#3770 Σ0ρλδ!!

This week using morphological operations, image transformations were done and use of filters on them. Day 41: Opening and Closing Images using Morphological Filters Day 42: Detecting edges and corners using morphological filters Day 43: Filtering Images using Low-pass Filters Day 44: Filtering Images using a Median Filter Day 45: Applying Directional Filters to Detect Edges

This week was spent on learning how to count pixels using histograms. Day 36: Computing the Image histogram Day 37: Equalizing the image histogram Day 38: Backprojecting a Histogram to Detect Specific Image Content Day 39: Using the Meanshift Algorithm to Find an Object Day 40: Retrieving Similar Images using Histogram Comparison

This week we learnt how to process images with classes. It helps in revisiting some important concepts of C++ as well. Day 26 - 28: Using strategy pattern in Algorithm Deesign Day 29 - 30: Converting colour spaces. 

This week we have learnt to manipulate pixels in images. Day 26: Accessing pixel values Day 27:Scanning an image with pointers and iterators Day 28: Writing efficient image scanning loops Day 29: Performing simple image arithmetic Day 30: Defining regions of interest

For next 7 weeks, we will learn the basics of OpenCV and how to use it to develop applications. For a CS student like me, what matters more than theoretical knowledge is practical knowledge. People with theoretical knowledge are dimes a dozen while those with practical knowledge and innovative ideas are one in a million. The best source  to learn OpenCV for beginners is :  http://www.laganiere.name/opencvCookbook/ This week we have done following tasks: Day 21: Installing OpenCV2 Library on Windows Day 22: Setting up Visual Studio and windows. Day 23: Creating First OpenCv Project Day 24: Loading, displaying and saving images Day 25:Working with Qt 

The major challenge in training ANN is the large dataset required to get more accurate results. More and more unique images are required  for each character. Also all of these images should be normalized and have same size,i.e., 10 x 10 pixels. To get this dataset we have to use the segmented characters produced by SVM - the Ouroboro problem. No clear solution to this problem has been found. 

Today, training data requirements for ANN were identified. It will require more than 20 images for each character. In our case, we will need to gather data for  set of 26 characters (10 numbers + 26 alphabets).