Image stitching combines multiple overlapping photos into a seamless panorama. This technique is crucial in computer vision, enabling wide-angle views from limited-field cameras. It involves key steps like feature detection, matching, alignment, warping, and blending.

The process starts with algorithms like SIFT or SURF to detect distinctive features in images. These features are then matched and used to align images through homography estimation. Finally, warping and blending techniques create a smooth, unified panorama.

Overview of image stitching

• Combines multiple images with overlapping fields of view to produce a single, seamless panoramic image
• Plays a crucial role in computer vision by enabling the creation of wide-angle views from limited-field-of-view cameras
• Involves several key steps including feature detection, matching, alignment, warping, and blending

Feature detection algorithms

SIFT vs SURF

• Scale-Invariant Feature Transform (SIFT) detects keypoints invariant to scale, rotation, and illumination changes
• Speeded Up Robust Features (SURF) approximates SIFT using box filters and integral images for faster computation
• SIFT generally offers higher accuracy, while SURF provides faster processing times
• Both algorithms generate feature descriptors used for matching across images

ORB and FAST

• Oriented FAST and Rotated BRIEF (ORB) combines modified FAST keypoint detector with rotated BRIEF descriptors
• Features from Accelerated Segment Test (FAST) quickly identifies corners by examining a circle of pixels around a candidate point
• ORB achieves rotation invariance and noise resistance, making it suitable for real-time applications
• FAST excels in speed but lacks scale invariance, often used in combination with other descriptors

Harris corner detector

• Identifies corner points by analyzing intensity changes in multiple directions
• Utilizes a corner response function based on eigenvalues of the second-moment matrix
• Offers good repeatability and distinctive features but lacks scale invariance
• Often used as a foundation for more advanced feature detectors

Feature matching techniques

Brute-force matching

• Compares each descriptor in the first image with every descriptor in the second image
• Utilizes distance metrics (Euclidean, Hamming) to measure similarity between descriptors
• Guarantees finding the best match but can be computationally expensive for large datasets
• Often combined with k-nearest neighbors (k-NN) to find multiple potential matches

FLANN-based matching

• Fast Library for Approximate Nearest Neighbors (FLANN) uses optimized algorithms for faster matching
• Employs data structures like k-d trees or hierarchical k-means trees to speed up nearest neighbor searches
• Trades off some accuracy for significant speed improvements, especially in high-dimensional spaces
• Allows for parameter tuning to balance between speed and accuracy based on application requirements

Ratio test for matches

• Improves match quality by comparing the distances of the two best matches for each feature
• Rejects matches if the ratio of distances exceeds a threshold (typically 0.7-0.8)
• Helps eliminate ambiguous matches and reduces false positives
• Particularly effective in scenes with repetitive patterns or textures

Image alignment

Homography estimation

• Computes the 3x3 transformation matrix that maps points from one image to another
• Assumes a planar scene or pure camera rotation between images
• Requires at least four corresponding point pairs to solve for the eight degrees of freedom
• Can be estimated using methods like Direct Linear Transform (DLT) or normalized DLT

RANSAC algorithm

• Random Sample Consensus (RANSAC) robustly estimates homography in the presence of outliers
• Iteratively selects random subsets of matches to compute candidate homographies
• Evaluates each candidate by counting inliers (matches consistent with the transformation)
• Selects the homography with the highest number of inliers as the best estimate

Perspective transformation

• Applies the estimated homography to align images in a common coordinate system
• Preserves straight lines and handles projective distortions
• Can be used to rectify images or create a composite view from multiple images
• Implemented using matrix multiplication for efficiency

Image warping

Forward vs backward warping

• Forward warping maps source pixels directly to the destination image
• Backward warping computes source pixel locations for each destination pixel
• Forward warping can lead to holes or overlaps in the output image
• Backward warping ensures all destination pixels are filled, commonly used in practice

Interpolation methods

• Nearest neighbor interpolation assigns the value of the closest pixel, fast but can produce aliasing
• Bilinear interpolation uses weighted average of four nearest pixels, balances quality and speed
• Bicubic interpolation considers 16 surrounding pixels, provides smoother results but higher computational cost
• Lanczos interpolation uses a windowed sinc function, offers high quality but slower performance

