This document discusses several computer vision and image processing applications including vision-based inspection, PCB defect detection, aircraft shape detection and classification, fingerprint minutia extraction, drowsy driver detection, lane detection, traffic sign recognition, orthophoto processing, car number plate recognition, face detection, and traffic light detection. It provides details on the development of a vision-based inspection system using cameras and software for image enhancement, pattern matching, segmentation, and classification to automatically detect defects in manufactured parts. It also presents case studies on PCB defect detection and aircraft shape detection and classification using computer vision techniques.

1. Image enhancement Algorithms
Image Pattern matching
Video Fire Detection

2. Fire detection by wavelet
Face Recognition

3. Facecount
1.8 Hair recognition
1.9 Hand Gesture Recognition

4. Vision based Inspection
A vision based inspection product is developed for testing the defects in a manufacturing plant by
using CCD /CMOS cameras .The system involves acquisition cameras with IEEE 1394 Fire wire
interface and software development on a PC based platform. Software for enhancement, blob detection,
pattern matching, segmentation and classification. We have developed expertise in inspection and
surface defects detection using machine vision cameras and image processing techniques. Defective
parts are automatically detected using machine vision image processing technology. The system
consists of CCD camera and optics, Frame grabber, lighting, part sensor, PC and inspection Image
processing software with hardware interfaces. Inspection software consist of developed image
processing software to detect defects of parts manufactured in a production process (Real time )
automatically .Some of the function of the software is to detect defects such as rust, scratch, parts
presence /absence ,measurements & gauging studies, etc. A sample inspection Results are given
below

5. Figure 3 Radiator cap and Ring travellers Inspection
PCB defectInspection –Case study
Objective:
 PCB Defect detection & classification –Fatal &Potential
 Finding Fiducial Marks based on Circularity,area,perimeter,etc-auto
 Finding PCB soldering Position-auto
 Golden Board (Learning Samples-feature statistics)
- Alignment
- Parts /component extraction from PCB board based on color information ,measurement
Good PCB Defective PCB
Registered-aligned

6. Image difference showing defects
Sample case Study:
Original PCB Defective PCB
Registered Defects
Difference (Positive + Negate)
Subtract-positive values highlighted Negative value highlighted Image
255-0 0-255
Breaks , circuit shots and conductor without holes detected

7. Noise free threshold image-median filtering
A- Finding Fiducial marks location Auto -original & defective
original PCB with FM Edge + fill holes
Aircraft Shape Detection& Classification-Casestudy
 Using Fourier descriptors
 Using invariant Moments
Feature descriptors -45 Parameters (Red color in results-Elongation note()
Image,area,mean,Sd,Min,max,XM,YM,perim,bx,by,width,heidght,major.minor,angle.circle,feret,intden.
skew,kurt,%area,raw,slice1,feretx,ferety,feretangle,AR,round,solid,cutoff,feret,mass,xc,yc,xxv,yyv,xyv,
yyv,xsk,ysk,xkurt,ykurt,orient,elongation(last)
1 SF16_P0_Y0_R0_2-1.png 119140 216.042 91.743 0 255 225.558 129.865 0 0 0
460 259 519.054 292.250 0 0 NaN 25739190 -1.930 1.726 0 25739190 1
NaN NaN NaN NaN 1.776 0.563 1 0 1 25739190 225.558 129.865 19014.543
6507.194 121.767 0.057 -0.009 -1.276 -1.437 0.558 4.028E15

8. Fingerprint Minutia Extraction
Problem- Extracting Minutia (termination & bifurcations) from Fingerprints automatically
Process:
- Enhancing the Image
- Converting to Binary image
- Thinning the image
- Identifying ridges & Bifurcations
- Termination identification
- Bifurcation identification
- Removing spurious edges using distance function
- Making ROI mask on Image
- Overlaying Image on Mask
- Extracting co-originates and angles for each terminated, bifurcation points(angles using LUT)
- Saving it in a file for each fingerprints
- Sample results & code
Original Fingerprint image Binary image

9. Thinned image Bifurcations, termination identified
Position of points marked Termination, Bifurcation
Mask area
Image overlay on Mask distribution of major points

10. Drowsy Driver Detection
Lane Blur detection
Lanes
Traffic sign board recognition
Ortho-image Processing–Auto-vectorization

11. Car number Plate Recognition
Car Number Plate Recognition
Face Detector

12. Face Detector
Red light Traffic Detector
Traffic Light Detector-Red signal