Uncertain Shape and Deformation Recognition Using Wavelet-Based Spatiotemporal Features

This paper proposes a wavelet-based spatiotemporal feature extraction method for recognizing uncertain shapes and their deformations. Uncertain shapes, such as hand gestures and fetal movements, exhibit individual and trial-dependent variations, making their accurate recognition challenging. Our approach constructs shape feature vectors by integrating wavelet coefficients across multiple scales, ensuring robustness to rotation and translation. By analyzing the temporal evolution of these features, we can detect and quantify deformations effectively. Experimental evaluations demonstrate that the proposed method accurately identifies shape differences and tracks deformations, outperforming conventional approaches such as template matching and neural networks in adaptability and generalization. We further validate its applicability in tasks such as hand gesture recognition and fetal movement analysis from ultrasound videos. These results suggest that the proposed wavelet-based spatiotemporal feature extraction technique provides a reliable and computationally efficient solution for recognizing and tracking uncertain shapes in dynamic environments.