Topological Contour Graph (TCG) Extraction

This repository implements the topological contour graph (TCG) extractor. Originally developed by Yuliang Guo; documented by Hongyi Fan and Chiang-Heng Chien. Used, edited, and updated by Chiang-Heng Chien.

Prerequisite data

1. Edges detected on the image, e.g. SE / third-order edge detector. The edges are formatted in a .edg file.
2. Compute initial curve fragments from edges using dborl_compute_curve_frags to produce a .cem file.

MATLAB usage

The main script is main_TCG.m which undergoes several steps from curve fragements to a topological contour graph:

• loads the image, .edg, and .cem
• breaks contours at corners
• fills large gaps (DP)
• breaks at T-junctions, prunes noise, merges geometrically, classifies junctions (BP)
• breaks at corners again and prunes

C++ port (CPP/)

The C++ code converts the MATLAB code in main_TCG.m (Step 3 and onward). It takes the original image, the edge file (.edg), and the contour fragement file (.cem) as inputs, and returns a new .cem file containing the new contour fragments of the topological contour graph.

Dependencies

• CMake ≥ 3.14
• C++17 compiler
• OpenCV 4 and above built with contib modules

Build

Follow the standard build and compile process to produce the executable TCG.

cd CPP
mkdir -p build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . -j

Once built is successful, provide specific .edg, .cem, and the original image file with optional threshold ori_diff (corner angle threshold in radians used to break long curves) and output file name output.cem. By default, the output file name is <image_name>_tcg_cpp.cem in the current working directory.

./TCG <input.edg> <input.cem> <input.image> [ori_diff_th] [output.cem]

Using the files under example_data,

./build/TCG ./example_data/edges/n03425413_14351.edg \
            ./example_data/contours/n03425413_14351.cem \
            ./example_data/images/n03425413_14351.JPEG \
            ./outputs/n03425413_14351_tcg_cpp.cem

Console output reports fragment counts and timings for each stage.

Visualize C++ results in MATLAB

The output .cem is readable by load_contours / draw_contours:

addpath(genpath('util'));
img = imread('../example_data/images/cable.png');
[CEM_cpp, ~, ~] = load_contours('outputs/cable_tcg_cpp.cem');

figure; imshow(img, 'border', 'tight'); hold on;
draw_contours(CEM_cpp{2}, 0, 1);
title('C++ final contours');

Notes / known differences

• "Corner break" (step 3) matches MATLAB closely on tested examples, e.g. fragment/corner counts.
• "Gap fill" (step 4) differ slightly from MATLAB because morphological skeleton (bwmorph('skel') used in MATLAB versus OpenCV Guo–Hall thinning), distance transform, and imgradient / Sobel are not bit-identical. Later stages inherit that discrepancy.
• Indices inside C++ are 0-based and .cem files keep the usual 0-based edge IDs. MATLAB loaders convert to 1-based when needed.