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==== Report draft ====
### **1. Executive Summary**

This document outlines the research plan for advancing our automatic Region of Interest (ROI) boxing capabilities. The project's ultimate goal is to develop a highly accurate, robust, and flexible system for identifying ROIs in images. We will begin by analyzing our current baseline—a heuristic algorithm that also serves as our ground truth generator. Subsequently, we will explore a phased approach of progressively more sophisticated methods: (1) a fully automatic **zero-shot** model, (2) a user-guided **prompt-based** model, (3) an interactive **human-in-the-loop** extension, and finally, (4) a data-driven **fine-tuning** approach. Each phase will be evaluated on accuracy, efficiency, and the required level of human intervention, providing a comprehensive understanding of the trade-offs involved.

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### **2. Introduction & Project Rationale**

**2.1. Background**
Region of Interest (ROI) boxing is a critical preliminary step in many image analysis pipelines, including [mention your specific domain, e.g., medical image analysis, industrial quality control, autonomous vehicle perception]. Accurate and efficient ROI detection isolates relevant objects or areas, significantly improving the performance and reducing the computational cost of subsequent processing tasks.

**2.2. Project Aim and Significance**
The primary aim of this research is **to evolve our ROI detection capabilities from a rigid, heuristic-based method to a flexible, learning-based system that can operate across various levels of automation.**

This project is significant because:
*   **It addresses the limitations of our current method:** The existing heuristic algorithm, while functional, may be brittle, fail on unseen edge cases, and lack adaptability.
*   **It reduces manual labor:** By exploring zero-shot and fine-tuning methods, we aim to minimize or eliminate the manual effort currently required for correcting failed detections or annotating new datasets.
*   **It enhances interactivity and precision:** Prompt-based and human-in-the-loop methods will empower users to guide the model with minimal effort, achieving high precision on difficult or ambiguous targets.
*   **It establishes a robust evaluation framework:** This research will systematically compare different approaches, providing clear insights into the cost-benefit of each method.

==== infographic contents format ==== 

# Project Report – Automatic ROI Boxing

## 1. Executive Summary
*(≤ ½ page)*  
- **Goal:** Fully-automated Region-of-Interest (ROI) boxing for <your domain>.  
- **Current bottleneck:** Manual annotation & rigid heuristics.  
- **Solution path:** Iterate from heuristic baseline → zero-shot → prompt-based → prompt + HITL → fine-tuning.  
- **Expected impact:** <e.g., 5× faster data prep, 30 % precision gain, etc.>

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## 2. Background & Previous Method

| Item | Description |
|------|-------------|
| Heuristic algorithm | Rule-based edge detection + morphological ops + size filter. |
| Ground-truth generation | Batch run heuristic → manual clean-up (≈  N hours / M samples). |
| Known issues | Misses small ROIs, over-boxes background, brittle to lighting changes. |
| Baseline metrics | mAP @ 0.5 IOU = 0.62; Precision = 0.70; Recall = 0.55. |

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