Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l(1) minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.

Human activity recognition using wearable body sensors is playing a significant role in ubiquitous and mobile computing. One of the issues related to this wearable technology is that the captured activity signals are highly dependent on the location where the sensors are worn on the human body. Existing research work either extracts location information from certain activity signals or takes advantage of the sensor location information as a priori to achieve better activity recognition performance. In this paper, we present a compressed sensing-based approach to co-recognize human activity and sensor location in a single framework. To validate the effectiveness of our approach, we did a pilot study for the task of recognizing 14 human activities and 7 on body-locations. On average, our approach achieves an 87:72% classification accuracy (the mean of precision and recall).

Human activity recognition and human behavior understanding play a central role in the field of ubiquitous computing. In this paper, we propose a novel method using magnetometer embedded in the mobile phone to recognize activities by detecting household appliance usage. The key idea of our approach is that when the mobile phone user performs a certain activity at home, the embedded magnetometer is capable of capturing the changes of the magnetic field strength around the mobile phone caused by the household appliance in operation. Our mobile application uses these changes as magnetic signatures for each of these appliance such that the daily household acitivities associated with these appliance such as cooking can be recognized.

Many ubiquitous computing applications involve human activity recognition based on wearable sensors. Although this problem has been studied for a decade, there are a limited number of publicly available datasets to use as standard benchmarks to compare the performance of activity models and recognition algorithms. In this paper, we describe the freely available USC human activity dataset (USC-HAD), consisting of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for healthcare scenarios. We briefly review some existing publicly available datasets and compare them with USC-HAD. We describe the wearable sensors used and details of dataset construction. We use high-precision well-calibrated sensing hardware such that the collected data is accurate, reliable, and easy to interpret. The goal is to make the dataset and research based on it repeatable and extendible by others.

We describe panoramic stereo immersive image capture and display systems, and techniques for adjusting the perceived stereo effect of 2D regions in the images. We briefly review the image capture model, describe the geometrical parameters and the relevance of horizontal disparity, and describe the stereo panorama synthesis process. The 2D regions are selected by a mean-shift feature space segmentation algorithm and/or user interaction. Finally, we describe a region-based horizontal disparity adjustment method that enhances or reduces the stereo visual effect for selected segmented regions

Multi-view autostereoscopic (AS) displays are a recently developed technology that allows several viewers to see stereo 3D images without the use of glasses or goggles. Our system enables users to interact with real or virtual 3D displayed objects by means of a hand-held cursor or (in the future) through hand gestures. In this paper we mathematically derive two multicamera image acquisition models for multi-view AS displays. These models describe the relationship of object coordinates to camera, screen and image coordinates. We present some examples of virtual objects rendered using the models.

Large complex networks have become an inseparable part of modern society. However, very little has been done to develop tools to manage and ensure the security of such networks. Network operators continue to slave over endless daily logs and alerts in a struggle to keep networks operational. Perhaps the most formidable enemy of network operations today is the volume of management data that must be perused. Expensive commercial products attempt to visualize data but with limited utility, as witnessed by the prevailing use of command-line interfaces and homegrown scripts. In addition to data collection tools, operators need to immediately observe and debug the effects of their actions; yet that information is buried deep in the data that pours daily from monitoring equipment. Thus, they need better ways to abstract network events and better, more informative ways to render them.

Summary form only given. The author discusses developments in optical signal processing and highlights the many ways in which the technology of optical and electronic signal processing is converging.

A parallel digital optical cellular image processor (DOCIP) functionally comprises an array of identical I-bit processing elements or cells, a fixed interconnection network, and a control unit. Four interconnection network topologies are described, and include two variants of a mesh-connected array and two variants of a cellular hypercube network. The instruction sets of these single-instruction multiple-data (SIMD) machines are based on a mathematical morphological theory, binary image algebra (BIA), which provide an inherently parallel programming structure for their control. Physically, a DOCIP architecture uses a holographic optical element in a 3D free-space optical system to implement off-chip interconnections, and an optoelectronic spatial light modulator to implement a 2D array of nonlinear processing elements and (optionally) local on-chip interconnections. Two examples are given. The first, an experimental implementation of a single 54-gate cell of the DOCIP, uses an optically recorded hologram for within-cell optical interconnections, and a spatial light modulator for a 2D array of optically accessible gates. The second, a design for an efficient and more manufacturable architecture, uses a computer-generated diffractive optical element for cell-to-cell interconnections, and a 20 smart-pixel array of DOCIP cells, each cell having electronic logic and optical input/output.>

