Image Enhancement TRIZ Cases

Rotating an inspection substrate shifts the imaging unit to a clear side view of the nozzle, enabling precise detection of posture, position, and defects.

Scenario-based areas of interest focus automotive sensor processing on relevant regions, cutting compute load for detection and segmentation.

Environment-triggered image capture compares baseline and just-in-time vehicle photos to document damage accurately and reduce rental disputes.

Ranging and image-based vehicle size checks block oversized EVs from battery swap stations, preventing facility damage and entry errors.

A simulated reference vehicle matches test trajectory and speed to compare driving dynamics objectively without lookup tables or repeated tests.

Machine learning with physical process models improves CMP endpoint detection and carrier pressure control for more uniform wafer polishing.

Epipolar geometry, depth resampling, and disparity mapping reduce inverse occlusion artifacts in vehicle virtual camera views.

Two synchronized cameras and a patterned projector cut 3D sensing power use while preserving depth imaging for real-time capture.

Grouped edge patterns and linear approximation help filter reflection-caused short edges and set parking frames more accurately.

Vehicle data adjusts event-camera frame intervals and widths, cutting power use while preserving image processing quality.

A current comparator and replica circuit correct op-amp offset current in autofocus drivers for more precise lens positioning.

External sensing data drives synchronized virtual images in a compact AR display, improving immersion, safety, and multi-focus viewing.

Aggregated RADAR and LIDAR scans are filtered, tiled, and aligned to improve autonomous map creation and localization in dynamic environments.

Active illumination from multiple directions reveals shadows that expose low-contrast obstacles for safer vehicle detection in poor visibility.

Urban edge detection from cameras or lidar estimates vehicle roll and pitch more accurately without drift-prone sensors or costly GNSS.

Positioning stays in WGS84 during data fusion, then shifts to GCJ-02 only afterward to avoid accumulated errors and improve vehicle localization.

Predicting and correcting deformation between low-quality wafer images improves restoration accuracy without extra imaging damage or throughput loss.

Selective row disparity matching enables on-the-fly stereo camera calibration with lower computation while preserving long-range alignment accuracy.

HD map lane markings are transformed into the camera view to overlay precise lane boundaries and route guidance on live road images.

Layout-based coordinate and size calibration improves killer defect classification accuracy and sampling efficiency in semiconductor wafer fabs.

Multiple interior cameras with predefined detection ranges select the best head pose data to improve airbag actuation accuracy.

A conductive inspection layer raises reflectivity and contrast in semiconductor vias, exposing bottom residues and profile defects in optical images.

Segmented image batches combine sequential and parallel 3D modeling to speed offline map generation while preserving mapping accuracy.

Using downward-facing cameras with IR illumination and auxiliary data, this case improves lane position detection on wet, bright, or low-light roads.

Optical flow from two camera images estimates object relative speed, reducing radar dependence while maintaining stable vehicle tracking.

Range images turn sparse LiDAR point clouds into dense inputs for deep learning lane detection with reliable distance cues in changing light.

Camera-based luminance imaging replaces point-by-point photometry to chart license plate light distributions faster across varied geometries.

Facial cues around the eyes are used to detect concentration-lowering emotions and trigger visual or sensory stimuli to restore focus.

Camera images linked to geolocation let users pick exact vehicle rendezvous points beyond map limits, improving navigation precision and control.

Multiple video frames and a pre-trained track model predict obstacle paths, reducing false braking and improving autonomous driving safety.

Rear camera image processing maps hitch features to 3D ground-referenced coordinates, improving trailer alignment without extra depth sensors.

Physics-based simulated images guide electron beam metrology settings to improve critical dimension accuracy and defect detection.

Phase-based depth mapping from reflected eye signals expands gaze detection and enables real-time focus adjustment for comfortable 3D viewing.

Using onboard cameras and sensors, this case detects open parking spots and roadside persons without fixed space sensors, then shares their locations.

Optical sensors and position markers enable self-calibration across vehicles, cutting setup time while preserving coordinate accuracy.

Fusing camera and radar regions of interest improves 3D object detection accuracy and reliability without the load of raw sensor fusion.

Motion sensing and touch data are combined to correct mistargeted screen inputs caused by bumps, vibration, or handheld movement.

Pre-captured images under different lighting modes isolate shadows and reflections, improving nozzle and chuck pin monitoring accuracy.

A configurable target panel and positioning aid calibrate vehicle cameras across models with less setup time, space, and processing load.

Transition-point ROI analysis detects free-space boundaries in occupancy grids with lower computation and more reliable verification.

Transforms vehicle optical-sensor coordinates into accurate map positions by matching tie points and correcting SLAM drift when GPS is unreliable.

Moiré fringe overlay targets amplify tiny lithography alignment errors and improve CCD-based measurement with integrated pattern recognition.

Shared vehicle and roadside sensor data are merged to predict hidden object motion and coordinate autonomous vehicles through intersections.

Fusing 2D image labels, 3D sensor data, and map information cuts manual ground-truth effort while improving object pose and size estimation.

Mounted in the wheel arch, optical image tracking converts wheel displacement into distance and turning angle for accurate vehicle trajectory.

Mark images change with obstacle tilt in surround-view camera images, helping drivers judge obstacle shape and orientation more clearly.

Multiple secondary batteries are rotated and imaged at once, then stitched into full lateral-surface views to raise inspection throughput.

Motion features from vehicle video help predict overtaking and cut-in intent early, enabling proactive hazard response.

In-situ SEM grayscale scoring detects etch redeposition on cell sidewalls without wafer damage, improving inspection throughput and yield.

Context data such as HD maps, sun position, and speed reduction factors improves vehicle object state prediction beyond sensor-only tracking.