We consider the problem of communicating compact descriptors for the purpose of establishing visual correspondences
between two cameras operating under rate constraints. Establishing visual correspondences is a critical
step before other tasks such as camera calibration or object recognition can be performed in a network of cameras.
We verify that descriptors of regions which are in correspondence are highly correlated, and propose the use
of distributed source coding to reduce the bandwidth needed for transmitting descriptors required to establish
correspondence. Our experiments demonstrate that the proposed scheme is able to provide compression gains of
57% with minimal loss in the number of correctly established correspondences compared to a scheme that communicates
the entire image of the scene losslessly in compressed form. Over a wide range of rates, the proposed
scheme also provides superior performance when compared to simply transmitting all the feature descriptors.
KEYWORDS: Scanning electron microscopy, Reflectivity, Sensors, Semiconductors, Metrology, Critical dimension metrology, Electron beams, Manufacturing, Detection and tracking algorithms, 3D modeling
As the fabrication density increases in semiconductor manufacturing processes, cost effective determination of the exact dimensions of various device-interconnects becomes a critical issue. Single detector based critical dimension metrology systems are attractive due to cost effectiveness. Yet, deducing dimensions like sidewall curvature and 3D shape of the semiconductor surface are quite difficult using a single detector system. Non-destructive metrological systems are also very desirable in evaluating critical dimension and manufacturing error because they are inexpensive compared to multi-detector systems.
In our paper, we build upon the physical modeling approach to modify a classical shape from shading algorithm to reconstruct the 3D shape from a single non-stereoscopic SEM image. Our ultimate aim is to design an efficient real-time, non-destructive CD-SEM system using a single detector. Appropriate reflectance function is utilized for low-excitation SEM imagery for reconstructing the depth map of the surface using the shape from shading algorithm. In this paper, we present the results from low-excitation SEM reflectance model used for SFS approach. We compare the results with a standard SFS algorithm that uses Lambertian reflectance model. We also discuss the advantages of this computer vision based approach compared to other destructive CD-SEM technologies.
The use of meteorological radar reflectivity Z to estimate rainfall rate R is approached using a different perspective from the classical Z-R relation. Simultaneous rain measurements from different sensors are combined to construct a model that estimates the vertical air velocity by minimizing the error in reflectivity between the different sensors. This model is based on the fact that rain rate and reflectivity are both dependent on the integrals of rain drop size distribution (DSD) but only R depends on vertical air velocity. This study attempts to validate the vertical air velocity estimates and quantify their affects on the rainfall rate estimation. Disdrometer Flux Conservation Model (DFC) uses measurements from disdrometers and other sensors such as vertically pointing radar profilers and scanning radars. Disdrometers measure a drop size flux (Phi) (D), defined as the number of drops passing a horizontal surface per unit time, per unit area, per drop size. The flux is equal to the product of the drop size distribution near the ground NG(D) and drop velocity near the ground vG(D). The drop velocity is the difference between the droplet terminal velocity and the vertical component of the wind velocity, which varies with altitude. The estimates derived from the DFC model using two pair wise selected sensors are used to study the change of reflectivity and vertical air velocity with altitude. Sensitivity tests for the DFC model are also discussed and these outcomes are validated by comparison with independent profiler vertical velocity observations.
The paper discusses the approach of using single detector system to classify the photo resist surface using different signal and image processing methodologies. We couple the understanding of the physical phenomenon and come up with an integrated method to generally classify the surface depending upon the side wall curvature. Construction of 3-D image of the surface and extracting features from the methods to classify the surfaces are the predominant aspects of the approach. We have used several methods to serve the purpose including the wavelet filters in edge and surface roughness detection. In this paper, non-stereoscopic 3-D shape reconstruction method is applied to address a difficult inverse problem in semiconductor fabrication metrology. The problem is that of deducing a chip's vertical cross-section from two-dimensional top-down scanning electron microscope images of the chip surface. Our results are illustrated with a variety of real data sets. In semiconductor chip fabrication, photo resistive material is used as an overlay, which will protect substrate areas (typically metal), which must remain on the chip after other unprotected substrate areas are etched off. The shape and size of the photo-resist material, at the submicron level, is therefore largely responsible for the shape and quality of the protected substrate. Critical dimension scanning electron microscopy (SEM) is used to determine this shape, and the research addressed in this paper proposes an image processing approach combined with physical modeling, to accurately obtain surface shape information from SEM imaging.
The purpose of this paper is to describe an experimental procedure for verifying the navigation data provided by the NASA Goddard Space time Flight Center's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR). The TRMM PR is a satellite-borne, electronically scanning, range-gated radar that produces 3D images of the structure of atmospheric precipitation. Due to the dynamic nature of precipitation events in space and time, proper collocation is critical to the accuracy of the data.11
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