Geometric Image Transformation TRIZ Cases

Object-aware alpha blending separates road and object regions in camera overlap zones to keep nearby obstacles clear in overhead vehicle views.

Planar homography and self-supervised scene maps detect road obstacles from one camera, cutting sensor complexity for real-time use.

Single-touch image switching lets work vehicle operators toggle monitor and enlarged views intuitively while reducing interface complexity.

Image-based tracking of wheels and lines selects a reliable trailer angle to keep the trailer end centered during reversing.

A split model combines down-scaled global data with tiled local regions to preserve object recognition accuracy while reducing memory use.

A single camera uses planar homography and a self-supervised scene structure map to detect road obstacles with lower sensor and compute cost.

Dynamic virtual viewpoint and projection tilt control reduce surround-view distortion while keeping nearby vehicles visible without double imaging.

Pre-rendered 2D seed images are selected by road curvature and distorted by lane position to show driving surroundings without 3D rendering.

A virtual projection surface rotates and shifts with vehicle tilt to preserve all-round view image quality on slopes and uneven ground.

A monocular reverse assist approach splices current and historical top views to widen rear path coverage while limiting hardware and memory use.