Building Detection Using LIDAR Data and Multispectral Images

A method the automatic detection of buildings from LIDAR data and multispectral images is presented. A classification technique using various cues derived from these data is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. Both first and last pulse data and the normalised difference vegetation index are used in that process. We describe the algorithms involved, giving examples for a test site in Fairfield (Victoria)

Automatic Handwritten Signature Verification System for Australian Passports

We present an automatic handwritten signature verification system to prevent identity fraud by verifying the authenticity of signatures on Australian passports. In this work, fuzzy modeling has been employed for developing a robust recognition system. The knowledge base consists of unique angle features extracted using the box method. These features are fuzzified by an exponential membership function, consisting of two structural parameters which have been devised to track even the minutest variations in a person's signature. The membership functions in turn constitute the weights in the Takagi-Sugeno (TS) model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. The efficacy of the proposed system has been tested on a large database of over 1200 signature images obtained from 40 volunteers achieving a recognition rate of more than 99%

Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images

A method for the automatic detection of buildings and their roof planes from LIDAR data and multispectral images is presented. For building detection, a classification technique is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. The detection of roof planes is based on a region growing algorithm applied to the LIDAR data, the seed regions detected by a grey-level segmentation of the multispectral images. We describe the algorithms involved, giving examples for a test site in Fairfield (Sydney)

Benefits of hybrid DCT domain image matching

An enhancement to least squares image matching is proposed which combines a Discrete Cosine Transform (DCT) domain solution of the linearized normal equations, and resampling between iterations in the pixel domain. This approach reduces the size of the normal equations by discarding higher frequency DCT coefficients, while avoiding the overhead of image resampling in the DCT domain. A method for computing the DCT of the sampled derivative of a function from the DCT of its samples is given, and the least squares problem is framed in the DCT domain. In an experimental comparison between the proposed algorithm and an equivalent pixel domain algorithm, we find that the match time can be halved for 32 × 32 pixel windows, and reduced to 75% for 16 × 16 windows, while measures of match quality remain comparable or improve. The measures of much quality considered were the mean and standard deviation of the disparity error, and the number of match windows that converged. The optimum percentages of DCT coefficients for these window sizes were 20% for the 16 × 16 window and 10% for the 32 × 32 window. An 8 × 8 window size was also tested, but showed no speed-up over the pixel domain algorithm. The approach incorporates derivative estimates that result in better accuracy than can be achieved using the first differences of a pixel domain approach

Stereo image matching using robust estimation and image analysis techniques for DEM generation

Digital Elevation Models (DEM) produced by digital photogrammetry workstations are often used as a component in complex Geographic Information Systems (GIS) modeling. Since the accuracy of GIS databases must be within a specified range for appropriate analysis of the information and subsequent decision making, an accurate DEM is needed. Conventional image matching techniques may be classified as either area-based or feature-based methods. These image matching techniques could not overcome the disparity discontinuities problem and only supply a Digital Surface Model (DSM). This means that matching may not occur on the terrain surface, but on the top of man-made objects such as houses, or on the top of the vegetation. In order to get more accurate DEM from overlapping digital aerial images and satellite images, a 3D terrain reconstruction method using compound techniques is proposed. The area-based image matching method is used to supply dense disparities. Image edge detection and texture analysis techniques are used to find houses and tree areas. Both these parts are robustified in order to avoid outlyers. The final DEM comes from the two parts of image matching and image analysis and hence overcomes errors in the DEM caused by matching on tops of trees or man-made objects

A First Order Predicate Logic Formulation of the 3D Reconstruction Problem and its Solution Space

This paper defines the 3D reconstruction problem as the process of reconstructing a 3D scene from numerous 2D visual images of that scene. It is well known that this problem is ill-posed, and numerous constraints and assumptions are used in 3D reconstruction algorithms in order to reduce the solution space. Unfortunately, most constraints only work in a certain range of situations and often constraints are built into the most fundamental methods (e.g. Area Based Matching assumes that all the pixels in the window belong to the same object). This paper presents a novel formulation of the 3D reconstruction problem, using a voxel framework and first order logic equations, which does not contain any additional constraints or assumptions. Solving this formulation for a set of input images gives all the possible solutions for that set, rather than picking a solution that is deemed most likely. Using this formulation, this paper studies the problem of uniqueness in 3D reconstruction and how the solution space changes for different configurations of input images. It is found that it is not possible to guarantee a unique solution, no matter how many images are taken of the scene, their orientation or even how much color variation is in the scene itself. Results of using the formulation to reconstruct a few small voxel spaces are also presented. They show that the number of solutions is extremely large for even very small voxel spaces (5 x 5 voxel space gives 10 to 10(7) solutions). This shows the need for constraints to reduce the solution space to a reasonable size. Finally, it is noted that because of the discrete nature of the formulation, the solution space size can be easily calculated, making the formulation a useful tool to numerically evaluate the usefulness of any constraints that are added

A Search for Dark Higgs Bosons

Recent astrophysical and terrestrial experiments have motivated the proposal of a dark sector with GeV-scale gauge boson force carriers and new Higgs bosons. We present a search for a dark Higgs boson using 516 fb-1 of data collected with the BABAR detector. We do not observe a significant signal and we set 90% confidence level upper limits on the product of the Standard Model-dark sector mixing angle and the dark sector coupling constant.Comment: 7 pages, 5 postscript figures, published version with improved plots for b/w printin

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Search for the standard model Higgs boson in the H to ZZ to 2l 2nu channel in pp collisions at sqrt(s) = 7 TeV

A search for the standard model Higgs boson in the H to ZZ to 2l 2nu decay channel, where l = e or mu, in pp collisions at a center-of-mass energy of 7 TeV is presented. The data were collected at the LHC, with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. No significant excess is observed above the background expectation, and upper limits are set on the Higgs boson production cross section. The presence of the standard model Higgs boson with a mass in the 270-440 GeV range is excluded at 95% confidence level.Comment: Submitted to JHE