Face Recognition: Making face model and similiarity threshold
Face Detection: Face feature recognition (Eye, Nose, Mouth)
Convolution Kernel: Custom Kernel UI and implementation
Convolution: Convolution implementation and median convolution
Handwriting Letter Recognition: Letter dataset, UI, parameter configuration
ASCII Recognition: Front-end Implementation and image data
Bone (Thinning Algorithm): Thinning Algorithm implementation (Zhang Suen)
Number Recognition: Dynamic User Interface implementation
Equalizer: Equalizer UI to get input from user
Image Normalizer: Cumulative Normalization
Frequency Histogram: Frequency Histogram Calculation

Face Recognition: Counting distance between face image from LBPH histogram
Face Detection: Face detect using k-means
Convolution Kernel: Convolution Algorithm for Kernel
Convolution: Gradient convolution
Handwriting Letter Recognition: Letter predict with knn using letter features
ASCII Recognition: Make ASCII predict rules
Bone (Thinning Algorithm): Thinning Algorithm implementation (Zhang Suen)
Number Recognition: Number Prediction using knn from chaincode
Equalizer: Equalizer function using Cumulative Histogram
Image Normalizer: Scaling Normalization
Frequency Histogram: Frequency Histogram Plot

Face Recognition: Implementing LBPH Algorithm
Face Detection: Multiple Face detect using S. Kolkur skin color and Face Elimination From non-Face Skin
Convolution Kernel: Kernel Data (Sobel, Prewitt, dll)
Convolution: Differents Convolution 
Handwriting Letter Recognition: Get features from letter (strokes, circle, branch, etc)
ASCII Recognition: Get features from bone and make ASCII predict rules
Bone (Thinning Algorithm): Thinning Algorithm implementation (Zhang Suen)
Number Recognition: Build chaincode from image implementation
Equalizer: Cumulative Frequency Histogram Calculation from input
Image Normalizer: Cumulative Frequency Histogram Calculation
Frequency Histogram: Frequency Histogram Calculation