between each other. Which library to use, technology and etc.?
Rather vague question but I suggest you read this topic for starters, it's a similar question.
How to get real world coordinates (x, y, z) from a distinct object using a Kinect
Also look at OpenCV, it's a library that can work together with Kinect and can process shapes, recognize objects etc.
I can recommend PCL, which can be found at http://pointclouds.org/.
It supports template matching, image smoothing, etc. They also provide multiple tutorials and with a bit of searching, you should be able to find some implementations of a kinect based scanner.
Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 2 years ago.
Improve this question
I want to do segmentation of video in real time (30 fps, 640х480).
Is it possible on a Core 2 Duo E8500?
Could you point to some library or algorithm that can do this?
Take a look at S. Paris, Edge-preserving smoothing and mean-shift segmentation of video streams, ECCV 2008.
In general, yes, there are some segmentation algorithms that are computationally efficient and are likely to operate at frame rate. Often you can use some optimization tricks to improve the efficiency of algorithms, so if you want to know about a specific task or algorithm you need to provide more details so we can help. I disagree with Abid Rahman K, OpenCV is not necessarily the best option.