In this paper we propose an algorithm for predicting a person's perceptual attention focus (PAtF) through the use of a
Kalman Filter design of the human visual system. The concept of the PAtF allows significant reduction of the bandwidth
of a video stream and computational burden reduction in the case of 3D media creation and transmission. This is
possible due to the fact that the human visual system has limited perception capabilities and only 2 degrees out of the
total of 180 provide the highest quality of perception. The peripheral image quality can be decreased without a viewer
noticing image quality reduction. Multimedia transmission through a network introduces a delay. This delay reduces the
benefits of using a PAtF due to the fact that the person's attention area can change drastically during the delay period,
thus increasing the probability of peripheral image quality reduction being detected. We have created a framework which
uses a Kalman Filter to predict future PAtFs in order to compensate for the delay/lag and to reduce the
bandwidth/creation burden of any visual multimedia.
The possibility of perceptual compression using live eye tracking has been anticipated for some time by many researchers. Among the challenges of real-time eye-gaze-based perceptual video compression are how to handle the fast nature of eye movements with the relative complexity of video transcoding and also take into account the delay associated with transmission in the network. Such a delay requires additional consideration in perceptual encoding because it increases the size of the area that requires high-quality coding. We present a hybrid scheme, one of the first to our knowledge, that combines eye tracking with fast in-line scene analysis to drastically narrow the high acuity area without the loss of eye-gaze containment.
The possibility of perceptual compression using live eye-tracking has been anticipated for some time by many researchers. Among the challenges of real-time eye-gaze based perceptual video compression is how to handle the fast nature of eye movements with a relative complexity of video transcoding and also take into the account a delay
associated with transmission in the network. Such delay requires an additional consideration in perceptual encoding because it increases the size of the area that requires high quality coding. In this paper we present a hybrid scheme, one of the first to our knowledge, which combines eye-tracking with fast in-line scene analysis to drastically narrow down the high acuity area without the loss of eye-gaze containment.
A novel uni-complex valued trinary associative model, which is implementable in the optical domain, is proposed. Retrieval of the stored pattern is accomplished using an threshold formula in the inner product domain. An algorithm to determine adaptive threshold formula for this trinary associative memory model is presented. The optimal threshold is chosen to yield the best performance. Different threshold parameters have been investigated to obtain the range of optimal threshold parameters. In order to validate our performance model, character recognition problem with noisy and noise-free data are investigated. Moreover, a bi-complex representation model for associative memory retrieval is presented and compared to previous methods.
This paper presents the result of our research on a new associative memory, which unlike any existing neural network based artificial associative memories, can dynamically localize (or focus) its search on any subset of the pattern space. This new ability now makes the power of associative computing available to a new class of pattern matching applications. Application areas which will particularly benefit from this model include (1) detection of small irregular patterns (medical diagnostics), (2) detection of tiny targets, (3) background varying target recognition, (4) visual example based content-based image retrieval, (5) robust adaptive control systems which needs to continue operating with small number of surviving sensors, in the face of post learning loss of sensors.
KEYWORDS: Picture Archiving and Communication System, Databases, System integration, Radiology, Information fusion, Diagnostics, Computing systems, Medicine, Imaging systems, Control systems
There exists tremendous opportunity in hospital-wide resource optimization based on system integration. This paper defines the resource planning and scheduling requirements integral to PACS, RIS and HIS integration. An multi-site case study is conducted to define the requirements. A well-tested planning and scheduling methodology, called Constrained Resource Planning model, has been applied to the chosen problem of radiological service optimization. This investigation focuses on resource optimization issues for minimizing the turnaround time to increase clinical efficiency and customer satisfaction, particularly in cases where the scheduling of multiple exams are required for a patient. How best to combine the information system efficiency and human intelligence in improving radiological services is described. Finally, an architecture for interfacing a computer-aided resource planning and scheduling tool with the existing PACS, HIS and RIS implementation is presented.
KEYWORDS: Content addressable memory, Databases, Holography, Chemical elements, Computer programming, Signal to noise ratio, Data modeling, Cognitive modeling, Visualization, Distortion
This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.
The paper presents mathematical and empirical results of the behavior of a new multidimensional neural computing paradigm called multidimensional holographic associative computing (MHAC). MHAC can be potentially used for high density associative storage and retrieval of image information. Unlike conventional neural computing, each morsel of information in MHAC is presented as a complex vector in a multidimensional unit spherical space. Each of the individual phases of the vector enumerates a value of the information. The magnitude of the vector represents the associated confidence in the information. In contrast, the conventional neural computing operates only on the notion of confidence. The proposed multidimensional generalization demonstrates significant improvement in associative storage capacity without the loss of generalization space. Virtually, unlimited pattern associations can be enfolded over a single holographic memory substrate by higher order encoding. In addition, its well-structured computation, simultaneous multi-channel learning, and single step non- iterative retrieval promise highly scalable parallelism. The paper presents the theory of operation of MHAC that is founded on the generalized holographic principles and multidimensional Hebbian learning. The paper also presents analytical as well as empirical evidence from computer simulation supporting the superior performance of MHAC cells.
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