Car License Plate Detection Using Morphological Operators

by admin in , , , on July 25, 2020
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Introduction:

This code uses morphological operators for detecting the car license plate. The code is well explained and determines the place of the plate with a red rectangular border (The code does not read the plate number). The following explanations are everything you need to know about the Morphological Operators supported with external links to articles and examples that certainly will help you.

Morphological Operations

Morphology is a broad set of image processing operations that process images based on shapes. In a morphological operation, each pixel in the image is adjusted based on the value of other pixels in its neighborhood. By choosing the size and shape of the neighborhood, you can construct a morphological operation that is sensitive to specific shapes in the input image.

Elements of Morphology

Types of Morphological Operations

Morphology is a broad set of image processing operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors.

Read the article here.

Morphological Reconstruction

Morphological reconstruction can be thought of conceptually as repeated dilations of an image, called the marker image, until the contour of the marker image fits under a second image, called the mask image. In morphological reconstruction, the peaks in the marker image “spread out,” or dilate.

Read the article here.

Structuring Elements

An essential part of the morphological dilation and erosion operations is the structuring element used to probe the input image. A structuring element is a matrix that identifies the pixel in the image being processed and defines the neighborhood used in the processing of each pixel. You typically choose a structuring element the same size and shape as the objects you want to process in the input image. For example, to find lines in an image, create a linear structuring element.

Read the article here.

Border Padding for Morphology

Morphological functions position the origin of the structuring element, it’s center element, over the pixel of interest in the input image. For pixels at the edge of an image, parts of the neighborhood defined by the structuring element can extend past the border of the image.

Read the article here.

Pixel Connectivity

Morphological processing starts at the peaks in the marker image and spreads throughout the rest of the image based on the connectivity of the pixels. Connectivity defines which pixels are connected to other pixels. A set of pixels in a binary image that forms a connected group is called an object or a connected component.

Read the article here.

Lookup Table Operations

A lookup table is a vector in which each element represents the different permutations of pixels in a neighborhood. Lookup tables are useful for custom erosion and dilation operations.

Read the article here.

Applications of Morphology

Dilate an Image to Enlarge a Shape

Dilation adds pixels to boundary of an object. Dilation makes objects more visible and fills in small holes in the object.

See the example here.

Remove Thin Lines Using Erosion

Erosion removes pixels from the boundary of an object. Erosion removes islands and small objects so that only substantive objects remain.

See the example here.

Use Morphological Opening to Extract Large Image Features

You can use the morphological opening to remove small objects from an image while preserving the shape and size of larger objects in the image.

See the example here.

Flood-Fill Operations

A flood fill operation assigns a uniform pixel value to connected pixels, stopping at object boundaries.

see the example here.

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Release Information

  • Price
    :

    $3.99

  • Released
    :

    July 25, 2020

  • Last Updated
    :

    July 25, 2020

  • File Size
    :

    0.65 Mb

  • Compatible With
    :

    Matlab R19

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