Machine Vision ENT 273 Hema C.R. Binary Image Processing Lecture 3

Road Map Monochrome Images Binary Image Binary Image Processing Binary Algorithms Hema ENT 273 Lecture 3

Monochrome Images Monochrome images consist of one color [shades of gray values] also called gray level images Information in these images is represented as gray values from 0 to 255 or 256 levels Monochrome images were used in the early days of machine vision when computing power were less. Most machine vision applications use monochrome images to reduce processing time Hema ENT 273 Lecture 3

Grayscale Image Also known as intensity images Image is represented as a matrix with each pixel assigned gray intensity level. Two types Data type [double class] - a floating number between 0 and 1 is assigned to the pixels [many mathematical functions can only be applied to double class] Unit 8 - an integer between 0 and 255 is assigned to the pixels. [requires only 1/8 storage compared to double class] Hema ENT 273 Lecture 3

Binary Images Binary images are images whose pixels have only two possible intensities 0 or 1 Binary images contains only two colors, black[0] and white[1or 255] Requires significantly smaller memory and processing requirements Well developed algorithms are available for binary imaging Ideal for industrial applications which require only silhouette of objects Hema ENT 273 Lecture 3

Formation of Binary Images Binary images are formed by thresholding a grayscale or color image to separate an object from the background Thresholding Is a method to convert a grayscale image to a binary image so that objects are separated from the background Threshold Hema ENT 273 Lecture 3

Thresholding For thresholding to be effective objects and backgrounds should have sufficient contrast. Intensity values of objects or background must be known Fixed threshold Range between T1 and T2 General threshold Z is a set of intensity values for object components Same threshold values may not work for new domains Threshold is selected on application basis Automatic thresholding is the first step in analysis of images in machine vision systems Hema ENT 273 Lecture 3

Geometric Properties of Binary Images 8 4 Connectivity A pixel is connected to another pixel if and only if there exists a path between them 4 connected -4 immediate neighbors 8 connected 4 + 4 corner neighbors Connected components - set of connected pixels Background - set of connected 0 pixels 1 2 3 4 Hema ENT 273 Lecture 3

Geometric Properties of Binary Images Size or area of binary image is Position Position of a binary is determined by finding center of the image or centroid C[x,y] Hema ENT 273 Lecture 3

Geometric Properties of Binary Images Horizontal projection Image Projections Projections are compact representations of images Projections are not unique more than one image can have the same projections Figure shows the horizontal [H] and vertical [V] projections of an image Vertical projection Hema ENT 273 Lecture 3

Binary images Definitions Neighbors Path Foreground Connectivity 4 neighbors [ 4 connected], 8 neighbors [8 connected], Path Sequence of pixel indices Foreground Set of all 1 pixels in an image Connectivity Pixels are said to be connected if there exists a path between them Connected components Set of pixels in which each pixel is connected to all other pixels Background Set of connected pixels that have points on the border of an image Boundary Set of pixels that are at the outer edge of the image Interior Set of pixels that are not in the boundary Surrounds Pixels that surround all image pixels Hema ENT 273 Lecture 3

Binary Algorithms Component Labeling Size Filter Component labeling algorithm finds all connected components in an image and assigns a unique label to all points in the same component. Size Filter Size filters are used to remove noise, this filters removes all regions smaller than a given threshold. Poor choice of threshold results in errors 3 1 2 Hema ENT 273 Lecture 3

Binary Algorithms Euler Number Region Boundary Genus or Euler number is defined as the number of connected components minus the number of holes E = C – H Region Boundary The algorithms selects a starting pixel and tracks the boundary until it comes back to the starting pixel Hema ENT 273 Lecture 3

Binary Algorithms Area Area is the number of pixels in a component Perimeter The number of boundary pixels of a component Hema ENT 273 Lecture 3

Holes Perimeter Hema ENT 273 Lecture 3

Morphological Operators Thinning Binary regions are reduced to lines that approximate their center lines Purpose of thinning is to reduce components to their essential information to facilitate further analysis Dilation This operator enlarges the boundary of foreground pixels, holes inside pixels become smaller Erosion Erodes away the boundary regions, foreground pixels shrink in size 16 Hema ENT 273 Lecture 3

Morphological Operators Close Closing is similar in some ways to dilation in that it tends to enlarge the boundaries of foreground (bright) regions in an image Open The basic effect of an open operator is somewhat like erosion in that it tends to remove some of the foreground (bright) pixels from the edges of regions of foreground pixels. However it is less destructive than erosion in general Thickening Thickening is a morphological operation that is used to grow selected regions of foreground pixels in binary images. Hema ENT 273 Lecture 3

References http://homepages.inf.ed.ac.uk/rbf/HIPR2/label.htm http://homepages.inf.ed.ac.uk/rbf/HIPR2/wksheets.htm Hema ENT 273 Lecture 3

Machine Vision Binary Image Processing End of lecture 3