CSE-573: Computer Vision & Image Processing

Problem: Detect edges of a grayscale image. Do not use any API provided by opencv (cv2) and numpy(np) in your code (except “np.sqrt()”).

Approach:

• The project applied Sobel and Prewitt filters to detect edges in a given image
• Implemented common image processing tasks :
  • padding
  • convolution
  • correlation
  • normalization etc.

Sample input and output:

Input image:

Output image:

Edge detection using Prewitt filter:

Edge detection using Sobel filter:

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Problem: Character Detection: Find a specific character in a given image using template matching algorithms.

Approach:

• The project applied Template matching algorithm to detect a specific character (ex. a/b/c) in a given image
• Implemented NCC (Normalized Cross Correlation) for matching the template with the given image

Sample input and output:

Input image:

Templates:

Output image:
detecting a

detecting b

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Problem: Image Stitching: Create a panoramic image from at most 3 images. Overlap of the given images will be at least 20% and not more than 50%. Any API provided by OpenCV could be used, except “cv2.findHomography()” and APIs that have “stitch”, “Stitch”, “match” or “Match” in their names, e.g., “cv2.BFMatcher()” and “cv2.Stitcher.create()”.

Approach:

• Keypoints detection and 128 bit feature vector computation using SIFT
• Homography matrix generation using SVD
• Implemented RANSAC algorithm for finding the best Homography matrix
• Stitched all images

                                                                                      Project Report

Sample input and output:

Input image:

Output image:

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Problem: Implement the Viola-Jones face detection algorithm which is capable of detecting frontal faces in real time and is regarded as a milestone in the development of computer vision. Any APIs provided by OpenCV that have “cascade”, “Cascade”, “haar” or “Haar” functionality can not be used. Using any APIs that implement part of Viola-Jones algorithm directly, e.g., an API that computes integral image, will result in a deduction of 10% − 100% of the maximum possible points of this project

Approach:

• Used FDDB dataset to train the model with 'face images and CBCL dataset to train with 'non-face images'
• Implemented integral image calculation
• Adaboost implementation
• Developed CASCADING to reject non-face region quickly

                                                                                      Project Report

output:

• MIT license

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