Image Analysis TRIZ Cases

A non-overlapping reference pad enables vision-based chip alignment inspection without bonding noise, improving display panel bonding quality.

Image and text recognition identify tire, vehicle, and pressure-table data to set the correct tire pressure and avoid manual filling errors.

Center-truncated radar measurement models improve moving object state and geometry tracking while keeping computation manageable.

Quadrant-based LiDAR processing separates building edges from bushes and glass reflections for more reliable object recognition and map matching.

Multiple time-separated camera images are aligned on stationary objects and fused into a super-resolution view for more accurate road structure recognition.

A field multiplier such as a DOE or prism expands structured-light projection beyond the lens field, enabling compact wide-FOV 3D mapping.

Timed collection and similarity-based image selection cut redundant training data and speed recognition model relearning for automated driving.

Automatic switching between camera and basic screens follows cutoff lever state, reducing repeated inputs and improving work machine operability.

Parallel ray tracing in a 3D scene emulates LiDAR echoes for moving targets, reducing test time while preserving motion-distorted accuracy.

A virtual line between user and vehicle reshapes the path around obstacles, enabling intuitive mobile robot control with lower collision risk.

Smooth virtual camera angle transitions replace rigid view switching in 3D parking displays, reducing flicker and improving visual coherence.

Short-range wireless anchors correct IMU drift to place virtual overlays accurately on real electrical equipment.

2D camera labels guide 3D sensor annotation to narrow object search regions, improving accuracy while cutting point-cloud processing time.

Virtual lane markings on the windshield shift and tilt with detected lane position, making driving assist status easier for drivers to recognize.

Spatio-temporal correlation and feature-flow warping improve future semantic prediction while separating motion from novel scene changes.

Multiple virtual image depths in one vehicle display optical path reduce eye strain and bulky hardware while preserving glasses-free 3D viewing.

Physiological sensing estimates driver skill and confidence so ADAS can adjust warnings and autonomy in real time to reduce distraction and improve safety.

A moving light fan and SPAD blur imaging speed target mark acquisition, separate spurious reflections, and support geodetic positioning.

Area-based visible light control raises recognition reliability by reducing noise, blur, and pixel saturation in vehicle camera images.

Priority-ranked object prediction lets autonomous vehicles model critical objects first, cutting latency and improving reaction time.

Adaptive MEMS micro-mirror segmentation steers multiple laser beams per frame, cutting SWaP-C while supporting designation, ranging, and imaging.

Exposed thermal pad edges make the solder fillet visible, enabling optical PCB bond inspection and wave soldering without x-ray.

Cropped multi-camera object metadata is fused to extend autonomous vehicle detection range while reducing redundant real-time image processing.

Representative point extraction cuts object-detection data volume, speeding presence and shape processing without losing positional accuracy.

Perturbation-based output stability checks assess image recognition reliability in autonomous vehicles without costly neural network verification.

Bayesian trajectory updates combine projected kinematic states with new observations to improve long-horizon traffic behavior prediction.

Point tracking in simulated road scenarios builds more precise velocity grids for autonomous vehicle training, especially on curved trajectories.

Lane association, map data, and Kalman-filtered sensing help infer a remote vehicle's context and likely maneuvers without V2V.

Observed kinematic states reweight projected trajectories to extend traffic participant prediction beyond short-term extrapolation.

Automatic error detection and image data creation at the component mounting machine cuts operator movement and keeps production flowing.

Multiple imaging devices switch by boom position to keep the operator's area of interest visible during work vehicle movement.

Motion-vector feedback compares tracked object movement with vehicle motion to detect camera misalignment and keep surround-view stitching accurate.

Combining GPS with onboard and roadside LiDAR image comparison corrects vehicle position errors and improves autonomous driving accuracy.

Electrical actuation shifts the lens and sensor for precise camera recalibration, avoiding complex alignment equipment and drift-related replacement.

Seat-specific image recognition conditions use vehicle state and occupant cues to distinguish the driver from other visible faces.

Pairing data from nearby devices and ambient sound help autonomous vehicles detect blind-area objects without overloading real-time processing.

3D image processing distinguishes driver hands from objects and other body parts on the steering wheel, reducing false positives for vehicle control.

Weak-current monitoring and segmented detectors pinpoint solar cable damage early, reducing false alarms during non-power periods.

Deep-trench SiPM pixels reduce crosstalk and improve return intensity and reflectivity capture for more accurate low-level camera-LiDAR fusion.

Multi-module vision inspection tracks substrate, nozzle, droplet, and coating defects in real time to raise display manufacturing yield.

Tracks light-spot position changes over time to separate stationary lights from moving vehicle lights without knowing source height.

Ultrasonic sensing on the boom detects obstacles hidden from the driver and displays their position and distance for safer crane vehicle movement.

Real-time shelf imaging projects action indicators onto item boundaries, reducing restocking errors and time spent checking lists.

Obstacle coordinates are transformed into a passenger-eye frame so on-board 3D views match the real window perspective more closely.

A movable light fan and linear SPAD array speed target mark acquisition, reject spurious reflections, and support geodetic distance locating.

Substrate image metrics and previous-layer layout data are used to tune lithography settings, reducing edge placement errors and stochastic variation.

A single camera tracks multiple vehicle pedals to avoid sensor wear, cut component count, and keep actuator control accurate.

Rear camera path overlays let drivers adjust an autonomous reversing trajectory for more precise vehicle-to-trailer alignment during hitching.