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Previously we described the development of an attenuated Pasteurella multocida mutant that expresses only the N-terminal truncated fragment of P. multocida toxin (N-PMT) and its protective effects in a mouse model. This paper details our evaluation of the vaccine potential of this mutant strain in pigs. Pigs vaccinated with the mutant showed significantly higher rates of antibody induction and lower nasal conchal (turbinate) scores for atrophic rhinitis than controls, which suggests that this mutant strain may be a good candidate for a live attenuated vaccine.

Mycoplasma synoviae, a major worldwide pathogen in poultry, causes respiratory tract infection and arthritis in chickens and turkeys. Two major surface antigens of M. synoviae are encoded by a single gene, vlhA (variably expressed lipoprotein and hemagglutinin). The gene product is cleaved post-translationally to yield the lipoprotein major surface protein (MSP) B (MSPB) and the hemagglutinin MSPA. The availability of MSPA as an antigen for serodiagnosis was studied by means of a protein chip based on surface plasmon resonance imaging (SPRi). The diagnostic potential of SPRi for measurement of levels of antibody to MSPA was compared with that of a conventional enzyme-linked immunosorbent assay (ELISA) kit. The results from SPRi, a process that took only 1 h, were similar to those from ELISA. Therefore, MSPA can be used as an antigen for serologic studies, and SPRi, a label-free and high-throughput method, may be a valuable tool in avian serodiagnostic studies.

Pasteurella multocida serogroup D causes progressive atrophic rhinitis in pigs and produces a potent, intracellular, mitogenic toxin known as P. multocida toxin (PMT), which is encoded by the toxA gene. Highly toxic to cells, PMT is a poor antigen and becomes more immunogenic after its native structure has been destroyed. Previously, we found that the N-terminal fragment of PMT (N-PMT) can induce a strong immune response that is protective against wild-type challenge. Here, an attenuated P. multocida mutant expressing only N-PMT was developed and its protective effect was evaluated. The mutant provides protective immune responses against bacterial and toxin challenges, and so is a good live vaccine candidate.

Extraction of man-made structures from satellite images is one of the essential issues in remote sensing and many techniques were proposed for building extraction from high resolution satellite images. However, most of them rely stereo analysis or additional data sources such as LIDAR for retrieval of 3D building information. This paper proposes a semiautomatic approach that extracts 3D building information from single satellite images. In this approach, after measuring boundaries of a building roof we determine the building height by projecting building shadow onto the image space until the projected shadow matches the actual shadow. Then we extract building footprints by translating roof boundaries along the direction of vertical edges to the amount determined by the building height. To increase the level of automation, we devised a technique that adjusts the height so that the projected shadow matches the actual shadow and determine building height automatically. We also devised a technique to extract roof boundaries by clicking only two corner points. A panchromatic IKONOS image and Quickbird image over Deajeon area were used to test proposed algorithms and a IKONO stereo pair was used to get reference building heights. While building heights determined manually showed the accuracy of an RMS error of 1.66 m, the heights determined automatically showed an RMS error of 1.86 m. The results show that 3D building information can be extracted from single satellite images and that the automation approaches proposed in this paper worked.

The main objective of this paper is to investigate accuracy of various sensor models developed for linear pushbroom-type satellite images. In particular this paper investigates the accuracy of sensor models in two aspects; the accuracy of bundle adjustments and the accuracy of estimating exterior orientation parameters. Analysis of the first accuracy has been reported by many previous studies whereas the analysis of the second accuracy has somewhat been neglected. We argue that the second accuracy is as important as the first one. It describes a model's ability to predict satellite orbit and attitude, which has many direct and indirect applications. We implemented and tested two types of physical sensor models based on the collinearity equations: one with position and euler angles as model parameters and the other with orbit and attitude angles as model parameters. Tests were carried out with two Kompsat-1 EOC scenes and GPS-driven control points. Test results showed that the model with orbit and attitude parameters produced better estimation of exterior orientation parameters while maintained comparable accuracy of bundle adjustments. © 2005 IEEE.

Precision correction is the process of geometrically aligning images to a reference coordinate system using GCPs (Ground Control Points). Many application of remote sensing data, such as change detection, mapping, and environmental monitoring, rely on the accuracy of precision correction. However it is very time consuming, laborious and expensive process. In this research, we propose a new method for automatic precision correction of satellite images using the GCP chips collected from lower resolution satellite images to reduce or eliminate the number of GCPs required. Different Earth observation satellites provide the satellite images of different resolution, different swath and different position accuracy. In general, the satellite images of lower resolution have wider swath and vice versa. In that sense, if we can utilize the GCP chips collected from lower resolution satellite images to do precision correction of the higher resolution satellite images then the number of GCPs required to have a precisely corrected images of the same area would be reduced. In this experiment, we used GCP chips collected from Landsat-7 panchromatic images of 15 m resolutions to perform precision correction of the KOMPSAT-1 EOC images of 6.7 m resolutions. In this case, since Landsat-7 images provide higher positional accuracy of less than 250 m at the systematically corrected level (L1G) comparing about 2 km positional accuracy of KOMPSAT-1, GCPs collected from level 1G of Landsat-7 images can provide enough accuracy in certain applications. To utilize this approach easily, this study would be applied to the automatic precision correction method developed previously, which exploits the normalized cross correlation and the RANSAC (Random Sample Consensus) algorithm to removed the outliers of matching results. The initial result showed the possibility of using GCP chips of lower resolution to carry out precision correction in the frame of the automatic precision correction method utilizing RANSAC algorithm.

Describes an approach to reduce the execution time of an area-based stereo matching technique by defining a patch (or window) carefully. It is shown that the conventional way of defining a patch contains redundancy and that a patch defined otherwise improves the performance in terms of coverage and speed while accuracy degradation is nominal. This paper also describes an attempt of defining optimal patch for stereo matching.

