Image Analysis TRIZ Cases

CMOS imaging classifies PCR partitions accurately across wide sample concentrations.

The calibration box uses an obliquely downward opening, lighting, and reflection to reduce ambient light interference and improve accuracy.

A microfluidic cartridge uses real-time CMOS fluorescence imaging and cycle thresholds to classify partitions across wide concentrations.

This LiDAR case combines 2D mapping, Kalman filtering, and range-based clustering to reduce processing power and improve reproducibility.

This case combines overlapping ground images into a planar panorama to detect crop rows precisely and guide agricultural machine steering.

When added lenses alter focus and distortion, captured optical codes update calibration parameters for accurate imaging.

MNDWI extracts river boundaries, while NDVI sub-pixel decomposition improves width, velocity, depth, and runoff estimates.

A pre-trained vision-language model generates pseudo-masks through weak supervision for novel categories, reducing annotation time.

This case maps 2D detections to 3D spatial coverage, revealing blind spots while guiding computing resources.

Mirrored 3D sensors enable compact bore monitoring with accurate patient positioning.

This case uses video to count scanning actions and compare them with registered products, improving checkout registration accuracy.

A lens and sensor array compares pixel values across candidate depths to focus compact, high-sensitivity imaging.

The case combines images, depth maps, and OCR to identify devices and overlay configurations without manual entry.

This detection approach uses asymmetric illumination or aperture stops to correct defocus and improve relative mark-position accuracy.

The detection portion captures touch and fingerprint data, enabling varied operations and authorized processing without extra sensors.

A display apparatus detects free space with its camera, then selects exercise motions that fit the user's environment.

Known-material regions provide feedback that adjusts X-ray calibration during cargo scans, improving Zeff-based material separation.

Cost-minimized anisotropic weights improve texture quality while reducing filtering complexity.

Adjust environment map resolution by specular width to preserve texture quality.

Photogate sensors and local circuits generate compact feature vectors before neural processing, reducing data load and power needs.

A processor identifies each window's region of interest, creates capture cues, and processes window-specific data for sharing.

Semantic camera segmentation automatically labels radar targets for richer automotive datasets.

Location-specific gesture codes and video analytics help distinguish distress signals and trigger targeted assistance alerts by urgency.

Camera-side tracking continues during poor communication, while terminal target information corrects position and speeds reacquisition.

Image analysis compares live product photos with collection baselines and recommends adjustments during capture for consistent catalogs.

Detect changed 3D regions, transform their coordinates, and transmit grid differences to reduce recording and communication volume.

Two environment-specific recognition models switch with pre-input video data to maintain continuity without excessive processing load.

Transparent portions and markings on mattress holding parts provide visual feedback on subject fixation during medical image examinations.

This case combines image analysis, capability-matched suggestions, and upscaling to guide clay modeling and produce 3D-print files.

Track instrument positions with a real-time patient avatar, reducing fluoroscopy exposure.

AI identifies vehicle parts and damage in varied images, then overlays annotations to support consistent online vehicle displays.

Extrusion, tracing, and parallel-line tools project annotations into 3D space for accurate multi-floor building reconstruction.

Historical frame data predicts target tracking and renders texture to correct HMD camera-eye misalignment in pass-through views.

A concave mirror and multiple light sources keep fixation targets visible across a wider ocular fundus angle during scanning.

Project N-band images into three channels with user-set reference vectors to simplify storage, RGB display, and downstream analysis.

Visual features are paired with generated text sequences to reduce missed detections when predefined categories are incomplete.

Multiple cameras rank biometric records by quality, letting retrieval focus on reliable data when processing capacity is limited.

Separate class processing combines topological and classifier cases to reduce gaps, ramps, and computational overhead in contour maps.

An autonomous camera captures device shadows at multiple positions to estimate light sources and improve large-scene rendering.

An image sensor, field-of-view structure, and lens system capture droplets at depth for rapid, precise counting.

Clusters and texture-stored offsets compress 3D splats for GPU decoding, reducing file size while preserving rendering fidelity.

Modified census comparison enables fast object recognition with lower computational demand.

AI-assisted metadata extraction combines 2D image-based 3D models with database and Internet data for editable digital assets.

Side and central camera spaces support flexible installation and accurate ball measurement for golf, baseball, soccer, and other sports.

A probability image estimates character positions, groups them, and improves recognition despite uneven spacing and distortion.

This case uses Gaussian-masked local features and teacher-network losses to balance image-detection speed with precision.

An integrated imager and height measurer overlap inspection tasks with object movement, reducing sequential inspection time.

Emotion estimation adapts to the user's attention object and appearance, resolving accuracy limits from general state-based assessment.

Separate terrain and object models from 3D scenes with less manual work.

Distance-aware image processing uses downsampling and staged decoding to reduce mobile scanning latency while preserving accuracy.