We provide Image Processing, DSP Projects to students.

Application areas include

Pattern recognition
Machine learning
Image processing
Deep Learning
OpenCV
OpenCV with Image processing
OpenCV  With Python Programming

We can also port the image processing applications onto ARM9, ARM11, Cortex-A8, Raspberry Pi, Beagle board  XM and DM3730 boards

 

Image Processing

 

1. Image processing basics
• Digital Image Representation
• Image File Formats
• Basic Terminology

2. Arithmetic and logic operations
• Arithmetic Operations: Fundamentals and Applications
• Logic Operations: Fundamentals and Applications

3. Geometric operations
• Introduction
• Mapping and Affine Transformations
• Interpolation Methods
• Image Cropping, Resizing, Flipping, and Rotation
• Spatial Transformations and Image Registration

4. Gray-level transformations
• Overview of Gray-level (Point) Transformations
• Examples of Point Transformations
• Specifying the Transformation Function

5. Histogram processing
• Image Histogram: Definition and Example
• Computing Image Histograms
• Interpreting Image Histograms
• Histogram Equalization
• Direct Histogram Specification

6. Neighborhood processing
• Neighborhood Processing
• Convolution and Correlation
• Image Smoothing (Low-pass Filters)
• Image Sharpening (High-pass Filters)
• Region of Interest Processing
• Combining Spatial Enhancement Methods

7. Frequency-domain filtering
• Fourier Transform
• Low-pass Filtering
• High-pass Filtering

8. Image restoration
• Modeling of the Image Degradation and Restoration Problem
• Noise and Noise Models
• Noise Reduction Using Spatial-domain Techniques
• Noise Reduction Using Frequency-domain Techniques
• Image De blurring Techniques

9. Edge detection
• First-order Derivative Edge Detection
• Second-order Derivative Edge Detection
• The Canny Edge Detector
• Edge Linking and Boundary Detection

10. Image segmentation
• Introduction
• Intensity-based Segmentation
• Region-based Segmentation
• Watershed Segmentation

11. Advance image processing
• Discrete Cosine Transform
• Discrete Wavelet Transform

Machine Learning

 

1 – Data Preprocessing
2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial  Regression, SVR, Decision Tree Regression, Random Forest Regression
3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree   Classification, Random Forest Classification
4 – Clustering: K-Means, Hierarchical Clustering
5 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
6 – Natural Language Processing: Bag-of-words model and algorithms for NLP
7 – Dimensionality Reduction: PCA, LDA, Kernel PCA
8 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

 

Deep Learning 

 

VM, Decission trees, Fisher LD, KNearest N, Linear regression, polynomial regression, regression trees, CNN, RNN, LSTM.