This document provides an overview of image processing and machine vision tools in LabVIEW. It discusses key LabVIEW applications in areas like design, control, and measurement. It then describes tools for reading and analyzing image files in LabVIEW Vision, including functions for acquiring images from cameras and developing simple vision-based measurement systems. The rest of the document outlines various image analysis techniques in LabVIEW like histogram analysis, line profiling, blob analysis, and thresholding; and explains how they can be used for tasks like inspection, object detection, and dimensional measurements.

1. PRESENTATION ON
IMAGE PROCESSING &
LABVIEW
Dr. Vikram Mutneja
Associate Professor
Shaheed Bhagat Singh State University, Ferozepur

2. Key Lab-View Applications
 Design
 Signal and Image Processing
 Embedded System
Programming
 (PC, DSP, FPGA, Microcontroller)
 Simulation and Prototyping
 Control
 Automatic Controls and
Dynamic Systems
 Mechatronics and Robotics
 Measurements
 Circuits and Electronics
 Measurements and
Design Prototype Deploy
A single graphical development platfo

3. LabVIEW-based Vision
 LabVIEW Vision enables to read/create image
Files and provides means for managing those
files
 Built-in functions (VIs) for analyzing image files
(select areas of interest, measure intensity,
etc.)
 Based on LabVIEW IMAQ software which enables
to acquire images from cameras
 use these software tools to develop a simple
visionbased
 measurement system, particularly for object
 motion

4. Overview of LV-Based Vision
Tools

5. Image Analysis
 Image analysis combines techniques that
compute statistics and measurements based on
the gray-level intensities of the image pixels
 Image analysis functions are used determine
whether the image quality is good enough for your
inspection task.
 You can also analyze an image to understand its
content and to decide which type of inspection
tools to use to handle your application.
 Image analysis functions also provide
measurements you can use to perform basic
inspection tasks such as presence or absence
verification.

6. Tools for Image Analysis
 Vision Utilities – VIs for creating and
manipulating images, etc.
 Image Processing – provides ‘low level’ Vis
for analyzing images
 Machine Vision – groups many practical
Vis for performing image analysis.
 E.g. “Count and Measure Objects” VI is found
under this group.

7. Histogram
 A histogram counts and graphs the total number of
pixels at each gray-scale level
 Histogram is used to determine if the overall intensity in
the image is suitable for an inspection task
 To adjust your image acquisition conditions to acquire
higher quality images
 To analyze if a sensor is underexposed or saturated
 An underexposed image contains a large number of
pixels with low gray-level values
 The low gray-level values appear as a peak at the lower
end of the histogram

8. Underexposed Image &
Histogram

9. Overexposed Image &
Histogram

10. Use of Contrast
 A strategy to separate the objects from the
background relies on a difference in the
intensities of both, for example, bright particles
and a darker background.
 The analysis of the histogram of an image may
reveals how many well-separated intensity
populations it does has.
 Adjust your imaging setup until the histogram
of your acquired images has the contrast
required by your application

11. Contrast and Histogram
Analysis
 The analysis of the histogram in this image
reveals that it has two or more well-separated
intensity populations

12. Generating Intensity
Histogram
 Within the ROI, a histogram is generated of
the intensity values
 Note that most of the image is made up of
pixels with intensity greater than about 180.
White is 255.

13. Line Profile
 A line profile plots the variations of intensity
along a line
 It returns the gray-scale values of the pixels
along a line and graphs it
 Line profiles are helpful for examining
boundaries between components, quantifying
the magnitude of intensity variations, and
detecting the presence of repetitive patterns

14. A Line & its Line Profile
 A bright object with uniform intensity appears in the
profile as a plateau
 Higher the contrast between an object and its
surrounding background, the steeper the slopes of the
plateau. Noisy pixels, on the other hand, produce a
series of narrow peaks

15. Create an Image data object
 Menu: NI Measurements->Vision->Vision Utilities-
>Image Management

16. Image Data Types

17. Blob Analysis
 A blob (binary large object) is an area of touching
pixels with the same logical state. All pixels in an
image that belong to a blob are in a foreground
state. All other pixels are in a background state
 In a binary image, pixels in the background have
values=0 while every nonzero pixel is part of a
binary object
 Blob analysis is used to
 Detect blobs in an image and make selected
measurements of those blobs
 Find statistical information-such as the size of blobs or
the number, location, and presence of blob regions

18. Applications of Blob Analysis
 Detecting flaws on silicon wafers
 Detecting soldering defects on electronic
boards
 Inspection applications such as finding
structural defects on wood planks or detecting
cracks on plastics sheets
 To locate objects in motion control applications
when there is significant variance in part
shape or orientation
 To define a feature set that uniquely defines
the shape of the object

19. Thresholding
 Used to select ranges of pixel values in
grayscale and color images that separate the
objects under consideration from the
background
 Converts an image into a binary image, with
pixel values of 0 or 1
 This process works by setting to 1 all pixels
whose value falls within a certain range, called
the threshold interval, and setting all other
pixel values in the image to 0

20. Thresholding applied on an
Image

21. Machine Vision Tasks
 Edge Detection
 Gauging
 Object Detection
 Object Alignment
 Pattern Matching
 Dimensional Measurements
 Color Inspection
 Color Matching
 Color Location
 Color Pattern Matching

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