Blending techniques

Feathering

• Gradually transitions between overlapping images using weighted averaging
• Weights typically based on distance from the seam or edge of the overlap region
• Helps reduce visible seams and exposure differences between images
• Can be implemented efficiently using distance transforms or alpha masks

Gradient domain blending

• Minimizes differences in image gradients rather than pixel intensities
• Solves a Poisson equation to reconstruct the blended image from modified gradients
• Effectively handles exposure differences and reduces ghosting artifacts
• Computationally intensive but produces high-quality results for challenging cases

Multi-band blending

• Decomposes images into frequency bands and blends each band separately
• Low-frequency bands are blended over large spatial ranges
• High-frequency bands are blended over smaller ranges to preserve details
• Combines benefits of both feathering and gradient domain blending
• Particularly effective for handling exposure differences and preserving texture details

Panorama creation

Cylindrical projection

• Maps images onto a cylindrical surface before stitching
• Reduces distortion for horizontal panoramas with limited vertical field of view
• Simplifies alignment to a 1D search problem (horizontal translation and rotation)
• Works well for sequences captured by rotating the camera about its vertical axis

Spherical projection

• Projects images onto a sphere, suitable for full 360-degree panoramas
• Handles both horizontal and vertical camera rotations
• Requires careful calibration of camera focal length and principal point
• Can produce seamless panoramas that wrap around both horizontally and vertically

Challenges in image stitching

Parallax effects

• Occur when the camera center moves between shots, violating the pure rotation assumption
• Can cause misalignments and ghosting, especially for objects at different depths
• Mitigated by using a tripod or special panoramic heads to minimize camera translation
• Advanced techniques like multi-perspective stitching can handle some parallax effects

Exposure differences

• Result from varying lighting conditions or camera auto-exposure between shots
• Can lead to visible seams or unnatural brightness transitions in the panorama
• Addressed through exposure compensation techniques or advanced blending methods
• Global color correction may be applied as a post-processing step

Moving objects

• Create ghosting artifacts when objects appear in different positions across images
• Can be handled by detecting and removing inconsistent regions
• Advanced methods may use graph cuts or seam carving to select the best regions from each image
• Real-time stitching algorithms often employ temporal filtering to handle dynamic scenes

Applications of image stitching

Panoramic photography

• Creates wide-angle or 360-degree views from multiple standard photographs
• Used in landscape photography, virtual tours, and immersive experiences
• Consumer cameras and smartphones often include built-in panorama modes
• Professional applications may use specialized panoramic cameras or robotic mounts

Satellite imagery

• Combines multiple satellite passes to create large-scale, high-resolution Earth imagery
• Used in mapping, environmental monitoring, and urban planning
• Requires handling of large datasets and accounting for Earth's curvature
• Often involves multispectral data and specialized projection systems

Medical imaging

• Stitches together multiple microscope images to create high-resolution views of tissue samples
• Used in whole slide imaging for digital pathology and telepathology
• Requires high precision and may involve z-stacking for 3D tissue samples
• Often needs to handle varying staining and illumination conditions

Performance optimization

Multi-resolution techniques

• Use image pyramids to perform initial alignment at lower resolutions
• Progressively refine alignment and blending at higher resolutions
• Significantly reduces computation time for large images or panoramas
• Can be combined with coarse-to-fine feature matching strategies

GPU acceleration

• Leverages parallel processing capabilities of graphics hardware
• Accelerates computationally intensive tasks like feature detection and image warping
• Enables real-time stitching for video streams or interactive applications
• Requires careful algorithm design to maximize parallelism and memory efficiency

Quality assessment

Seam visibility

• Evaluates the smoothness of transitions between stitched images
• Can be measured using edge detection or gradient analysis along seam lines
• Lower seam visibility indicates better blending and overall stitching quality
• May be used as a feedback metric to optimize blending parameters

Geometric accuracy

• Measures how well the stitched image preserves shapes and proportions
• Can be assessed using known geometric patterns or by comparing with ground truth data
• Important for applications requiring precise measurements or analysis
• May involve evaluating local and global distortions introduced by the stitching process

Color consistency

• Assesses the uniformity of colors and tones across the stitched panorama
• Can be measured using color histograms or statistical measures of color distribution
• Important for achieving natural-looking results and preserving the original scene appearance
• May involve evaluating both global color balance and local color transitions in overlap regions