🤖 AI &amp; ML

Incremental Structure from Motion pipeline

Detect and match features, estimate two-view geometry, triangulate, then incrementally add images with PnP and refine via bundle adjustment.

Fundamental matrix versus essential matrix

Both relate corresponding points across two views via the epipolar constraint; the fundamental matrix works in pixel coordinates, the essential matrix in calibrated coordinates and encodes relative pose.

Disparity and depth in stereo vision

Disparity is the horizontal shift of a point between left and right images; depth is inversely proportional to disparity given baseline and focal length.

Epipolar constraint for correspondence search

The match for p1 must lie on its epipolar line in the second image, reducing a 2D search to 1D; the relation is encoded by the fundamental matrix.

Feature choice for real-time mobile SLAM

Pick ORB for fast FAST keypoints and cheap binary descriptors matched by Hamming distance; accept reduced robustness versus SIFT for real-time, low-power operation.

CNN features for image retrieval

Pass the image through a pretrained CNN and read activations from a late layer as a descriptor; deeper layers encode semantics, earlier layers encode texture.

Bag of Visual Words model

Cluster many local descriptors (e.g. k-means) into visual words; assign each image's features to words; represent the image as a histogram of word counts for a classifier.

Descriptor matching and Lowe's ratio test

Match by nearest-neighbor descriptor distance; Lowe's ratio test keeps a match only if the best is clearly better than the second-best, rejecting ambiguous ones.

SIFT versus SURF versus ORB

SIFT is most accurate but slow with float descriptors; SURF approximates SIFT for speed; ORB is fast, binary, and free, ideal for real-time and embedded.

SIFT scale and rotation invariance

Scale-space extrema via difference-of-Gaussians give scale invariance; a dominant gradient orientation gives rotation invariance; the descriptor is a normalized gradient histogram.

Image gradients, Sobel, and Canny

The gradient measures local intensity change in x and y; Sobel approximates it via convolution kernels; Canny uses gradient magnitude and direction plus non-max suppression and hysteresis.

Harris corner detector and corner stability

Harris finds points where intensity changes strongly in all directions using the structure tensor of gradients; corners are well localized in two directions, unlike edges.

Image rotation: forward versus inverse mapping

Forward mapping sends source pixels to non-integer destinations, leaving holes and overlaps; inverse mapping iterates over output pixels, finds the source location, and interpolates.

Removing salt-and-pepper noise

Use a median filter; it replaces a pixel with the neighborhood median so extreme outliers are discarded.

intermediate1 min read

Lens distortion and camera calibration

Radial distortion bends straight lines (barrel/pincushion), tangential comes from lens-sensor misalignment; calibrate with a known pattern to estimate intrinsics and distortion coefficients.

easy1 min read

RGB versus HSV color spaces

RGB mixes three light channels; HSV separates hue, saturation, value so color identity decouples from brightness.

Zero-Shot, Few-Shot, and Chain-of-Thought Trade-offs

Zero-shot is cheap but weak on reasoning, few-shot adds demos at token cost, CoT boosts multi-step accuracy but spends the most tokens and latency.

FlashAttention and IO-Aware Attention

FlashAttention is IO-aware, tiling and fusing attention in fast SRAM to avoid materializing the n-by-n matrix in slow HBM.

Causal versus Masked Language Modeling

Pre-training learns general language from unlabeled text; CLM predicts the next token left-to-right, MLM predicts masked tokens using both sides.