🤖 AI &amp; ML

What is Bundle Adjustment and why is it tractable?

Jointly refine 3D points and camera poses by minimizing reprojection error, expensive due to many coupled parameters; sparsity of the Jacobian and the Schur complement make it tractable.

Why learn detection and description jointly like SuperPoint?

A shared backbone jointly optimizes detection and description for matching, sharing computation and learning data-driven robustness instead of hand-crafted heuristics.

Feature detector vs feature descriptor.

A detector finds where interesting points are, a descriptor encodes the local appearance around each so points can be matched.

How is an HDR radiance map constructed from exposures?

Recover the inverse camera response function from corresponding pixels, linearize each exposure to radiance, then merge with confidence weights into a floating-point radiance map.

Design drift detection and retraining for a CV model.

Monitor input and prediction distributions plus delayed labels, detect data and concept drift, trigger an automated retrain-validate-deploy pipeline with versioning and rollback.

Design a cashierless retail checkout vision system.

Multi-camera coverage, person tracking and re-ID, product detection plus action recognition for take or return, sensor fusion, edge-cloud split, and failure handling.

CPU vs GPU vs Edge TPU for inference.

CPU is flexible but slow, GPU offers massive parallelism at high power, Edge TPU gives efficient low-power int8 inference but is constrained; choose by latency, power, cost, and model fit.

How do you train and evaluate on imbalanced defect data?

Resampling, class weighting, focal loss, and anomaly framing for training; evaluate with precision, recall, PR-AUC, and F-beta, not accuracy.

How do you speed up a slow detection model?

Quantization, pruning, distillation, lighter backbones, and resolution or batching tweaks, each trading some accuracy or effort for speed.

Precision vs recall in object detection.

Precision is fraction of detections that are correct, recall is fraction of true objects found; prioritize recall for safety-critical detection, precision when false alarms are costly.

What data augmentations help small image datasets?

Apply label-preserving transforms like flips, crops, rotation, color jitter, and mixing to enlarge effective data and reduce overfitting.

How does focus stacking build an all-in-focus image?

Align frames, measure local sharpness as a focus metric, select or blend the sharpest source per region, then composite.

Why merge a burst instead of one long low-light exposure?

Short frames avoid motion blur and clipping while averaging cuts noise; align frames and merge robustly, rejecting misaligned regions to avoid ghosting.

How does a plenoptic camera enable post-capture refocus?

Light field records ray direction plus position via a microlens array, refocusing shears and integrates the 4D data to choose a virtual focal plane.

Compare Gray World and White Patch white balance.

Gray World assumes average scene color is gray, White Patch assumes the brightest pixel is white, both fail on dominant colors or clipping; learning predicts illuminant from data.

How does smartphone Portrait Mode produce bokeh?

Estimate per-pixel depth via dual-pixel or stereo or learning, segment the subject, then apply depth-dependent blur.

Outline the classic image stitching pipeline.

Detect and match features like SIFT, estimate a homography with RANSAC, warp and blend with multiband or feathering.

How do you build an HDR image from bracketed exposures?

Align frames, recover the camera response function, merge to a linear radiance map weighted by exposure, then tone map for display.

How do BYOL and Barlow Twins avoid representation collapse?

Collapse is embeddings shrinking to a constant or low-rank subspace; BYOL uses predictor plus momentum target plus stop-gradient, Barlow Twins decorrelates feature dimensions.