Computer Vision
Machines that see, and systems that act on what they see.
Perception systems from detection and segmentation to video understanding and multimodal vision — engineered for real environments.
Architectural layers
Problem
What problem does this solve?
- Vision demos fail on real data: lighting, occlusion, camera quality, edge cases.
- Labeling is expensive; model choice and data strategy must be engineered together.
- Edge and latency constraints rule out the default 'biggest model' answer.
Scope
What CAS builds
- Detection, classification and segmentation systems with measured accuracy/latency envelopes.
- Video understanding pipelines: tracking, temporal reasoning, event detection.
- Multimodal perception combining vision with language for inspection, search and QA.
- Data engines: labeling workflows, active learning loops, drift monitoring.
Architectures
Architectures that may be used
Each links into ModLens, the CAS architecture explorer, where the structure and trade-offs are diagrammed.
CNN families
open in ModLens →
YOLO detection
open in ModLens →
R-CNN family
open in ModLens →
Vision Transformers
open in ModLens →
Multimodal systems
open in ModLens →
Method
How CAS approaches engineering
- Characterize the visual domain first: variance, failure cases, labeling budget.
- Select architectures by constraint set — accuracy, latency, memory, power — not by leaderboard rank.
- Design the data engine early; model improvements plateau while data quality keeps paying.
- Ship with calibration and confidence handling, so downstream code knows what to trust.
Outcomes
What can result
- Perception with known, measured behavior in the deployment environment.
- A data engine that keeps improving the system after launch.
- Latency and cost matched to the real deployment target.
Delivery
What the process looks like
01 · Domain audit
Visual variance analysis, failure taxonomy, data and labeling plan.
02 · Baseline
Fast baseline system to anchor metrics and de-risk the program.
03 · Architecture
Model selection and adaptation against the constraint set.
04 · Data engine
Labeling workflow, active learning, hard-mining loops.
05 · Deploy
Edge or cloud deployment, monitoring, drift and recalibration.
Preparation
What a client should prepare
- Representative imagery or video, including hard cases.
- Deployment constraints: hardware, latency, connectivity.
- Definition of what a wrong perception decision costs.
Outcomes depend on data, constraints and integration reality. CAS states assumptions explicitly and reports negative results when evidence demands them.
Discuss computer vision with an engineer.
Bring the problem; we will bring the architecture, the evaluation plan and the honest feasibility read.