Thursday, July 28, 2011

Point Vectors in OpenCV

I've started exploring C++ API of OpenCV library and use Standard Template Library along with it. I find it very convenient to use.

For example take a look at the following program. We use vectors to store a sequence of "Point" in OpenCV

In OpenCV 2.0 C++ API:

Monday, July 18, 2011

OpenNI API and Kinect Skeleton

Here is the link, which I've used to download, install OpenNI and other API's to do lots of fun stuff with Kinect.

Sunday, July 17, 2011

Android Sensors API tutorial

I found a document that well describes the different sensors available on Android devices and the API to be used to use the sensors.

Here it is:

Its almost an year old. There might be new sensors added already and new API's exposed.

I wanted to share this with you guys...


Monday, July 11, 2011

android screencast to PC

I found this program "Android Screenshots and Screen Capture by alexus-ivanov, etf, mightypocket " on the web (written in Java) that exports the android screen to the PC.

It comes with a executable jar file. If you are using the latest version of the SDK, you may want to copy the adb binary to sdkroot/tools folder in order for this program to work.

This might be actually useful if you were to give an presentation which is stored on a phone or you may want to share your screen onto PC.

After running the progarm, set the screenshots, Android SDK root export folders.

Its slow though.

Screenshot of my phone:

I also found a similar project "androidscreencast" on the web, didn't try it though.

Saturday, July 9, 2011

Why do we need Feature Selection (Pattern Recognition)?

Feature selection process is a very important task in Statistical Pattern Recognition. It significantly increases/reduces the performance of any classification algorithm applied afterwards. I've mentioned a few guidelines which are commonly used.

In Statistical Pattern Recognition, we need Feature selection techniques due to the following reasons:
  • All Features: Selection of all features for Pattern Classification will lead to more error. So, less features the better.
  • Curse of dimensionality: The more dimensions you add to the feature vector for classification, you might encounter more error on the long run. Less features will give sub-optimal results. There is this safe "number of dimensions" which are needed for optimal classification for the given size of a data-set.
  • Complexity, Size: increase of storage, complexity will be the result of a selection of all dimensions. It basically computationally intensive. So, we don't want to be there either.
  • dimension Subset: specific subset of features may give more accuracy. Our goal will be to find those optimal subset of features that try to classify a given data-set.