MistSeeker Vision — Vision Education Pipeline (MVP)

MistSeeker Vision is a vision education / preprocessing pipeline designed to decompose images and videos into structured, explainable features for downstream AI training and multimodal reasoning.

Instead of competing with end-to-end vision models, this project focuses on what happens before training: how visual inputs can be modularly separated (2D / 3D / time), physically decomposed (color / curvature / shadow), and formatted for reasoning models (e.g., LLMs).

What this project is

MistSeeker Vision is a vision education / preprocessing pipeline that prepares learning-ready visual data by exposing interpretable intermediate representations.

What this project is NOT

This is not a SOTA end-to-end vision model and does not aim to replace CLIP, ViT, SAM, or similar architectures.

1. Overview

This MVP provides a modular pipeline with the following principles:

• Explicit modularization
• Separation of 2D / 3D / temporal (4D) modes
• Physics-inspired feature decomposition (color regions, curvature cues, shadow cues)
• Text / non-text separation (OCR as an independent module)
• Structured outputs for downstream training and reasoning

Goal: Produce clearer, structured vision representations to improve training stability, interpretability, and dataset quality.

2. Why this approach?

2.1 Structural ambiguity in 2D perception

Many pipelines learn physical cues (curvature / shadow / reflection) implicitly, which reduces interpretability and increases data dependency.
MistSeeker Vision exposes these cues as explicit intermediate representations.

2.2 2D / 3D / time (4D) separation

Real-world scenes are 3D, and videos introduce time (t).
This project provides a modular path to extend image processing into frame-based temporal processing.

2.3 Training efficiency and dataset preparation

This pipeline converts raw images and videos into learning-ready structured data (JSON reports + feature vectors), supporting:

• Dataset inspection
• Labeling assistance
• Multimodal reasoning experiments

3. Pipeline Flow

[Image / Video Input] → Dimension detection (2D / 3D / 4D) → Object extraction (CIELAB-based region definition) → Curvature cues (between regions) → Shadow / illumination cues → OCR (independent) → Structured output (JSON + vector representations) → (Optional) LLM formatting / explanation layer

4. Models & Definitions (Conceptual)

4.1 Object definition (CIELAB intersection)

An object region is defined as a pixel set in CIELAB ranges:

O_i = { (x,y) | L(x,y) ∈ [L_min, L_max] ∧ a(x,y) ∈ [a_min, a_max] ∧ b(x,y) ∈ [b_min, b_max] }

4.2 Curvature cues (region-to-region structural discrepancy)

κ_ij = λ(O_i, O_j) ≈ ΔS_ij / ΔX_ij

• 2D: brightness / texture changes
• 3D: normals / surface changes (when available)
• 4D: temporal tracking with time variable (t)

4.3 Shadow / illumination cues

I = ρ (L · N)

Used as an interpretable cue for illumination, reflection, and shadow separation.

5. Installation

Requirements

• Python 3.8+
• Tesseract OCR (system installation required)

macOS

brew install tesseract
brew install tesseract-lang

Ubuntu / Debian

sudo apt-get install tesseract-ocr
sudo apt-get install tesseract-ocr-kor

Windows
	•	Install Tesseract OCR and ensure tesseract is available in PATH
	•	WSL is recommended for smoother setup

Python dependencies

pip install -r requirements.txt


6. Usage

Single image

python app.py path/to/image.jpg --output result.json

Video processing (4D mode)

python app.py path/to/video.mp4 --video --output result.json
python app.py path/to/video.mp4 --video --frame-interval 5 --max-frames 100

Batch processing

python app.py path/to/images/ --batch --output-dir results/

Optional LLM integration

LLM integration is optional and used only for formatting or explaining extracted features.

export OPENAI_API_KEY=...
python app.py path/to/image.jpg --use-llm --output result.json

Basic vs Advanced
	•	--basic-mode: faster, core features only
	•	default mode: includes curvature and shadow cues


7. MVP Features
	•	Dimension detection: 2D / 3D / 4D (Time)
	•	Physics-inspired feature extraction:
	•	Color regions (CIELAB)
	•	Shadow / illumination cues
	•	Curvature cues (2D / 3D / 4D)
	•	Object extraction and attribute representation
	•	OCR (multi-language)
	•	Structured JSON output
	•	Batch processing support
	•	Optional LLM formatting layer


8. Expected Benefits
	•	Improved dataset clarity and interpretability
	•	Better training stability through structured inputs
	•	Modular extension path (depth reconstruction, advanced physics models, GPU acceleration)
	•	Useful for robotics, simulation, and multimodal experiments


9. Project Structure

MistSeeker-vision/
├── app.py                      # CLI entrypoint
├── requirements.txt            # Dependencies
├── README.md                   # Documentation
├── src/
│   ├── __init__.py
│   ├── vision_pipeline.py      # Pipeline orchestrator
│   ├── dimension_detector.py   # 2D / 3D / 4D detection
│   ├── color_shadow_curvature.py # Convenience aggregation module
│   ├── curvature_model.py      # Curvature cues
│   ├── shadow_model.py         # Shadow / illumination cues
│   ├── object_detector.py      # Region / object extraction
│   ├── text_recognizer.py      # OCR module
│   ├── video_processor.py      # Video processing (4D extension)
│   └── llm_integration.py      # Optional LLM formatting layer
├── assets/
└── examples/


10. Roadmap
	•	Deep learning-based dimension detection
	•	Depth Anything V2 integration for 3D reconstruction
	•	GPU / TPU acceleration
	•	Web UI (Streamlit / Gradio)
	•	Additional formats (e.g., DICOM)


Notes
	•	This repository is an MVP focused on demonstrating a modular, explainable perception pipeline.
	•	Modules are intentionally designed to be extended for production needs.

Contact: contact@convia.vip