All Classes

Class	Description
Addition	Given two images of same dimention it'll apply addition operation on each pixel pair & return modified image
AutomaticThresholding	Given a buffered image with bimodal histogram, we'll segment that image into foreground and background using automatic thresholding mechanism.
BitwiseAND	Given two images of equal dimension, it'll compute resulting image ( concurrently i.e.
BitwiseLeftShift	Shifts each pixel intensity I[x, y] of given buffered image by applying left shift operator i.e.
BitwiseOR	Implementation of bitwise OR operator, given two buffered images of equal size, it'll compute bitwise OR for each pixel ( in each pixel for each color component ), and produces output buffered image, without anyhow modifying original images
BitwiseRightShift	Given one buffered image / path to image file & number of bit positions by which pixel intensity to be shifted ( rightwards ), it'll compute transformed image by picking each row up & applying bitwise right shift operator on each pixel intensity value.
BitwiseXOR	Given two images of equal dimension, it'll compute resulting image ( concurrently i.e.
Closing	Implementation of image smoothing op - Closing
ContrastStretching	Contrast Stretching is pretty similar to Histogram Equalization, but rather that computing pixel intensity probability & corresponding CDF values, we'll go for stretching pixel intensity range, which may be intially `a` & `b`, where a > 0 && or || b < 255, but we'll make it 0 to 255.
Dilation	Given an image with white foreground objects & black background color, it'll try to expand foreground area by applying a kernel on each pixel of image, which will make this pixel foreground ( white ) if at least one of the pixels present in its order-1 neighbourhood is white colored i.e.
Division	Given two images of equal dimension, it'll divide pixel intensity of first image by pixel intensity of second image, and store rounded value in resulting image, for each pixel, for each color component.
EdgeDetectionUsingDilation	Implements edge detection using dilation
EdgeDetectionUsingErosion	Detects edges of image by applying combination of erosion & subtraction.
Erosion	Given an image it'll try to shrink foreground region of image, where foreground denoted by white color ( higher pixel intensity )
Filter
GammaCorrection	Given an image ( either color or grayscaled ), we'll transform each pixel intensity of that image using power law transformation / gamma correction function.
GrayScale	Given a RGB image, it'll grayscale that image by taking mean of intensities ( intensity of each of three color components ) at each pixel location of image, while not modifying original image
HistogramEqualization	Applies histrogram equalization transformation to distribute pixel intensity values all over whole range of possible pixel values ( i.e.
HorizontalRotation	Horizontally rotates each row i.e.
Image	Image abstraction class used for performing neighbourhood ( N8 ) ops with ease, works only on grayscale image
ImportExportImage	Helper class to read content of an image into BufferedImage object & write BufferedImage into image file
InverseImageTransformation	Given a buffered image, applies inverse image transformation function on each pixel of it, and produces output buffered image
InverseLogTransformation	Inverse log transformation just works opposite of Log Transformation, it reduces pixel intensity values, which eventually makes image darker
LaplacianFilter
LogTransformation	Given an image ( either color or grayscaled ), we'll trasform each pixel intensity value by using one logarithm based function & output image to be buffered.
MeanFilter	Given a buffered image, it'll compute mean of pixel intensities for a square matrix ( odd valued row & column numbers ), centered at P(x, y), for each color components ( i.e.
MedianFilter	Given a buffered image / path to image file, it'll compute median of pixel intensity values in neighbourhood around it ( size of neighbourhood depends upon order [ >0 ] supplied while invoking filter ) for each pixel position.
MinFilter	Given an image, filters only minimum intensity pixels from neighborhood of a pixel ( neighbourhood surely depends upon order of filtering ) i.e.
ModeFilter	Implementation of mode filter i.e.
Multiplication	Given two images of equal dimension, it'll multiply pixel intensity values of each image ( pixel by pixel fashion ), and resulting image to be stored in a different buffered image
Opening	It's one of smoothing operations based on image morphological op.
Pixel
Position	Holds location, intensity & state information of a pixel in image
RegionGrowing	Given a buffered image ( grayscale, if not gray scaled yet, to be done inside this implementation ), it'll compute segmented image using a seed pixel location, applying region growing algorithm
Rotate	Performs affine transformation - ROTATION on given image, keeps source image unmodified
RotateAntiClockwise180	Given a buffered image, we'll rotate that image by 180° in anti-clockwise direction and obtain a new image, though original image buffer to stay unmodified.
RotateAntiClockwise270	Given a buffered image, we'll rotate that image by 270° in anti-clockwise direction and obtain a new image, though original image buffer to stay unmodified.
RotateAntiClockwise90	Given a buffered image, we'll rotate that image by 90° in anti-clockwise direction and obtain a new image, though original image buffer to stay unmodified.
RotateClockwise180	Given an image, we'll rotate it by 180° in clockwise direction, using combination of two basic operations
RotateClockwise270	Given an image, we'll rotate it by 270° in clockwise direction, using combination of two basic operations
RotateClockwise90	Given an image, we'll rotate it by 90° in clockwise direction, using combination of two basic operations
Rotation	To be implemented by horizontally & vertically rotating classes or any other utility classes which'll try to rotate image, following two abstract methods will be only way using which user gets to talk to rotation mechanism
Scale	Given an image, it'll scale that image as per requested x & y param values in respective directions & returns transformed image ( generated using affine transformation rules )
Subtraction	Given two images of same dimension it'll apply subtraction operation on each pixel pair & return modified image
Translate	Given a buffered image it'll compute translated buffered image by translating each pixel by given amount of translation along X-axis / Y-axis or both.
Transpose	Given a buffered image we'll transpose it, as we do in case of matrix, because 2D image is nothing but a matrix
VerticalRotation	Vertically rotates each column of given buffered image, returns a new buffered image