3040951
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors
In this paper we introduce the simultaneous tracking and activity recognition (STAR) problem, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring. Automatic health monitoring can potentially help the elderly population live safely and independently in their own homes by providing key information to caregivers. Our goal is to perform accurate tracking and activity recognition for multiple people in a home environment. We use a “bottom-up” approach that primarily uses information gathered by many minimally invasive sensors commonly found in home security systems. We describe a Rao-Blackwellised particle filter for room-level tracking, rudimentary activity recognition (i.e., whether or not an occupant is moving), and data association. We evaluate our approach with experiments in a simulated environment and in a real instrumented home.
18150918
A context-aware recognition survey for data collection using ubiquitous sensors in the home
Identifying what people do in the home can both inform ubiquitous computing application design decisions and provide training data to the machine learning algorithms used in their implementation. This paper describes an unsupervised technique in which contextual information gathered by ubiquitous sensors is used to help users label a multitude of anonymous activity episodes. This context-aware recognition survey is a game-like computer program in which users attempt to correctly guess which activity is happening after seeing a series of symbolic images that represent sensor values generated during the activity. We report a user study of the system, focusing on how well subjects were able to recognize their own activities, the activities of others, and counterfeits that did not correspond to any activity.
5540765
Digital Image Similarity for Geo-spatial Knowledge Management
The amount and availability of high-quality geo-spatial image data, such as digital satellite and aerial photographs, is increasing dramatically. Task-based management of such visual information and associated knowledge is a central concern for organisations that rely on digital imagery. We are developing geo-spatial knowledge management techniques that employ case-based reasoning as the core methodology. In order to provide effective retrieval of task-based experiences that center around geo-spatial imagery, we need to forward novel similarity metrics for directly comparing the image components of experience cases. Based on work in geo-spatial image database retrieval, we are building an effective similarity metric for geo-spatial imagery that makes comparisons based on derived image features, their shapes, and the spatial relations between them. This paper gives an overview of the geo-spatial knowledge management context, describes our image similarity metric, and provides an initial evaluation of the work.
26290427
Knowledge Capture and Reuse for Geo-spatial Imagery Tasks
The continuously increasing amount and availability of geospatial image data is giving rise to problems of information overload in organisations that rely on digital geo-spatial imagery. Intelligent support for relevant image retrieval is needed in order to help manage large geospatial image libraries. Moreover, managing the knowledge implicit in using geo-spatial imagery to address particular tasks is crucial for capturing and making the most effective use of organisational knowledge assets. We are developing case-based knowledge-management support for large geo-spatial image repositories, which incorporates sketch-based querying for image retrieval; image manipulation and annotation tools for highlighting and composing relevant aspects of task-relevant imagery; and automatic context-based querying for retrieving relevant previous task experiences. This paper describes our approach to knowledge capture and reuse through task-based image annotation, and it introduces the environment we are developing for capture and reuse of task knowledge involving geo-spatial imagery.
6770135
Empirical comparison of analog and digital auditory preprocessing for automatic speech recognition
Results from digital and analog filter bank preprocessors are compared in order to establish the validity of analog processing for automatic speech recognition (ASR) systems. Three systems are evaluated using speaker and context independent phoneme recognition tasks. The three ASR systems are identical except for the preprocessing techniques used to derive three signal representations: extraction of (1) the digital mel-frequency spectrum, (2) the mel-frequency spectrum from commercial discrete bandpass filters and (3) the exponential spectrum from analog VLSI bandpass filter bank. The discrete analog system exhibits a 38% increase in recognition accuracy over the digital preprocessing technique. The digital and analog VLSI-based techniques perform comparably (within 3% of each other).
26861465
Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization
We are developing methods to characterize atherosclerotic disease in human carotid arteries using multiple MR images having different contrast mechanisms (T1W, T2W, PDW). To enable the use of voxel gray values for interpretation of disease, we created a new method, local entropy minimization with a bicubic spline model (LEMS), to correct the severe (/spl ap/80%) intensity inhomogeneity that arises from the surface coil array. This entropy-based method does not require classification and robustly addresses some problems that are more severe than those found in brain imaging, including noise, steep bias field, sensitivity of artery wall voxels to edge artifacts, and signal voids near the artery wall. Validation studies were performed on a synthetic digital phantom with realistic intensity inhomogeneity, a physical phantom roughly mimicking the neck, and patient carotid artery images. We compared LEMS to a modified fuzzy c-means segmentation based method (mAFCM), and a linear filtering method (LINF). Following LEMS correction, skeletal muscles in patient images were relatively isointense across the field of view. In the physical phantom, LEMS reduced the variation in the image to 1.9 % and across the vessel wall region to 2.5 %, a value which should be sufficient to distinguish plaque tissue types, based on literature measurements. In conclusion, we believe that the correction method shows promise for aiding human and computerized tissue classification from MR signal intensities.
16397302
Analog VLSI circuits for manufacturing inspection
We present three types of analog VLSI circuits that can be used in manufacturing inspection systems. The first set of circuits performs an adaptive threshold of an input image. The second circuit uses morphological operations with programmable structuring elements to detect oriented edges. Both of these circuits can be used as high speed preprocessors for visual inspection of manufacturing processes. The third circuit performs a computation necessary for selective attention in visual processing. This circuit is a component of a larger system that will facilitate a serial/parallel processing scheme in order to increase the speed of processing in machine vision tasks. Ail circuits presented use focal-plane processing to achieve their massively parallel architectures. For each design, the processing pixels contain vertical bipolar phototransistors to accommodate parallel optical inputs. All circuits have been fabricated using a standard 2.0 /spl mu/m digital CMOS process. Data for each of these circuits is presented.