KOMPSAT-2 (Korea Multi-Purpose Satellite) is a new high resolution Earth observation satellite to be launched in 2004 by the Korean Government. This satellite is capable of acquiring 1m panchromatic and 4m multispectral images and will transmit the image to ground at 320 Mbps. This paper will address the work being carried out for the development of image reception, processing and distribution system for the KOMPSAT-2 image data. The development work has been focused on automation, speed, reliability, integrity, cost, expandability and security of the system. The system was divided into three sub-systems and three sub-systems into 20 software modules. An emphasis was put to the integration of value-added processing modules into the whole system. With this, we intended to fully automate the processing chain from image reception to the generation of value-added information. The development work is still undergoing and this paper will report the design of the system, progress and lessons learned.

A semi-automatic road extraction method from high-resolution (1-m) satellite images is presented. As IKONOS, a high-resolution (1-m) satellite has been launched and several companies have plans to launch high-resolution satellites, extraction of man-made objects from high-resolution satellite images has been main concern of many scientists. The method consists of three phases; 1) NUBS (Non Uniform B-Spline) curve is formed by given seed points. 2) A road candidate area is made by straightening image along the NUBS curve. 3) Finally, road is extracted by a tracking algorithm which uses adaptive least squares correlation match method and linearity. Due to straightening image, the tracking algorithm extracts roads accurately even though there are road gaps, and the size of a matrix for least squares correlation match can be reduced. We test our method on high-resolution (1-m) satellite (IKONOS) image. The test result reveals our method is robust and can be one of the feasible solutions of mapping from high-resolution (1-m) satellite images.

In previous work, we and others have developed visibility-based tool path generation schemes. Almost all previous research implicitly assumes that all visible parts are machinable. Though usually true practice, this assumption hides several subtleties inherent to the geometry of the machining process. Here, we define machinability in a stricter sense, as a generalization of the robotic path planning problem. Then, we define various “tight” necessary conditions for strict machinability, and show the connections between these conditions. After demonstrating the richness of the information contained in visibility, we show how to compute visibility effectively. Visible directions constitute an approximate feasible configuration space of a cutting tool. We also address questions pertaining to the topological connectivity of the feasible space. The theoretical results of this paper lay down a firmer foundation of machining path planning.

Over the last few years, there has been increasing interest, principally from the telecommunications and airline industries in the supply of worldwide high resolution databases of urban areas. High resolution (l–5m) spaceborne data is beginning to address these requirements. Critical to the successful exploitation of these spaceborne data is the development of algorithms for automated or the semi-automated extraction of building objects using cartographic data and the automated extraction of DEMs.

An automated building detection system has been developed previously based on a line- relation-graph. This paper describes improvements made on this building detection system originally developed for aerial imagery. Major improvement has been achieved by introducing a `super' building hypothesis, which refers to a building hypothesis formed from a `U'-shaped chain of lines. This paper also reports experiments on automated building detection from 2 m resolution spaceborne imagery (DD5) using the improved system. Although small buildings could not be extracted (as they were not visible), the improved system showed strong feasibilities of (fully) automated extraction of large buildings from DD5 imagery.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The built environment constitutes a very important target for automated image understanding systems. Currently, these environments only have manually surveyed 2D vector information in paper or digital map form. Increasingly, there are many requirements for 3D information. Automated systems are the only viable way to acquire this information over vast areas with a temporal frequency which will keep pace with the rate of change of many of these areas. Most published image understanding techniques for the automatic extraction of man-made objects only address a specific class of scene at a specific resolution. To address the full range of circumstances which will be required and make a technique more robust, it should be applied to multi-resolution images. In this paper, two automated systems one for building height extraction (stereoscopic) and one for building detection from monoscopic images are applied to multi-resolution aerial and spaceborne imagery. These systems were originally developed with for 0.15 m resolution inner city urban area imagery. With 0.24 m resolution suburban imagery, they performed very successfully. With 0.85 m resolution urban imagery containing very complicated buildings, they show promising results. A 2 m resolution Russian DD5 image was also tested with the monoscopic building detection system and the results showed automatic extraction of large industrial buildings is possible with such imagery. In summary, it was shown that these fully automated systems can handle images with various resolutions and environments.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

For automatic building extraction, the need of the fusion of several image understanding cues has been emphasised. In this paper, the fusion of a stereoscopic (pyramidal matching) and a monoscopic technique (building detection) was investigated. In the fusion technique proposed, building detection output is used as boundaries of height interpolation and pyramidal matching output as the source of height information. After height interpolation, buildings are reconstructed in 3D space. This fusion technique was tested with several examples and proved to be very powerful. The results of this paper support the role of fusion in more complete image understanding.

The main objective of this research is automatic generation of more accurate DEM/ortho-image and, by extension, to update existing DEMs and ortho-images database automatically by using high resolution satellite images. In the first part, we tested the possibility of automatic GCP and tie-point generation for DEMs and ortho-images generation. We extract Ground Control Points (GCPs) automatically from high resolution satellite image by using existing ortho-images and existing DEMs. In second part, we tested whether high resolution images can be used to update existing DEMs and ortho-images. For this, we performed automatic generation of DEMs/ortho-images by using true GCPs obtained by GPS surveying. For testing the method proposed, we used IKONOS, SPOT 5, QuickBird, Kompsat-2 high resolution satellite images. Assessment for results supports that automatic generation of DEMs/ortho-images was performed successfully. We generated a DEM from IKONOS images fully automatically by using GCPs generated from existing DEMs and ortho-images. Newly updated DEM was compared with existing data. Updated DEM shows more detailed surface, and the result of validation shows that the accuracy is similar with that of existing DEMs/ortho-images.