Contour Tracking Algorithm (WtL2)

• Contour tracking algorithm is a method that computes closed, 1-pixel-wide object outlines for precise image segmentation.
• It uses deep learning and recurrent CNN 'walkers' to iteratively follow and capture fine boundary details across various modalities.
• WtL2 enhances segmentation quality through modular retraining and adaptive binarization, achieving high IoU in complex RGB and IR applications.

A contour tracking algorithm is a computational method for tracing, representing, and ultimately segmenting object boundaries within an image or sequence of images. The primary objective is to generate a closed, 1-pixel-wide outline of an object, which can then be binarized to yield a high-quality mask for detailed segmentation tasks. Techniques vary in their representation of contours, tracking mechanisms, robustness to different image modalities (e.g., IR, RGB), and their strategies for addressing gap closure, fine detail recovery, and segmentation quality. The Walk the Lines 2 (WtL2) algorithm exemplifies state-of-the-art advances in this space, addressing both the generation of single-object, finely-detailed masks and the generalization across challenging domains such as infrared and multi-category RGB images (Kelm et al., 7 Nov 2025).

1. Principles and Evolution of Contour Tracking Algorithms

Classical contour tracking approaches (e.g., active contours, edge-following methods) typically exploit local gradient features, energy minimization, or parametric curve evolution to represent and evolve object boundaries. Recent advances have shifted toward data-driven, neural-network-based methods that operate on soft contour probability maps derived from deep networks, producing rasterized, closed contours that better capture fine-scale object geometry.

WtL ("Walk the Lines") introduced a deep learning architecture that “walks” around a soft contour map using a recurrent walker CNN, constructing a closed, 1-pixel-wide outline. Walk the Lines 2 (WtL2) generalizes this framework to infrared imagery and diverse RGB categories by decoupling and retraining contour detection modules and adapting the mask binarization process—a strategy targeted at niche applications where generic NMS or polygon-based techniques underperform in closure or detail (Kelm et al., 7 Nov 2025).

2. Stepwise Description and Architecture of WtL2

WtL2 operates in a three-stage pipeline:

1. Soft Contour Detection:
   • A neural contour detector such as RefineContourNet receives the input image $I$. For RGB: $I\in\mathbb{R}^{H\times W\times 3}$; for IR: $I\in\mathbb{R}^{H\times W}$.
   • Outputs a soft map $C(x, y)\in[0, 1]$, indicating object-boundary likelihood at each pixel.
   • Thinning via NMS is possible for seeding, but WtL2 avoids it in the main tracking step to retain boundary continuity and detail.
2. Iterative Contour Tracking:
   • An ensemble of CNN “walkers” is initialized at high-confidence seed points.
   • Each walker at time $t$ observes a $7 \times 7$ rotated patch extracted from $I$ and $C$, aligned to the prior step’s walking direction $\phi_{t-1}$.
   • The walker CNN predicts a direction $\Delta\theta = f_{\mathrm{walk}}(P^{(t)})$ discretized into one of eight possible steps $\Theta = \{-135^\circ, -90^\circ, ..., 180^\circ\}$.
   • Next position: $$p^{(t+1)} = p^{(t)} + s \cdot (\cos{\Theta}, \sin{\Theta})$$ with preferential $s=1$ to preserve thin structures.
   • Walkers terminate upon revisiting a pixel or leaving the image domain.
   • Aggregation of all surviving walker tracks yields a closed, 1-pixel-wide contour $L$.

   Pseudocode (core loop):

function WtL2_Segment(I): C = RefineContourNet(I) Cn = NMS(C) # optional; for seed selection only seeds = topK_pixels(Cn) walkers = spawn_walkers(seeds) L = empty_contour_map() while any(walkers alive): for w in walkers: P = extract_rotated_patch(I, C, w.position, w.prev_dir) Δθ = w.CNN(P) s, Θ = quantize(Δθ) p_new = w.position + s*(cosΘ, sinΘ) if visited(p_new) or out_of_bounds(p_new): w.terminate() else: L[p_new]=1 w.position = p_new; w.prev_dir = Θ M = binarize_contour(C, L) return M

1. Contour Binarization:
   • Given the rasterized $L$, a robust binarization algorithm iteratively identifies a “separation line” through a contour point, testing for foreground/background connectivity, and searches for a threshold $T^*$ on $C(p)$ that reconnects the region into a single, closed area.
   • This method generalizes earlier ship-specific heuristics to arbitrary object shapes and contexts.

