1. Object Recognition and Feature Detection Using MATLAB
Sadhana Venkataraman1, Yukai Tomsovic 2, Ms. Gangotree Chakma 3
Farragut High School 1, West High School 2, University of Tennessee Knoxville 3
TOPICS
INTRODUCTION
Introduction
Edge Detection
Shape Detection
Color Segmentation
Feature Detection
Conclusion
Method to perform operations on an image to get an enhanced image/extract useful information
Basic Steps:
Acquire and import image
Analyze and manipulate image
Output -- image, component of image, or report based on analysis
2. EDGE DETECTION
3. SHAPE DETECTION
Method
Image with several small circles was created
Code used to find radii of all circles in terms of the number of pixels
Range was set between the radius of the smallest circle and the radius of the largest circle
MATLAB outlines the circles
Practical uses
Astronomy -- classification of stars
Medicine -- detecting sickness
Archaeology -- finding objects
Can be utilized to count objects and measure properties of individual objects
Method:
Create a binary image (figure 2)
Label connected components from the binary matrix
Measure properties to classify objects
Figure 3. Original Image
Figure 1. Original image
Figure 2. Binary image
Figure 4. Outlines of circles drawn by MATLAB
4. COLOR SEGMENTATION
Figure 6-8: resulting segmented colors
Applications of Color Segmentation:
Distinguish different types of tissue, cells, tumor borders, etc.
Used in medical thermography to detect breast cancer, tendon/ligament injuries, areas of poor circulation
Used to separate colors without clearly defined edges
Method:
Convert RGB image to L*a*b color space to extract color information
Classify colors in ‘a*b*’ space with K-means clustering
Label pixels to separate clusters of color
Figure 5. Original Image of watercolor paint
Figure 6. First cluster of color
Figure 7. Second cluster of color
Figure 8. Third cluster of color
5. FEATURE DETECTION
Use key features to determine whether two images are identical
Figure compare original image (9) to rescaled and rotated image (10)
The image is rotated and rescaled -- (MATLAB’s job - to restore the distorted image back to its original form)
Color-coded overlay of the original and distorted images and determined the number of matching features (11)
MATLAB identified that the images were the same based on an if statement written in the code
Same process used to determine whether pictures are different (Figure 15-19)
Figure 17. MATLAB identifies these pictures as different
Figure 13. MATLAB identifies these pictures as the same.
Figure 12. Comparison of original (left) & recovered (right) images
Figure Overlay of matching features
Figure 10. Distorted Image
Figure 14. Image 1
Figure 15. Image 2
Figure 16. Overlay of Images 1 and 2
Figure 9. Original image
6. CONCLUSION
Our research is a foundation for more advanced technology. Some further applications of image processing are in the medical field, for PET scans and x-rays, for space image processing, such as the Hubble space telescope images, and for character recognition in scanning license plates and zip codes.
REFERENCES
Anbarjafari, Gholamreza. "Introduction to Image Processing." University of Tartu, n.d. Web. 13 June 2016.
This work was supported primarily by the ERC Program
of the National Science Foundation and DOE under NSF Award Number EEC