The H.264 protocol for high resolution video offers several enhancements which can be leveraged for the selective
tracking and focused resolution of disjoint macro-blocks of the frame sequence such that a smooth degradation of
context is achieved at significant compression rates. We demonstrate the near real time temporal and spatial foveation of
the video stream. Tracking results produced by spatial statistics of the georegistered motion vectors of the H.264 frames
are useful for change detection and background discrimination as well as temporal foveation. Finally, we discuss the
online analytical processing of the spatial database of full motion video through use of the automatically generated
geospatial statistical descriptor metadata.
Modern day remote video cameras enjoy the ability of producing quality video streams at extremely high resolutions.
Unfortunately, the benefit of such technology cannot be realized when the channel between the sensor and the operator
restricts the bit-rate of incoming data. In order to cram more information into the available bandwidth, video
technologies typically employ compression schemes (e.g. H.264/MPEG 4 standard) which exploit spatial and temporal
redundancies. We present an alternative method utilizing region of interest (ROI) based compression. Each region in the
incoming scene is assigned a score measuring importance to the operator. Scores may be determined based on the
manual selection of one or more objects which are then automatically tracked by the system; or alternatively, listeners
may be pre-assigned to various areas that trigger high scores upon the occurrence of customizable events. A multi-resolution
wavelet expansion is then used to optimally transmit important regions at higher resolutions and frame rates
than less interesting peripheral background objects subject to bandwidth constraints. We show that our methodology
makes it possible to obtain high compression ratios while ensuring no loss in overall situational awareness. If combined
with modules from traditional video codecs, compression ratios of 100:1 to 1000:1, depending on ROI size, can easily be
achieved.
KEYWORDS: Cameras, Video, 3D image processing, Video processing, Digital photography, Imaging arrays, Ranging, Video surveillance, Optical flow, 3D modeling
Recent advances in digital photography have enabled the development and demonstration of plenoptic cameras with
impressive capabilities. They function by recording sub-aperture images that can be combined to re-focus images
or to generate stereoscopic pairs.
Plenoptic methods are being explored for fusing images from distributed arrays of cameras, with a view toward
applications in which hardware resources are limited (e.g. size, weight, power constraints). Through computer
simulation and experimental studies, the influences of non-idealities such as camera position uncertainty are being
considered. Component image rescaling and balancing methods are being explored to compensate. Of interest is
the impact on precision passive ranging and super-resolution. In a preliminary experiment, a set of images from a
camera array was recorded and merged to form a 3D representation of a scene. Conventional plenoptic refocusing
was demonstrated and techniques were explored for balancing the images. Nonlinear methods were explored for
combining the images limited the ghosting caused by sub-sampling.
Plenoptic processing was explored as a means for determining 3D information from airborne video. Successive
frames were processed as camera array elements to extract the heights of structures. Practical means were
considered for rendering the 3D information in color.
KEYWORDS: Data fusion, Sensors, Target recognition, Data modeling, Data mining, Target detection, Detection and tracking algorithms, Statistical analysis, Data processing, System identification
A method for calculating unbiased entropic estimates of multivariate associations between mixed data is given. Since
there is no assumption of unimodality of the distributions of the categorical and continuous-valued data, measures of
central dispersion are not appropriate for the quantification of association. Empirical estimates of entropic
associations are provided with respect to the partition entropy of a uniform binning interval and the cardinality of the
sensed data. The increased computational demand incurred by the appropriate generalized measure is mitigated by a
branch and bound algorithm for information-optimal attribute selection. The methodology is applied against a known
data set used in a standard data mining competition that features both sparse categorical and continuous valued
descriptors of a target with promising results.
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