Removing Small Objects
This example shows how to remove small objects from an image by using its’ size. (thanks Janifal for contributing the sample image) (more…)
Image Matching
This example show new features of IPCV 1.1 for features detection and matching.
Convolution and Correlation –> Image filtering and Template matching
In image processing, most of the time the used of convolution and correlation for filtering is more to personal preferences, as they perform almost the same operation. They are identical if the kernel is symmetrical.
Morphological Operation on Binary Image
The two principal morphological operations are dilation and erosion. Dilation allows objects to expand, thus potentially filling in small holes and connecting disjoint objects. Erosion shrinks objects by etching away (eroding) their boundaries. These operations can be customized for an application by the proper selection of the structuring element, which determines exactly how the objects will be dilated or eroded.
Converting from Color to Gray and Binary image
Most of the images acquired are in color or RGB format, which would need more processing power, memory and time to process. In a lot of cases, images could be converted to gray scale or binary for processing, which require less computing power.
In this tutorial, we are going to learn how to convert the color image to grayscale and binary image prior to the processing.
Image Representation in Scilab
Scilab IPCV represents images in a few formats. The 3 basic types of images supported in Scilab are:
Installation of Scilab IPCV
The Installation should be straight forward for Windows, but a few more steps for Linux.
Welcome to Scilab IPCV Page!
Welcome to Scilab Image Processing and Computer Vision Toolbox page, we call it IPCV. This module was previously based on SIVP module and now has been re-coded to work with Scilab 6.0!