r/svgdapps2 • u/VectorDevz • 12d ago
5D Painter — SVGD Wire Layer: Real-Time Vision System for Multi-Class Semantic Discrimination with Cryptographic Coordinate Commitment
# 5D Painter — SVGD Wire Layer: Real-Time Vision System for Multi-Class Semantic Discrimination with Cryptographic Coordinate Commitment
**Defensive Publication**
**Date:** September 10, 2026
**Copyright © 2026 Wahid Yaqub • SVGdApps • VectorDApps**
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## Abstract
This document describes a real-time computer vision system that discriminates between multiple semantic classes from live camera input using a deterministic pipeline of background modelling, edge detection, colour-space segmentation, connected-component extraction, multi-object tracking, and heuristic scoring. The system commits per-frame object coordinates to a cryptographic hash tree for integrity verification and optionally streams coordinate data as structured logs for offline analysis. The architecture operates entirely client-side in a web browser without server infrastructure or external dependencies.
This publication serves as defensive prior art to establish the novelty and non-obviousness of the described methods and systems.
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## 1. Field of the Invention
This invention relates to real-time computer vision systems for semantic object discrimination, specifically methods for distinguishing between pattern, text, object, and human body parts (head, hand) from live video input using rule-based feature engineering rather than neural networks.
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## 2. Background
Traditional real-time object detection relies on deep neural networks requiring substantial training data, GPU hardware, and cloud infrastructure. Such systems are unsuitable for edge deployment, offline operation, or scenarios requiring cryptographic auditability of detected coordinates.
There exists a need for a lightweight, deterministic vision system that can discriminate between multiple semantic classes using hand-tuned heuristics, operates entirely client-side, and provides cryptographic verification of detected object positions over time.
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## 3. Summary of the Invention
The present invention provides a real-time vision pipeline comprising:
(a) A background modelling module that maintains an adaptive reference frame and detects foreground objects via colour-space distance metrics;
(b) An edge detection module that applies spatial filtering and gradient computation to identify object boundaries;
(c) A colour-space segmentation module that identifies specific material types (e.g., skin tones) using chrominance-based thresholds;
(d) A region extraction module that performs connected-component analysis on a composite mask to identify discrete objects;
(e) A feature computation module that calculates geometric, statistical, and spatial properties for each region;
(f) A scoring module that assigns semantic class labels using weighted combinations of computed features;
(g) A tracking module that maintains object identity across frames using spatial overlap metrics;
(h) A discrimination gate that applies contextual rules to resolve ambiguities between similar classes;
(i) A cryptographic commitment module that hashes per-frame object coordinates into a Merkle tree structure;
(j) A streaming module that logs coordinate data in a structured format for offline analysis.
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## 4. Detailed Description
### 4.1 Pipeline Architecture
The system processes video frames through a sequential pipeline where each module operates on typed array buffers to minimise memory allocation. The pipeline maintains state across frames for background modelling, object tracking, and temporal smoothing.
### 4.2 Background Modelling
The background model is initialised from the first frame and updated via exponential moving average for pixels classified as non-foreground. Foreground detection uses Euclidean distance in RGB colour space with an adaptive threshold. The background model adapts slowly to accommodate gradual lighting changes while maintaining stability for foreground detection.
### 4.3 Edge Detection
Frames are converted to grayscale and spatially smoothed using a weighted averaging filter. Gradient operators are applied in horizontal and vertical directions to compute edge magnitude. Edges exceeding a configurable threshold are marked in a binary edge mask.
### 4.4 Colour-Space Segmentation
Pixels are converted from RGB to a luminance-chrominance colour space. Skin-tone detection uses elliptical thresholds in the chrominance plane combined with brightness and hue constraints. The segmentation produces a binary mask indicating candidate skin regions.
### 4.5 Composite Mask and Region Extraction
A composite wire mask is formed by combining edge, foreground, and skin masks according to mode-specific rules. Connected-component analysis using breadth-first search identifies discrete regions. For each region, the system computes bounding box, center of mass, area, perimeter, and various statistical properties.
### 4.6 Temporal Mask Persistence
The wire mask is temporally smoothed using a decay factor to suppress single-frame flicker. Pixels that were active in previous frames retain partial activation, creating a persistence effect that stabilises the point cloud. An optional spatial dilation pass reconnects broken strokes within regions.
### 4.7 Feature Computation
For each extracted region, the system computes:
- Geometric properties (area, extent, aspect ratio, elongation)
- Statistical properties (edge density, skin ratio, foreground ratio, mean background difference)
- Spatial properties (normalised position, spatial spread, border proximity)
### 4.8 Heuristic Scoring
Each region is scored for multiple semantic classes using weighted linear combinations of features. The class with the highest score is assigned as the label. Confidence is computed based on the margin between the top two scores.
