Evaluation of Real-Time Orientation Estimation Methods for Various Biological Objects with Irregular Shapes

This paper introduces a computationally efficient method for identifying the orientation of objects with diverse geometries and is compared with other popular image processing approaches. The proposed method estimates object orientation by averaging multiple extreme boundary points located at opposite sides of the detected object contour, thereby reducing the sensitivity of the orientation estimation to individual boundary-point perturbations. Unlike learning-based approaches that require model training, the proposed method operates directly on the detected object contour. Across the complete set of 1870 images, it achieved a sample-weighted pooled mean angular error of 16.17° and a mean execution time of 34.28 μs, while the macro-average angular error across the five datasets was 18.404°. The proposed method may be suitable for computationally constrained applications with morphology-dependent accuracy constraints. The proposed technique is evaluated against five established orientation estimation methods: Ellipse Fitting, Principal Component Analysis (PCA), Minimum Area Rectangle, Morphological skeleton, and Hough Transform. Although Hough Transform achieved higher overall angular accuracy, the proposed method required substantially less execution time. Extensive experiments were conducted on 1870 images of various fish species and plant leaves, covering a wide range of resolutions and structural complexities. Experimental results show that acceptable accuracy is achieved while maintaining a significantly lower computational execution time. In shape alignment applications based on Ensemble of Regression Trees (ERT), orientation estimation error within ±20° is typically acceptable; thus, the proposed method can be exploited.