Contour line and geographic feature extraction from USGS color topographical paper maps

This paper presents a method for the extraction of contour lines and other geographic information from scanned color images of topographical maps. Although topographic maps are available from many suppliers, this work focuses on United States Geological Survey (USGS) maps. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. These difficulties render simple approaches such as clustering ineffective. The proposed method overcomes these difficulties using a multistep process. First, a color key set, designed to comprehend color aliasing and false colors, is generated using an eigenvector line-fitting technique in RGB space. Next, area features, representing vegetation and bodies of water, are extracted using RGB color histogram analysis in order to simplify the next stage. Then, linear features corresponding to roads and rivers including contours, are extracted using a valley seeking algorithm operating on a transformed version of the original map. Finally, an A* search algorithm is used to link valleys together to form linear features and to close the gaps caused by intersecting features. The performance of the algorithm is tested on a number of USGS topographic map samples.     

Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

Maximum likelihood estimation methods for multispectral randomfield image models

Considers the problem of estimating parameters of multispectral random field (RF) image models using maximum likelihood (ML) methods. For images with an assumed Gaussian distribution, analytical results are developed for multispectral simultaneous autoregressive (MSAR) and Markov random field (MMRF) models which lead to practical procedures for calculating ML estimates. Although previous work has provided least squares methods for parameter estimation, the superiority of the ML method is evidenced by experimental results provided in this work. The effectiveness of multispectral RF models using ML estimates in modeling color texture images is also demonstrated     

A spatial correlation based method for neighbor set selection inrandom field image models

Random field (RF) models have widespread application in image modeling and analysis. The effectiveness of these models is largely dependent on the choice of neighbor sets, which determine the spatial interactions that are representable by the model. We consider the problem of selecting these neighbor sets for simultaneous autoregressive and Gauss-Markov random field models, based on the correlation structure of the image to be modeled. A procedure for identifying appropriate neighbor sets is proposed, and experimental results which demonstrate the viability of this method are presented.     

A vision system for inspection of ball bonds and 2-D profile ofbonding wires in integrated circuits

A critical stage in the manufacture of integrated circuit devices is inspection of the wire bonds which connect the chip to the lead fingers of the device. This paper describes a vision system for 1) automatic inspection of that part of the wire bond where the wire connects to the bond pad on the chip and 2) inspection of the 2-D profile of the bonding wire. A popular type of bonding (connection to bond pad) known as “ball bond” is considered here. Using two-dimensional images taken from the top of the IC wafer, the system determines several geometric measures which are important in determining the quality of the bond and the wire. These include the center and the boundary of the bond as well as the degree of straightness of the wire. A bond shape analysis based on the parameters of the best fitting ellipse to the bond is developed. The bonding process can be monitored through tracking the statistics of the bond shape measures. The system has been tested in a prototype manufacturing environment with excellent results     

Estimation of single sweep steady-state visual evoked potentials byadaptive line enhancement

The adaptive line enhancer (ALE) was used to obtain estimates of the single sweep steady-state visual evoked potential (SSVEP). Some relevant theoretical properties of the ALE are reviewed in the context of designing the ALE for this particular application. Using power spectral density estimates of the unprocessed and enhanced SSVEP data it was found that the method enhanced the estimated signal-to-noise ratio of the single sweep SSVEP by as much as 10 dB     

Invariant image recognition by Zernike moments

The problem of rotation-, scale-, and translation-invariant recognition of images is discussed. A set of rotation-invariant features are introduced. They are the magnitudes of a set of orthogonal complex moments of the image known as Zernike moments. Scale and translation invariance are obtained by first normalizing the image with respect to these parameters using its regular geometrical moments. A systematic reconstruction-based method for deciding the highest-order Zernike moments required in a classification problem is developed. The quality of the reconstructed image is examined through its comparison to the original one. The orthogonality property of the Zernike moments, which simplifies the process of image reconstruction, make the suggest feature selection approach practical. Features of each order can also be weighted according to their contribution to the reconstruction process. The superiority of Zernike moment features over regular moments and moment invariants was experimentally verified     

A physics-based coordinate transformation for 3-D image matching

Many image matching schemes are based on mapping coordinate locations, such as the locations of landmarks, in one image to corresponding locations in a second image. A new approach to this mapping (coordinate transformation), called the elastic body spline (EBS), is described. The spline is based on a physical model of a homogeneous, isotropic three-dimensional (3-D) elastic body. The model can approximate the way that some physical objects deform. The EBS as well as the affine transformation, the thin plate spline and the volume spline are used to match 3-D magnetic resonance images (MRI's) of the breast that are used in the diagnosis and evaluation of breast cancer. These coordinate transformations are evaluated with different types of deformations and different numbers of corresponding (paired) coordinate locations. In all but one of the cases considered, using the EBS yields more similar images than the other methods     

An adaptive modular artificial neural network hourly loadforecaster and its implementation at electric utilities

This paper describes a modular artificial neural network (ANN) based hourly load forecaster which has already been implemented at 20 electric utilities across the US and is being used on-line by several of them. The behavior or the load and its correlation with parameters affecting it (e.g. weather variables) are decomposed into three distinct trends of weekly, daily, and hourly. Each trend is modeled by a separate module containing several multi-layer feed-forward ANNs trained by the back-propagation learning rule. The forecasts produced by each module are then combined by adaptive filters to arrive at the final forecast. During the forecasting phase, the parameters of the ANNs within each module are adaptively changed in response to the system's latest forecast accuracy. The performance of the forecaster has been tested on data from these 20 utilities with excellent results. The on-line performance of the system has also been quite satisfactory and superior to other forecasting packages used by the utilities. Moreover, the forecaster is robust, easy to use, and produces accurate results in the case of rapid weather changes     

A constraint learning feedback dynamic model for stereopsis

This paper presents a stereo matcher inspired by the earlier work of Marr and Poggio (1976). Two major extensions are introduced: the algorithm is extended to gray-level images, and the inhibitory/excitatory weights of the model are learned rather than set a priori according to “uniqueness” and “continuity” constraints. Gray level stereo pairs of real scenes with known disparity maps are used to train the model. The trained system is successfully tested on other gray level stereo pairs of real scenes as well as a set of random dot stereograms. Performance is compared to a recent stereo matching algorithm