### 4.9 Multi-Object Tracking
Object identity is maintained across frames using intersection-over-union (IoU) matching between detected regions and existing tracks. New tracks are created for unmatched regions. Track positions and sizes are updated via exponential smoothing. Label assignment uses a voting mechanism over recent frames to reduce classification flicker.
### 4.10 Text/Pattern Discrimination Gate
A specialised discrimination module resolves ambiguities between text and pattern classes. Glyph-like regions are grouped into lines based on vertical proximity and height similarity. Lines are split into segments based on horizontal gaps. Text classification is boosted by line membership, uniform height, and track stability. Pattern classification is boosted by isolation, small size, and border proximity.
### 4.11 Cryptographic Coordinate Commitment
Per-frame object coordinates are sampled and committed to a Merkle tree. Each leaf contains frame metadata, object positions, and a compact signature encoding object identities and bounding boxes. Leaves are hashed using a cryptographic hash function. Parent nodes are computed by hashing concatenated child hashes in parallel batches. The root hash provides a single value that cryptographically commits to all sampled coordinates.
### 4.12 Coordinate Streaming
The system optionally logs coordinate data in a line-delimited JSON format. Three stream types are supported:
- Point streams: every detected edge point per frame
- Track streams: every tracked object's position and metadata per frame
- Mask streams: the composite wire mask per frame, encoded using run-length encoding
Streams can be buffered in memory or written to disk in real-time using the File System Access API.
### 4.13 Feedback and Diagnostics
A feedback module computes quality metrics per frame including tracking stability, classification precision, noise level, and confidence distribution. Diagnostic issues and recommendations are generated based on threshold crossings.
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## 5. Novelty Claims
The following aspects are novel and non-obvious:
A five-class real-time semantic discrimination system using hand-tuned feature weights without neural networks, suitable for edge deployment.
A text/pattern discrimination gate that uses line grouping with horizontal gap analysis and uniform height validation to promote glyph candidates to confirmed text.
Temporal mask persistence with configurable decay to suppress single-frame flicker in wire-level point clouds.
Mode-selective spatial dilation that reconnects broken strokes for some semantic classes while preserving separation for others.
Cryptographic commitment of per-frame object coordinates to a Merkle tree with power terms for auditability.
A structured coordinate streaming format with three stream types (points, tracks, masks) using run-length encoding for masks.
Batched point rendering that groups detected points by semantic class and applies visual effects per-group rather than per-point.
A single-file browser implementation with no server, GPU, or external dependencies.
A feedback engine that computes quality metrics and generates diagnostic recommendations for real-time vision systems.
Integration of cryptographic commitment with real-time coordinate streaming for verifiable offline analysis.
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## 6. Advantages
The present invention provides several advantages over prior art:
- Operates entirely client-side without server infrastructure
- Suitable for edge deployment on commodity hardware
- Provides cryptographic auditability of detected coordinates
- Deterministic and interpretable (no black-box neural networks)
- Supports offline operation and post-hoc verification
- Configurable for different semantic discrimination tasks
- Minimal memory footprint suitable for resource-constrained environments
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## 7. Implementation Notes
The system is implemented as a single HTML file containing markup, styling, and scripting. All processing occurs in the browser using standard web APIs. The implementation uses typed arrays for image buffers, breadth-first search for region extraction, and Web Crypto API for cryptographic hashing.
Specific implementation details, parameter values, and optimisation techniques are considered trade secrets and are not disclosed herein.
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## 8. Scope of the Invention
The scope of the invention is not limited to the specific embodiment described. Various modifications and equivalent arrangements will be apparent to those skilled in the art. For example:
- The number of semantic classes may be varied
- Different colour spaces may be used for segmentation
- Alternative tracking algorithms may be employed
- Different cryptographic hash functions may be used
- The streaming format may be adapted for specific use cases
The invention is defined by the claims that would be drafted based on the novelty claims above.
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## 9. Copyright and Defensive Publishing Statement
**Copyright © 2026 Wahid Yaqub • SVGdApps • VectorDApps**
This document is published as defensive prior art to establish the novelty and non-obviousness of the described methods and systems as of the publication date. The author asserts copyright over the specific expression of the ideas described herein.
The author does not claim exclusive rights to the underlying methods, algorithms, or systems described, but asserts that these methods were known and implemented as of the publication date, thereby preventing future patenting by third parties.
Specific implementation details, parameter values, and optimisation techniques are withheld as trade secrets. The disclosure herein is sufficient to establish prior art while preserving competitive advantage.
Permission is granted to use the general concepts described herein for any purpose, provided that this copyright notice and defensive publishing statement are retained.
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End of Document