From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Information Theory and Reliable Communication

Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-and-treat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.

/pdf/health-monitoring-and-management-using-internet-of-things-2lx1tqm8h2.pdf

Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges

This paper studies distributed compression for the uplink of a cloud radio access network where multiple multiantenna base stations (BSs) are connected to a central unit, which is also referred to as a cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and they are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.

Robust and Efficient Distributed Compression for Cloud Radio Access Networks

A visual sensor network (VSN) consists of a large amount of camera nodes which are able to process the captured image data locally and to extract the relevant information. The tight resource limitations in these networks of embedded sensors and processors represent a major challenge for the application development. In this paper, we focus on finding optimal VSN configurations which are basically given by: 1) the selection of cameras to sufficiently monitor the area of interest; 2) the setting of the cameras' frame rate and resolution to fulfill the quality of service requirements; and 3) the assignment of processing tasks to cameras to achieve all required monitoring activities. We formally specify this configuration problem and describe an efficient approximation method based on an evolutionary algorithm. We analyze our approximation method on three different scenarios and compare the predicted results with measurements on real implementations on a VSN platform. We finally combine our approximation method with an expectation-maximization algorithm for optimizing the coverage and resource allocation in VSN with pan-tilt-zoom camera nodes.

/pdf/resource-aware-coverage-and-task-assignment-in-visual-sensor-4gm4b23ymp.pdf

Resource-Aware Coverage and Task Assignment in Visual Sensor Networks

We explore camera scheduling and energy allocation strategies for lifetime optimization in image sensor networks. For the application scenarios that we consider, visual coverage over a monitored region is obtained by deploying wireless, battery-powered image sensors. Each sensor camera provides coverage over a part of the monitored region and a central processor coordinates the sensors in order to gather required visual data. For the purpose of maximizing the network operational lifetime, we consider two problems in this setting: a) camera scheduling, i.e., the selection, among available possibilities, of a set of cameras providing the desired coverage at each time instance, and b) energy allocation, i.e., the distribution of total available energy between the camera sensor nodes. We model the network lifetime as a stochastic random variable that depends upon the coverage geometry for the sensors and the distribution of data requests over the monitored region, two key characteristics that distinguish our problem from other wireless sensor network applications. By suitably abstracting this model of network lifetime and utilizing asymptotic analysis, we propose lifetime-maximizing camera scheduling and energy allocation strategies. The effectiveness of the proposed camera scheduling and energy allocation strategies is validated by simulations.

/pdf/camera-scheduling-and-energy-allocation-for-lifetime-43p3zyzf9t.pdf

Camera Scheduling and Energy Allocation for Lifetime Maximization in User-Centric Visual Sensor Networks

We examine the trade-off between lifetime and distortion in image sensor networks deployed for gathering visual information over a monitored region. Users navigate over the monitored region by specifying a viewpoint that varies with time, and the network attempts to meet the user requirement by synthesizing the desired view using a selection of cameras. We compare two camera selection methods, the first maximizes PSNR for the user's view without considering the cameras' available energy whereas the second uses knowledge of available energy at the cameras to maximize network lifetime. Our simulation results demonstrate a clear trade-off between the two strategies, with selection based on energy alone performing up to 3 dB worse initially than the PSNR based selection yet providing significantly higher coverage lifetime. In addition, we observe that under a fixed total energy constraint, more cameras with lower energy per node are preferable over fewer cameras with higher energy per node. Our results suggest that a hybrid or adaptive camera selection algorithm may provide the optimal lifetime-distortion trade-off.

/pdf/lifetime-distortion-trade-off-in-image-sensor-networks-4snqatn4vk.pdf

Lifetime-Distortion Trade-off in Image Sensor Networks

This paper presents a new fragile watermarking algorithm for binary documents. Watermark embedding in binary documents is announced to be difficult. In this paper, pixel-manipulation is performed in spatial domain to avoid obvious artifact. A novel criterion to define the priority of a pixel to "flip"' is proposed. The algorithm also reaches a new solution of security and embedding capacity issues. Simulation results indicate the effectiveness of the algorithm.

http://ieeexplore.ieee.org/iel5/9834/30993/01452800.pdf

Watermark embedding in binary images for authentication

Plane-Based calibration algorithms have been widely adopted for the purpose of camera calibration task. These algorithms have the advantage of robustness compared to self-calibration and flexibility compared to traditional algorithms which require a 3D calibration pattern. While the common assumption is that the intrinsic parameters during the calibration process are fixed, limited consideration has been given to the general case when some intrinsic parameters maybe varying, others maybe fixed or known. We first discuss these general cases for camera calibration. Using a counting argument we enumerate all cases where plane-based calibration may be utilized and list the number of images required for each of these cases. Then we extend the plane-based framework to the problem of cameras with zoom variation, which is the most common case. The approach presented and may be extended to incorporate additional varying parameters described in the general framework. The algorithm is tested using both synthetically generated images and actual images captured using a digital camera. The results indicate that the method performs very well and inherits the advantage of robustness and flexibility from plane-based calibration algorithms.

/pdf/plane-based-calibration-of-cameras-with-zoom-variation-t8m0pjh3qk.pdf

Plane-based calibration of cameras with zoom variation

We propose a sequential framework for the distributed multiple-sensor estimation and coding problem that decomposes the problem into a series of side-informed source coding problems and enables construction of good codecs. Our construction relies on a separation result for the simplified scenario where information from one sensor is to be sent to the CP that already has information regarding the desired signal. We show that the optimal encoder decoder combination, in this setting, can be decomposed, without loss in performance, into two steps: A first preprocessing step to extract relevant information from the indirect observation with consideration of the side information, followed by a second step of side-informed encoding of the preprocessed output. A recursive exploit of the decomposition coupled with side-informed transform coding allows us to construct encoders by reusing scalar Wyner-Ziv codecs. We develop a numerical procedure for obtaining bounds delineating the best achievable performance for the proposed sequential framework and construct and demonstrate a practical codec in the proposed framework that achieves empirical performance close to the bound. Furthermore, we also compare the bounds for the sequential scheme against bounds for a number of alternate schemes-for some of which codec constructions are obvious and others for which codec constructions are inobvious. Our results show that, in most cases, our codec exceeds the performance bound of schemes offering obvious constructions. The achievable bound for our sequential framework is also shown to be close to a general nonconstructive distributed bound that does not impose the sequential constraint indicating that the sequential approach may not cause a significant performance compromise.

/pdf/distributed-estimation-and-coding-a-sequential-framework-3kw7xli9hq.pdf

Chao Yu

Papers

Camera Scheduling and Energy Allocation for Lifetime Maximization in User-Centric Visual Sensor Networks

Lifetime-Distortion Trade-off in Image Sensor Networks

Watermark embedding in binary images for authentication

Plane-based calibration of cameras with zoom variation

Distributed Estimation and Coding: A Sequential Framework Based on a Side-Informed Decomposition