Optimal Estimation of Matching Constraints

International audienceWe describe work in progress on a numerical library for estimating multi-image matching constraints, or more precisely the multi-camera geometry underlying them. The library will cover several variants of homographic, epipolar, and trifocal constraints, using various different feature types. It is designed to be modular and open-ended, so that (i) new feature types or error models, (ii) new constraint types or parametrizations, and (iii) new numerical resolution methods, are relatively easy to add. The ultimate goal is to provide practical code for stable, reliable, statistically optimal estimation of matching geometry under a choice of robust error models, taking full account of any nonlinear constraints involved. More immediately, the library will be used to study the relative performance of the various competing problem parametrizations, error models and numerical methods. The paper focuses on the overall design, parametrization and numerical optimization issues. The methods described extend to many other geometric estimation problems in vision, e.g. curve and surface fitting

The Value-of-Information in Matching with Queues

We consider the problem of \emph{optimal matching with queues} in dynamic systems and investigate the value-of-information. In such systems, the operators match tasks and resources stored in queues, with the objective of maximizing the system utility of the matching reward profile, minus the average matching cost. This problem appears in many practical systems and the main challenges are the no-underflow constraints, and the lack of matching-reward information and system dynamics statistics. We develop two online matching algorithms: Learning-aided Reward optimAl Matching ($\mathtt{LRAM}$) and Dual-$\mathtt{LRAM}$ ($\mathtt{DRAM}$) to effectively resolve both challenges. Both algorithms are equipped with a learning module for estimating the matching-reward information, while $\mathtt{DRAM}$ incorporates an additional module for learning the system dynamics. We show that both algorithms achieve an $O(\epsilon+\delta_r)$ close-to-optimal utility performance for any $\epsilon>0$, while $\mathtt{DRAM}$ achieves a faster convergence speed and a better delay compared to $\mathtt{LRAM}$, i.e., $O(\delta_{z}/\epsilon + \log(1/\epsilon)^2))$ delay and $O(\delta_z/\epsilon)$ convergence under $\mathtt{DRAM}$ compared to $O(1/\epsilon)$ delay and convergence under $\mathtt{LRAM}$ ($\delta_r$ and $\delta_z$ are maximum estimation errors for reward and system dynamics). Our results reveal that information of different system components can play very different roles in algorithm performance and provide a systematic way for designing joint learning-control algorithms for dynamic systems

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Quality estimation and optimization of adaptive stereo matching algorithms for smart vehicles

Stereo matching is a promising approach for smart vehicles to find the depth of nearby objects. Transforming a traditional stereo matching algorithm to its adaptive version has potential advantages to achieve the maximum quality (depth accuracy) in a best-effort manner. However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage/frequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road