An Epidemic of Kala Azar in Kenya

Abstract Not Provided

Using Raster Sketches for Digital Image Retrieval

This research addresses the problem of content-based image retrieval using queries on image-object shape, completely in the raster domain. It focuses on the particularities of image databases encountered in typical topographic applications and presents the development of an environment for visual information management that enables such queries. The query consists of a user-provided raster sketch of the shape of an imaged object. The objective of the search is to retrieve images that contain an object sufficiently similar to the one specified in the query. The new contribution of this work combines the design of a comprehensive digital image database on-line query access strategy through the development of a feature library, image library and metadata library and the necessary matching tools. The matching algorithm is inspired by least-squares matching (lsm), and represents an extension of lsm to function with a variety of raster representations. The image retrieval strategy makes use of a hierarchical organization of linked feature (image-object) shapes within the feature library. The query results are ranked according to statistical scores and the user can subsequently narrow or broaden his/her search according to the previously obtained results and the purpose of the search

Scale and Orientation-invariant Scene Similarity Metrics for Image Queries

In this paper we extend our previous work on shape-based queries to support queries on configurations of image objects. Here we consider spatial reasoning, especially directional and metric object relationships. Existing models for spatial reasoning tend to rely on pre-identified cardinal directions and minimal scale variations, assumption that cannot be considered as given in our image applications, where orientations and scale may vary substantially, and are often unknown. Accordingly, we have developed the method of varying baselines to identify similarities in direction and distance relations. Our method allows us to evaluate directional similarities without a priori knowledge of cardinal directions, and to compare distance relations even when query scene and database content differ in scale by unknown amounts. We use our method to evaluate similarity between a user-defined query scene and object configurations. Here we present this new method, and discuss its role within a broader image retrieval framework

Using Raster Sketches for Digital Image Retrieval

This research addresses the problem of content-based image retrieval using queries on image-object shape, completely in the raster domain. It focuses on the particularities of image databases encountered in typical topographic applications and presents the development of an environment for visual information management that enables such queries. The query consists of a user-provided raster sketch of the shape of an imaged object. The objective of the search is to retrieve images that contain an object sufficiently similar to the one specified in the query. The new contribution of this work combines the design of a comprehensive digital image database on-line query access strategy through the development of a feature library, image library and metadata library and the necessary matching tools. The matching algorithm is inspired by least-squares matching (lsm), and represents an extension of lsm to function with a variety of raster representations. The image retrieval strategy makes use of a hierarchical organization of linked feature (image-object) shapes within the feature library. The query results are ranked according to statistical scores and the user can subsequently narrow or broaden his/her search according to the previously obtained results and the purpose of the search

Newspaper article, A Great Cereal, Fruit and Cattle Country, 1894

A newspaper article from the Caney Chronicle detailing the Oat and Cattle industry in Caney, Kansa

Low-Profile, Dual-Wavelength, Dual-Polarized Antenna

A single-aperture, low-profile antenna design has been developed that supports dual-polarization and simultaneous operation at two wavelengths. It realizes multiple beams in the elevation plane, and supports radiometric, radar, and conical scanning applications. This antenna consists of multiple azimuth sticks, with each stick being a multilayer, hybrid design. Each stick forms the h-plane pattern of the C and Ku-band vertically and horizontally polarized antenna beams. By combining several azimuth sticks together, the elevation beam is formed. With a separate transceiver for each stick, the transmit phase and amplitude of each stick can be controlled to synthesize a beam at a specific incidence angle and to realize a particular side-lobe pattern. By changing the transmit phase distribution through the transceivers, the transmit antenna beam can be steered to different incidence angles. By controlling the amplitude distribution, different side lobe patterns and efficiencies can be realized. The receive beams are formed using digital beam synthesis techniques, resulting in very little loss in the receive path, thus enabling a very-low loss receive antenna to support passive measurements

Active and Passive Hybrid Sensor

A hybrid ocean wind sensor (HOWS) can map ocean vector wind in low to hurricane-level winds, and non-precipitating and precipitating conditions. It can acquire active and passive measurements through a single aperture at two wavelengths, two polarizations, and multiple incidence angles. Its low profile, compact geometry, and low power consumption permits installation on air craft platforms, including high-altitude unmanned aerial vehicles (UAVs)

Poly-GAN: Regularizing Polygons with Generative Adversarial Networks

Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features predicted by Machine Learning (ML) algorithms in the GIScience domain, where information overload remains a significant problem in many cartographic/LBS applications. It addresses the limitations of traditional cartographic regularization/generalization algorithms, which can struggle with producing both accurate and minimal representations of multisided built environment objects. Furthermore, future work proposes a way to test the method on even more complex object shapes to address this limitation

SAMATS: Texture Extraction Explained

The creation of detailed 3D buildings models, and to a greater extent the creation of entire city models, has become an area of considerable research over the last couple of decades. The accurate modeling of buildings has LBS (Location Based Services) applications in entertainment, planning, tourism and e-commerce to name just a few. Many modeling systems deployed to date require manual correspondences to be made across the image set in order to determine the models 3D structure. This paper describes SAMATS, a Semi-Automated Modelling and Texturing System, which has the capability of producing geometrically accurate and photorealistic building models without the need for manual correspondences from a set of geo-referenced terrestrial images. This paper is the third in a trilogy of publications describing the entire SAMATS system, and describes the third of three components that comprise the full functionality of the complete SAMATS implementation. It focuses on the texture extraction step in detail, while providing an overview only of SAMATS’ other two components

OSM-GAN: Using Generative Adversarial Networks for Detecting Change in High-Resolution Spatial Images

Conference paper abstract