Image processing using labview

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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