Abstract: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: The normalized difference vegetation index (NDVI)
and normalized difference moisture index (NDMI) derived from the
moderate resolution imaging spectroradiometer (MODIS) have been
widely used to identify spatial information of drought condition. The
relationship between NDVI and NDMI has been analyzed using
Pearson correlation analysis and showed strong positive relationship.
The drought indices have detected drought conditions and identified
spatial extents of drought. A comparison between normal year and
drought year demonstrates that the amplitude analysis considered both
vegetation and moisture condition is an effective method to identify
drought condition. We proposed the amplitude analysis is useful for
quick spatial assessment of drought information at a regional scale.
Abstract: An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.
Abstract: In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.
Abstract: In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.
Abstract: This paper discusses applications of a revolutionary
information technology, Geographic Information Systems (GIS), in
the field of the history of cartography by examples, including
assessing accuracy of early maps, establishing a database of places
and historical administrative units in history, integrating early maps
in GIS or digital images, and analyzing social, political, and
economic information related to production of early maps. GIS
provides a new mean to evaluate the accuracy of early maps. Four
basic steps using GIS for this type of study are discussed. In addition,
several historical geographical information systems are introduced.
These include China Historical Geographic Information Systems
(CHGIS), the United States National Historical Geographic
Information System (NHGIS), and the Great Britain Historical
Geographical Information System. GIS also provides digital means to
display and analyze the spatial information on the early maps or to
layer them with modern spatial data. How GIS relational data
structure may be used to analyze social, political, and economic
information related to production of early maps is also discussed in
this paper. Through discussion on these examples, this paper reveals
value of GIS applications in this field.
Abstract: Array signal processing involves signal enumeration and source localization. Array signal processing is centered on the ability to fuse temporal and spatial information captured via sampling signals emitted from a number of sources at the sensors of an array in order to carry out a specific estimation task: source characteristics (mainly localization of the sources) and/or array characteristics (mainly array geometry) estimation. Array signal processing is a part of signal processing that uses sensors organized in patterns or arrays, to detect signals and to determine information about them. Beamforming is a general signal processing technique used to control the directionality of the reception or transmission of a signal. Using Beamforming we can direct the majority of signal energy we receive from a group of array. Multiple signal classification (MUSIC) is a highly popular eigenstructure-based estimation method of direction of arrival (DOA) with high resolution. This Paper enumerates the effect of missing sensors in DOA estimation. The accuracy of the MUSIC-based DOA estimation is degraded significantly both by the effects of the missing sensors among the receiving array elements and the unequal channel gain and phase errors of the receiver.
Abstract: Crime is a major societal problem for most of the
world's nations. Consequently, the police need to develop new
methods to improve their efficiency in dealing with these ever increasing crime rates. Two of the common difficulties that the police
face in crime control are crime investigation and the provision of crime information to the general public to help them protect themselves. Crime control in police operations involves the use of
spatial data, crime data and the related crime data from different organizations (depending on the nature of the analysis to be made).
These types of data are collected from several heterogeneous sources
in different formats and from different platforms, resulting in a lack of standardization. Moreover, there is no standard framework for
crime data collection, integration and dissemination through mobile
devices. An investigation into the current situation in crime control was carried out to identify the needs to resolve these issues. This
paper proposes and investigates the use of service oriented
architecture (SOA) and the mobile spatial information service in crime control. SOA plays an important role in crime control as an
appropriate way to support data exchange and model sharing from
heterogeneous sources. Crime control also needs to facilitate mobile
spatial information services in order to exchange, receive, share and release information based on location to mobile users anytime and
anywhere.
Abstract: An attractor neural network on the small-world topology
is studied. A learning pattern is presented to the network, then
a stimulus carrying local information is applied to the neurons and
the retrieval of block-like structure is investigated. A synaptic noise
decreases the memory capability. The change of stability from local
to global attractors is shown to depend on the long-range character
of the network connectivity.