Learn Digital Image Processing

Learn Digital Image Processing
The Learn Digital Image Processing app contain all concept of this domain. With the help of this app you can make the high professional researcher in this domain. Complete free handbook of DIP with diagrams and graphs. Learn Digital Image Processing (DIP) covers notes on Digital Image Processing. The best Learn Digital Image Processing in Engineering Education also brings the blog where you can contribute your work and get the research, industry, university News on the subject.
Learn Digital Image processing covers more than 200+ topics of Digital image processing. The topics are divided in 2 units. In 1st unit all topics explain and 2nd quick overview. Some of topics Covered in this application are:
-Signals and System Introduction
-History of Photography
-Applications and Usage
-Concept of Dimensions
-Image Formation on Camera
-Camera Mechanism
-Concept of Pixel
-Perspective Transformation
-Concept of Bits Per Pixel
-Types of Images
-Color Codes Conversion
-Grayscale to RGB Conversion
-Concept of Sampling
-Pixel Resolution
-Concept of Zooming
-Zooming Methods
-Spatial Resolution
-Pixels, Dots and Lines Per Inch
-Gray Level Resolution
-Concept of Quantization
-ISO preference curves
-Concept of Dithering
-Histograms Introduction
-Brightness and Contrast
-Image Transformations
-Histogram Sliding
-Histogram Stretching
-Introduction to Probability
-Histogram Equalization
-Gray Level Transformation
-Concept of Convolution
-Concept of Mask
-Concept of Blurring
-Concept of Edge Detection
-Prewitt Operator
-Sobel Operator
-Robinson Compass Mask
-Krisch Compass Mask
-Laplacian Operator
-Introduction to Frequency domain
-Fourier Series and Transform
-Convolution Theorem
-High Pass vs Low Pass Filters
-Introduction to Color Spaces
-Introduction to JPEG Compression
-Optical Character Recognition
-Computer Vision and Computer Graphics
-DIP Useful Resources
-Linear Discriminant Analysis
-Reduced search algorithm
-The Hough transform
-Edge detection techniques
-Laplacian of Gaussian
-Canny - Edge Detection Algorithm
-Fundamentals of color
-The RGB Color Model
-Additive vs. Subtractive Color Models
-Intensity slicing
-Intensity to color transformation
-Morphological Image Processing
-Dilating an Image
-Structuring Elements
-Structuring Element Decomposition
-Global properties using B-rep model
-Compression fundamentals
-Lossless data compression
-Image restoration
-Linear position invariant degradation models
-Elements of visual perception
-Color perception
-Image sampling and quantization
-Basic relationship between pixels
-Basic geometric transformations
-Introduction to Fourier transform and DFT
-Fast Fourier transform
-Properties of two dimensional Fourier transform
-Separable image transforms
-Walsh, Hadamard, discrete cosine, Haar and Slant transforms
-Spatial domain methods
-Interactive Specification of Color
-Colour Image Processing
-CIE primaries
-The HSI Model
-The YIQ Model
-Transferring Color to Greyscale Images
-Basics of Full-Colour Image Processing
-Colour Slicing
-Tone and Colour Corrections
-Grey scale manipulation and histogram equalization
-Image subtraction and Image averaging
-Spatial filtering on images
-Smoothing filters on images
-Sharpening filters on images
-Derivative filters on images
-Laplacian filters on images
-Frequency domain
-Homomorphic filtering
-Image restoration process
-Model of image degradation
-Noise models
-Characteristics of the noise models
-Order-Statistics Filters
-Adaptive filters - Digital Image Processing
-Least-Mean-Square (LMS) Algorithm
-Lossy compression
-Transform Coding
-Wavelet Image Coding
-JPEG Compression
-MPEG Compression
-Basics of Vector quantization
-Image Segmentation
-Edge Detection
-Image thresholding
-Region based segmentation
-Boundary Representation Models

in Learn Digital Image Processing (DIP), all topics are not scheduled because character limitations of play store.