A motion compensated image sequence enhancement algorithm is presented. A combined segmentation and motion estimation algorithm is employed. A temporal or a spatiotemporal low-pass filter is then applied. Mean and median filters are presented as low-pass filters. The temporal filtering is performed over the motion path of each pixel, which is provided by the motion-estimation algorithm. The spatial filtering does not blur the boundaries of the moving objects because the boundary locations are provided by the segmentation algorithm. The performance of the combined algorithm is examined using computer-generated and real image sequences corrupted by additive white Gaussian noise. The algorithm performs very well in a very noisy environment. Mean filtering is more effective in the case of white Gaussian noise, and median filtering is more effective in the case of salt-and-pepper noise and burst noise.>

The problem of motion estimation is reviewed, and a region matching motion estimation algorithm is presented. This algorithm estimates accurately the motion parameters in the case of linear 2-D motion and gives a very good linear approximation in the case of a nonlinear 2-D motion. It assumes that the images have been previously segmented. Its main advantage in comparison to other motion estimation algorithms is its robustness in the presence of noise. It does not require small motion or smooth intensity profiles but only segmented images. Its computation time can be significantly reduced by an appropriate choice of the initial values of the motion parameters. The performance of the algorithm is examined for different kinds of motion and various signal-to-noise ratios using computer generated and real images.>

A description is given of a supervised textured image segmentation algorithm that provides improved segmentation results. An improved method for extracting textured energy features in the feature extraction stage is described. It is based on an adaptive noise smoothing concept that takes the nonstationary nature of the problem into account. Texture energy features are first estimated using a window of small size to reduce the possibility of mixing statistics along region borders. The estimated texture energy feature values are smoothed by a quadrant filtering method to reduce the variability of the estimates while retaining the region border accuracy. The estimated feature values of each pixel are used by a Bayes classifier to make an initial probabilistic labeling. The spatial constraints are enforced through the use of a probabilistic relaxation algorithm. Two probabilistic relaxation algorithms are investigated. Limiting the probability labels by probability threshold is proposed. The tradeoff between efficiency and degradation of performed is studied.>

Two of the most important problems in scene analysis of image sequences are the segmentation of image frames into moving and nonmoving components and the 2-D motion estimation of the moving parts of the scene. The algorithm has two parts: segmentation and 2-D motion estimation. These two parts are interactively connected in a mutually beneficial way. The authors concentrate on the 2-D motion estimation problem assuming that the segmentation has been performed. Two algorithms for 2-D motion estimation are presented. The first is based on the method of differentials, while the second is a boundary matching method.>

In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.

A nonstationary 2-D recursive image restoration filter that uses a nonstationary mean, nonstationary variance (NMNV) image model and minimizes the local mean square error is developed. The 2-D recursive filter adapts itself to the local image statistics and is able to do space-variant processing. The NMNV image model has a simple dynamic representation which simplifies the filter structure considerably. However, the optimal recursive filter still requires extensive computation. A suboptimal approach that uses a reduced update concept is proposed to reduce the computational efforts. With some modifications, this nonstationary 2-D recursive filter is extended to a class of uncorrelated, signal-dependent noise such as multiplicative noise and Poisson noise. The explicit filter structures and simulation results for images degraded by these signal-dependent noises are presented.

Speckle noise exists in all types of coherent imagery such as synthetic aperture radar, acoustic imagery and laser illuminated imagery. Speckle can be reduced by averaging over several uncorrelated speckle images of the same object when these are available. In this paper, we attempt to reduce speckle noise from a single speckle image by using adaptive digital image restoration techniques. Many speckle noise reduction algorithms assume speckle noise is multiplicative. We model the speckle according the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the statistical properties of speckle. A linear minimum mean-square error filter is derived based on our speckle model and a nonstationary image model. The filter responds adaptively to the signal-dependent speckle noise and the nonstationary mean and variance of the original image. The necessary parameters are estimated from the noisy image. The 2-D recursive implementation of this filter is developed as a fast computation algorithm.

An analysis of two-dimensional imaging systems with general intensity nonlinearities is presented and several methods for real-time correction are discussed. Exact correction for measurement purposes and approximate correction for the human observer are described, and system implementation is by digital hardware, primarily using programmable read-only memories (PROM's). Calibration techniques for these systems are given, and estimates of computing time and required system complexity are made. Experimental application of these techniques is discussed.