Information Theoretic Modeling of High Precision Disparity Data for Lossy Compression and Object Segmentation

In this paper, we study the geometry data associated with disparity map or depth map images in order to extract easy to compress polynomial surface models at different bitrates, proposing an efficient mining strategy for geometry information. The segmentation, or partition of the image pixels, is viewed as a model structure selection problem, where the decisions are based on the implementable codelength of the model, akin to minimum description length for lossy representations. The intended usage of the extracted disparity map is to provide to the decoder the geometry information at a very small fraction from what is required for a lossless compressed version, and secondly, to convey to the decoder a segmentation describing the contours of the objects from the scene. We propose first an algorithm for constructing a hierarchical segmentation based on the persistency of the contours of regions in an iterative re-estimation algorithm. Then, we propose a second algorithm for constructing a new sequence of segmentations, by selecting the order in which the persistent contours are included in the model, driven by decisions based on the descriptive codelength. We consider real disparity datasets which have the geometry information at a high precision, in floating point format, but for which encoding of the raw information, in about 32 bits per pixels, is too expensive, and we then demonstrate good approximations preserving the object structure of the scene, achieved for rates below 0.2 bits per pixels.

Identification of Temperature Dynamics Using Subspace and Machine Learning Techniques

Identification, estimation, and control of temperature dynamics are ubiquitous and challenging control engineering problems. The main challenges originate from the fact that the temperature dynamics is usually infinite dimensional, nonlinear, and coupled with other physical processes. Furthermore, the dominant system time constants are often long, and due to various time constraints that limit the measurement time, we are only able to collect a relatively small number of input-output data samples. Motivated by these challenges, in this paper we present experimental results of identifying the temperature dynamics using subspace and machine learning techniques. We have developed an experimental setup consisting of an aluminum bar whose temperature is controlled by four heat actuators and sensed by seven thermocouples. We address noise reduction, experiment design, model structure selection, and overfitting problems. Our experimental results show that the temperature dynamics of the experimental setup can be relatively accurately represented by low-order models.