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A broadband green light source was demonstrated using a tandem-poled lithium niobate (TPLN) crystal. The measured wavelength and temperature bandwidth were 6.5 nm and 100 °C, respectively, spectral bandwidth was 36 times broader than the periodically poled case. Although the conversion efficiency was smaller than in the periodic case, the TPLN device had a good figure of merit owing to the extremely large bandwidth for wavelength and temperature. The developed broadband green light source exhibited speckle noise approximately one-seventh of that in the conventional approach for a laser projection display.

This paper describes a neural network (NN) based system for recognition and pose estimation of an unoccluded three-dimensional (3-D) object from any single two-dimensional (2-D) perspective view. The approach is invariant to translation, orientation, and scale. First, the binary silhouette of the object is obtained and normalized for translation and scale. Then, the object is represented by a set of rotation invariant features derived from the complex orthogonal pseudo-Zernike moments of the image. The recognition scheme combines the decisions of a bank of multilayer perceptron NN classifiers operating in parallel on the same data. These classifiers have different topologies and internal parameters, but are trained on the same set of exemplar perspective views of the objects. Next, two pose parameters, elevation and aspect angles, are obtained by a novel two-stage NN system consisting of a quadrant classifier followed by NN angle estimators. Performance is tested on clean and noisy data bases of military ground vehicles. Comparative studies with three other classifiers (a single NN, the weighted nearest-neighbor classifier, and a binary decision tree) are carried out.

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