Abstract

Detecting meaningful contours in noisy or unevenly illuminated images is a critical computer vision task. The study presents a flexible approach for adaptive thresholding, morphological processing, and contour detection, implemented in an open-source Python script leveraging OpenCV’s cv2 library. The code allows real-time adjustment of threshold parameters, morphological operations, and minimum contour area, enabling practitioners to fine-tune the detection process. Drawing on insights from the imbalanced classification literature—particularly the techniques discussed in Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification—highlights the importance of parameter tuning and context-specific optimization. Just as class imbalance treatments (e.g., SMOTE, Class Weights, Decision Threshold Calibration) significantly affect minority class detection, our results demonstrate that even minor changes to threshold and morphological parameters can substantially impact detection and quality of resulting contours. This underscores the need for adaptive, data-sensitive approaches that align algorithmic choices with the unique characteristics of the images under study.

Introduction

Contour detection is a cornerstone of many computer vision workflows, enabling tasks such as object localization, shape analysis, and boundary extraction. Traditional binary thresholding approaches often underperform in scenarios with uneven illumination or substantial noise. To address these challenges, adaptive thresholding methods apply local statistics to dynamically separate foreground from background. However, selecting appropriate parameter settings—akin to tuning class imbalance strategies in machine learning—can be non-trivial and highly dependent on data characteristics.

In machine learning, especially in the realm of imbalanced datasets, researchers have demonstrated that methods like SMOTE, class weight adjustments, and decision threshold calibration can significantly alter a model’s performance on minority classes (Chawla et al., 2002; Kovács, 2019; Elor & Averbuch-Elor, 2022). These techniques treat the imbalance problem at different stages—data preprocessing, model training, or post-training decision calibration—and must be carefully chosen based on the specific dataset and target metrics (e.g., F1-score, AUC, PR-AUC). The work presented here draws conceptual parallels: just as machine learning researchers must experiment with imbalance treatments, computer vision engineers often must systematically explore threshold parameters, morphological kernels, and iteration counts to ensure robust contour detection.

Methods

1. Adaptive Thresholding
   This study uses an adaptive thresholding approach (mean-based) from the OpenCV library to convert images to binary form:

binary_image = cv2.adaptiveThreshold(
    image, 255, 
    cv2.ADAPTIVE_THRESH_MEAN_C, 
    cv2.THRESH_BINARY, 
    block_size, 
    C_param
)

Drawing a parallel to the Balancing the Scales study, choosing block_size (akin to hyperparameters for SMOTE or class weights) can drastically change detection outcomes. Larger window sizes generalize thresholding but can lose detail, while smaller sizes capture local variations but increase noise.

2. Morphological Transformations
   Just as minority oversampling can “inflate” underrepresented classes, morphological dilation can artificially expand foreground regions. Conversely, erosion acts like a conservative classifier threshold—removing small, scattered areas. We implement dial-in controls for the iteration counts of each operation:

binary_image = cv2.dilate(binary_image, kernel, iterations=dilate_iterations)
binary_image = cv2.erode(binary_image, kernel, iterations=erode_iterations)

These manipulations resemble the post-training “Decision Threshold Calibration” used to balance recall and precision in imbalanced data contexts (Van Hulse et al., 2007). Similarly, in the current contour detection, too many dilations can boost false positives, whereas too many erosions may lose crucial details.

3. Contour Detection and Filtering
   After thresholding and morphological transformation, this study extract contours using:

contours, _ = cv2.findContours(
    binary_image, 
    cv2.RETR_EXTERNAL, 
    cv2.CHAIN_APPROX_SIMPLE
)

The study filter them by minimum area—analogous to focusing on “minority class predictions” above a certain confidence threshold:

filtered_contours = [
    contour for contour in contours 
    if cv2.contourArea(contour) > min_area
]

This step can be likened to post-hoc optimization of a classification threshold, where practitioners filter out low-confidence predictions to mitigate false positives.

4. Real-Time Parameter Tuning
   The code provides trackbars in an OpenCV window, enabling users to modify parameters—block size, C_param, morphology iterations, and more—in real time. This interactive interface parallels hyperparameter tuning in machine learning studies, such as searching for the optimal minority class weight or the best SMOTE oversampling factor.

Results & Discussion

The study tested the approach on a range of images with varying lighting conditions and noise. The results highlight the sensitivity of contour detection to parameter settings:

Block Size & C: Larger values smoothed noise but lost thin edges. Smaller sizes captured more details but introduced spurious contours.

Dilation & Erosion: A fine balance was needed. Over-dilation merged distinct objects, while over-erosion fragmented coherent shapes.
Minimum Contour Area: Adjustments filtered out clutter but risked discarding genuinely small objects.

These findings echo the Balancing the Scales conclusion that no single strategy universally dominates. Like the decision threshold calibration in imbalanced classification, adjusting morphological iterations or threshold parameters can yield large gains but must be tailored to the dataset (image) in question.

Conclusion

The adaptive thresholding and contour detection pipeline underscores the importance of parameter tuning in vision tasks, much like hyperparameter and threshold tuning in imbalanced classification. Inspired by methods such as SMOTE, class weights, and decision threshold adjustments, we illustrate how seemingly small changes in preprocessing or post-processing steps can yield significant improvements in performance metrics, whether in F1-score for classification or in contour accuracy for image segmentation.

Future work may include:

Automated Parameter Optimization: Incorporating grid search or Bayesian optimization for threshold and morphology hyperparameters.
Evaluating Advanced Filters: Exploring advanced oversampling analogies in image processing, such as region-level or edge-level SMOTE-like strategies.
Dataset Diversity: Testing on diverse image sets (e.g., medical, satellite, industrial) to highlight domain-specific best practices.
By linking techniques from both imbalanced classification and contour detection, we emphasize a unifying perspective: thoughtful parameter selection, guided by iterative experimentation and domain knowledge, remains central to success in any complex data-driven task.

References

Chawla, N.V., Bowyer, K.W., Hall, L.O., & Kegelmeyer, W.P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.
Kovács, G. (2019). SMOTE-variants: A Python implementation of 85 minority oversampling techniques. Neurocomputing, 366, 352-354.
Elor, Y., & Averbuch-Elor, H. (2022). To SMOTE, or not to SMOTE? arXiv preprint arXiv:2201.08528.
Van Hulse, J., Khoshgoftaar, T.M., & Napolitano, A. (2007, June). Experimental perspectives on learning from imbalanced data. In Proceedings of the 24th International Conference on Machine Learning (pp. 935-942).
Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification. [Online].