Difference between revisions of "Example Binary Image"
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* Image Rendering | * Image Rendering | ||
Relevant Applets: | |||
* [[Applet_Binary_Operations| Binary Operations]] | |||
* [[Applet_Binary_Segmentation| Binary Segmentation]] | |||
= Basic Example = | = Basic Example = |
Revision as of 11:59, 18 October 2011
Binary Image Processing
Binary images are images where each pixel can take on two values, typically represented by 0 or 1. Binary images are easy to compute and fast to process, which makes them popular in many applications.
Example File: BinaryImageExample.java
Concepts:
- Image Thresholding
- Morphological Operations
- Binary Labeling
- Pixel Math
- Image Rendering
Relevant Applets:
Basic Example
In this example a threshold is computed for the input image dynamically and the resulting binary image shown.
public static void binaryExample( BufferedImage image ) { // convert into a usable format ImageFloat32 input = ConvertBufferedImage.convertFrom(image,null,ImageFloat32.class); ImageUInt8 binary = new ImageUInt8(input.width,input.height); // the mean pixel value is often a reasonable threshold when creating a binary image float mean = PixelMath.sum(input)/(input.width*input.height); // create a binary image ThresholdImageOps.threshold(input,binary,mean,true); // Render the binary image for output and display it in a window BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary,null); ShowImages.showWindow(visualBinary,"Binary Image"); }
Labeled Example
Here clustered of blobs are detected and arbitrarily assigned labels.
public static void labeledExample( BufferedImage image ) { // convert into a usable format ImageFloat32 input = ConvertBufferedImage.convertFrom(image,null,ImageFloat32.class); ImageUInt8 binary = new ImageUInt8(input.width,input.height); ImageSInt32 blobs = new ImageSInt32(input.width,input.height); // the mean pixel value is often a reasonable threshold when creating a binary image float mean = PixelMath.sum(input)/(input.width*input.height); // create a binary image ThresholdImageOps.threshold(input,binary,mean,true); // remove small blobs through erosion and dilation // The null in the input indicates that it should internally declare the work image it needs // this is less efficient, but easier to code. binary = BinaryImageOps.erode8(binary,null); binary = BinaryImageOps.dilate8(binary, null); // Detect blobs inside the binary image and assign labels to them int numBlobs = BinaryImageOps.labelBlobs4(binary,blobs); // Render the binary image for output and display it in a window BufferedImage visualized = VisualizeBinaryData.renderLabeled(blobs, numBlobs, null); ShowImages.showWindow(visualized,"Labeled Image"); }