3. Adaptation Across Modalities and Categories

WtL2 achieves strong cross-domain performance through modular adaptation:

• Infrared Ship Segmentation:
  • Lack of IR-ship contour labels addressed by self-training: 227 pseudo-labeled images generated from raw coastal IR data and MassMIND; refined using CRFs and self-training to train RefineContourNet-IR.
  • The core walking CNN remains unchanged, indicating transferability of tracking policy; only the contour detector is retrained.
• Multi-category RGB Segmentation:
  • New “DOC” dataset: 90 finely annotated COCO images (50 for validation) re-labeled for precise contours.
  • Both walking CNN and binarizer retrained on diverse classes (transport, animals).
  • Binarization scheme fully generalized, removing waterline heuristics in favor of topological separation-line detection.

A plausible implication is that WtL2’s modular design enables porting to additional imaging contexts (e.g., medical, industrial) if high-quality contour annotations can be constructed for initial retraining.

4. Quantitative Performance and Comparative Analysis

WtL2 has been benchmarked in three settings: general RGB object segmentation (DOC), IR ship segmentation (DIRSC), and detailed object segmentation.

Closed-Contour and IoU Results:

Method	% Closed Shapes	Mean IoU on Closed
NMS	4%	–
WtL (orig)	56%	44.6
WtL2 (this)	80%	75.7

IR Ship (DIRSC, 10 images):

• RN-IR (baseline): 76.8% mean IoU.
• WtL2 (all images): 52.0% mean IoU (includes failures).
• WtL2 (on closed cases, 6/10): 86.7% mean IoU; individual peaks > 95%.

Detailed Object Segmentation (DOC, 50 RGB):

Method	Precision	Recall	IoU
RN baseline	93.1%	79.0%	75.0
E2EC (sota)	89.3%	89.7%	84.5
WtL2 (all)	89.6%	81.0%	75.7
WtL2 (closed subset)	93.6%	94.8%	89.0

On the subset of images where WtL2 produces a closed contour, it exceeds state-of-the-art contour-based nets in IoU and recovers fine details such as animal limbs and superstructures, although the overall closure success rate remains at 80% for general RGB, 60% for IR ships (on DIRSC).

Qualitative examples demonstrate recovery of fine features (e.g., deer antlers, ship antennae, giraffe ears), substantiating its use in high-detail segmentation scenarios.

5. Applications, Limitations, and Future Research Directions

Applications

• Maritime Surveillance: High-resolution mask generation for ship shape assessment in both RGB and IR.
• Specialized Segmentation: Fine-detailed mask extraction for rare categories (wildlife, industrial, medical), where sampling bias or morphological nuance is critical.
• Data Augmentation: Generation of high-fidelity masks for downstream training of other segmentation networks or synthetic data pipelines.

Limitations

• Failure to yield a closed contour (about 20% on generic scenes) results in an unusable (IoU $\rightarrow$ 0) segmentation, limiting applicability to images with clear foreground/background separation.
• Not suitable as a direct drop-in for unconstrained multi-object detection or general-purpose segmentation pipelines.
• Inference speed is modest (tens of seconds per image), arising from deployment of hundreds to thousands of walkers.

Future Directions

• End-to-End Integration: Unified learning of seeding and walking, possibly with end-to-end differentiable contour formation.
• Accelerated Inference: Transformer-style self-attention applied to the contour domain to parallelize tracking.
• Instance Extension: Support for multi-object segmentation and instance grouping within the same image.
• Adaptive Binarization: Integration of learned, context-sensitive binarization models to improve robustness on ambiguous or structured cluttered backgrounds.
• Video Extension: Temporal smoothing or consistency constraints to track and refine object contours in real-time or sequential frames.

This suggests that WtL2’s design, coupling modular contour detection with a robust walking/tracking engine and general binarization, offers a flexible framework for specialized detail-oriented segmentation tasks, contingent on clear object-background separation and high-quality initial contour detection (Kelm et al., 7 Nov 2025).

6. Relation to Broader Segmentation Methodologies

WtL2’s approach departs from conventional NMS and polygon-based segmenters by enforcing strict closure via dense contour tracking and binarization rather than relying on edge linkages susceptible to breaks. Against state-of-the-art contour nets (e.g., E2EC, DeepSnake), WtL2 empirically achieves higher peak IoUs and enhanced geometric fidelity on the successfully closed subset, particularly where fine structures dictate task success.

A plausible implication is that for tasks where the highest-possible boundary accuracy is imperative, and single, salient foreground objects are reliably isolated, advanced contour tracking algorithms such as WtL2 constitute a preferred solution over both mask-head and polygonal approaches.

7. Summary

Contour tracking algorithms, exemplified by WtL2, have reached a stage where modular adaptation to new modalities (IR, diverse RGB classes), fine-detail recovery, and robust closure via neural “walking” agents yield superior segmentation masks in specialized applications. While not yet general-purpose due to runtime and closure constraints, these methods highlight key advances in the neural representation and rasterization of complex boundaries, setting a foundation for future research into fast, reliable, and detail-aware segmentation pipelines (Kelm et al., 7 Nov 2025).