Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator-combined with specialized responsive visualizations-allows comprehensive interactive evaluation. Our fast framework allows feedback-driven development and fine-tuning of new order selection criteria in real-time. We demonstrate the applicability of our approach in three use-cases for two general as well as a real-world example.
We present a novel approach for compression of depth maps based on Compressed Sensing and Sparse Coding. Our proposed scheme compresses and stores the depth map, and then-during the decompression step----makes use of the readily available additional RGB information to guide the reconstruction. We introduce additional constraints to the underlying optimiza tion problem enforcing the correctness of the RGB image in the decompression step. A comparison with established compression schemes shows that our proposed method leads to a lower error rate at high com pression ratios.
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