Feature Extraction & Classical Vision
Edge detection, feature descriptors, and classical CV algorithms.
Camera Calibration and Geometry
Camera calibration determines the intrinsic and extrinsic parameters that define how a camera maps 3D world points to 2D image pixels, based on the pinhole model and its extensions.
Corner Detection
Corner detection identifies points where image intensity changes sharply in multiple directions, producing stable landmarks for tracking and matching via methods like Harris and Shi-Tomasi.
Edge Detection
Edge detection identifies boundaries in images where pixel intensity changes sharply, using gradient-based operators like Sobel and multi-stage pipelines like Canny.
HOG (Histogram of Oriented Gradients)
HOG captures local shape and appearance by aggregating gradient orientation histograms over dense spatial cells, forming the classical descriptor behind the breakthrough Dalal-Triggs pedestrian detector.
Hough Transform
The Hough transform detects parametric shapes (lines, circles, ellipses) by having each edge pixel vote in a parameter space, where peaks correspond to the shapes present in the image.
Image Stitching and Homography
Image stitching combines overlapping photographs into seamless panoramas by matching features, estimating projective homographies with RANSAC, and blending warped images together.
Optical Flow
Optical flow estimates the per-pixel apparent motion between consecutive video frames, using methods like Lucas-Kanade for sparse tracking and Horn-Schunck for dense fields.
ORB and Binary Descriptors
ORB, BRIEF, and BRISK encode local image patches as compact binary strings compared via Hamming distance, enabling feature matching orders of magnitude faster than floating-point descriptors like SIFT.
SIFT (Scale-Invariant Feature Transform)
SIFT detects keypoints and computes descriptors that remain stable across changes in scale, rotation, and illumination, enabling robust image matching and recognition.
Template Matching
Template matching slides a reference image patch across a target image, computing a similarity score at every position to find where the template appears.