ABSTRACT. We have proposed a novel model-based compression technique for nonstationary landmark shape data extracted from video sequences.

The main goal is to develop a technique for the compact storage of landmark shape data. We use nonstationary shape activity (NSSA) to model the shape sequences.

It was found that NSSA outperforms both SSA and ASM in terms of compressibility for a given distortion tolerance, so NSSA based compression technique could ...

Model-based compression of nonstationary landmark shape sequences ; Matching shape sequences in video with applications in human movement analysis. Veeraraghavan ...

Samarjit Das, Namrata Vaswani: Model-based compression of nonstationary landmark shape sequences. ICIP 2008: 1168-1171. manage site settings.

The main goal is to develop a technique for the compact storage of landmark shape data. We use Nonstationary Shape Activity (NSSA) to model the shape sequences.

The key contribution of this work is a novel approach to define a generative model for both 2D and 3D nonstationary landmark shape sequences. Greatly improved ...

Abstract—The goal of this work is to develop statistical models for the shape change of a configuration of “landmark” points (key.

o Samarjit Das and Namrata Vaswani, Model-based Compression of Nonstationary Landmark Shape Sequences, IEEE Intl. Conf. Image Proc. (ICIP), 2008. o Bi Song ...

Sep 2, 2024 · This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency.