Computer Vision projects during 22Spring semester at ITMO university
Below is a simple table of contents for the Practice :
- Practice 1 : basic methods for images segmentation into semantic areas.
- Binarization.(upper and lower binarization thresholds)
- Segmentation 1
- Image segmentation based on Weber principle
- Segmentation of RGB images by skin color
- Segmentation 2
- image segmentation in the CIE Lab color space by the nearest neighbors method .
- image segmentation in the CIE Lab color space by the 𝑘-means method .
- Segmentation 3
- Texture segmentation using mean value, Standard deviation, relative smoothness, local entropy.
- Practice 2 : Hough Transform.
- Search for lines.
- Search for straight lines using the Hough transform both for the original image and for the image obtained using differential operator.
- Search for circles.
- Search for circles of both a certain radius and from a given range using the Hough transform, both for the original image and for the image obtained using differential operator.
- classic Hough transform algorithms for lines, Highlight the selected points in the Hough parameter space.
- classic Hough transform algorithms for circles, Highlight the selected points in the Hough parameter space.
- Compare implementation results.
- Search for lines.
- Practice 3 : Features Detectors.
- Feature points detection.
- Using SIFT feature point descriptor
- Using ORB feature point descriptor
- Feature points matching.
- Extract feature points of an object and match them with feature points of a scene containing this object, Calculate the transformation matrix using RANSAC method and highlight the object position in the scene.
- Compare feature point descriptors for the task of image matching.
- simple automatic image stitching.
- calculate the transformation matrix between two images and stitch them into a single panoramic image.
- stitch three images into a single panoramic image.
- Feature points detection.
- Practice 4 : Face Detection using Viola-Jones Approach.
- Faces detection.
- search faces using Viola-Jones approach.
- Body parts detectiong.
- Search for eyes, mouth, and nose in one image, and use ROI to improve accuracy.
- face detection in videostream using pre-recorded video with faces.
- face detection in live videostream using web-camera.
- Faces detection.