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Image Extraction Processing Methodologies


Bhushan Thakare , Sinhgad Academy of Engineering Pune, India; Hemant Deshmukh, Dr.Rajendra Gode Institute Of Tech & Research, Amravati, India


Pixel Operation, Thresholding, Differencing, Template Matching, Direct, Fourier, Wavelets, Haar Wavelets, SIFT, SURE, Histogram of Oriented Gradients, Evidence gathering, Hough Transform, Lines, Circles, Generalized Hough Transform, Invariant Hough Transform


There are two major parts of thinking the way of Computer vision: “The extraction of image processing methodologies and their subsequent matching”. The first step is essential as the memory consuming and redundant raw image processing data as captured from cameras would be slow and complex to process by most sophisticated vision algorithm. High level feature extraction concerns finding shapes in computer images. In image processing there is a lots of features processing. It is a special form of dimensionally reduction of any images. When the input data to the algorithm is too large to be processed and is suspected to be notoriously redundant, then the input data will be transformed into a reduced set of features, is called as feature extraction. Thresholding is simple extraction technique. Thresholding is a process of converting grayscale input image to a bi-level image by using an optimal threshold. Thus the objective of binarization is to mark pixels that belong to true foreground regions with a single intensity and background regions with different intensities.

Other Details

Paper ID: IJSRDV4I50413
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 760-763

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