Browsing by Author "Tagare, Hemant D."

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    A theory of photometric stereo for a general class of reflectance maps
    (1990) Tagare, Hemant D.; de Figueiredo, Rui J. P.
    Photometric stereo is an image processing technique for 2$1\over 2$ dimensional surface reconstruction from local shading. The classical theory of photometric stereo has been developed only for surfaces that reflect in a Lambertian plus specular manner. However, there is plenty of experimental evidence that most real-world surfaces are not Lambertian plus specular. This thesis develops the theory of photometric stereo for non-Lambertian surfaces. First, based on the physics of reflection and scattering, a general class of reflectance maps is proposed. This class is shown to model real world data more accurately than the Lambertian model. Then, the normalized photometric stereo equation using these reflectance maps is analyzed and conditions for a globally unique solution for the equation are obtained. Furthermore, the un-normalized photometric stereo equation is studied and conditions for getting a globally unique solution using only three light sources are identified. The problem of jointly estimating the reflectance map and the surface normal is proposed and shown to be ill-posed. A regularized solution to the problem is demonstrated. Finally, it is shown that extra light sources are needed to obtain a complete reconstruction of the surface, and the number of new light sources needed to achieve this is identified.
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    Estimation of repetition rate from signal and texture features
    (1983) Tagare, Hemant D.; Figueiredo, Rui J. P. de; Parks, Thomas W.; Dufour, Reginald J.
    This thesis develops relevant definitions and a theoretical basis for estimating the repetition rate of a random repetitive signal. The repetition rate is estimated by looking for repetition amongst local features of the signals. These features have to satisfy a uniqueness condition, and we have shown that the derivatives of a signal constitute a set of such features. The estimator has been shown to be asymptotically unbiased. The estimation algorithm can not only be tuned to the waveshape information of the signal (by a proper choice of features), but also to the extent of non-stationarity expected in the input signal class. A set of features has been obtained for applying this algorithm to repetitive textured images and voiced speech signals. Vith these features, it has been possible to extract the repetition rate in both the above classes of signals. In the case of voiced speech this rate corresponds to its pitch.