A simple algorithm for matching three-dimensional objects is proposed, based on observed silhouettes. Experiments show that adding silhouettes improves matching through moments and Fourier transform coefficients. Convergence speed depends on silhouette information; small changes in 3D structure occur with low-information silhouettes. Fast convergence is achieved for man-made objects with clear edges when principal or orthogonal silhouettes are used. The method fails when object parts are missing or the object is not isolated, as principal direction calculations become inaccurate. The paper also discusses fractal-based texture classification, where texture properties are analyzed based on changes in resolution. Fractal properties are derived from area changes at different resolutions and used for texture comparison. The area of the gray level surface is measured at multiple resolutions, with fractal signatures derived from the rate of area decrease. These signatures are used for texture classification, and the method is tested on various textures. The paper also explores directional properties of textures, showing how fractal signatures vary with direction. It discusses the use of directional neighborhoods to analyze texture characteristics, such as the "raffia" texture. The study concludes that fractal-based texture analysis provides effective classification, with fractal signatures capturing texture details without requiring artificial frequency decomposition. The method is robust for textures that are not strictly fractal, and further research is ongoing to apply fractal analysis to industrial materials and compare it with other surface-texture probing techniques. The paper also presents a method for determining motion parameters in scenes with translation and rotation, using a hypothesize-and-verify approach. This method combines ideas from Jain and Prazdny to find translational and rotational parameters, and is tested on synthetic and real scenes. The method is sensitive to noise and requires better low-level techniques for real-world applications.A simple algorithm for matching three-dimensional objects is proposed, based on observed silhouettes. Experiments show that adding silhouettes improves matching through moments and Fourier transform coefficients. Convergence speed depends on silhouette information; small changes in 3D structure occur with low-information silhouettes. Fast convergence is achieved for man-made objects with clear edges when principal or orthogonal silhouettes are used. The method fails when object parts are missing or the object is not isolated, as principal direction calculations become inaccurate. The paper also discusses fractal-based texture classification, where texture properties are analyzed based on changes in resolution. Fractal properties are derived from area changes at different resolutions and used for texture comparison. The area of the gray level surface is measured at multiple resolutions, with fractal signatures derived from the rate of area decrease. These signatures are used for texture classification, and the method is tested on various textures. The paper also explores directional properties of textures, showing how fractal signatures vary with direction. It discusses the use of directional neighborhoods to analyze texture characteristics, such as the "raffia" texture. The study concludes that fractal-based texture analysis provides effective classification, with fractal signatures capturing texture details without requiring artificial frequency decomposition. The method is robust for textures that are not strictly fractal, and further research is ongoing to apply fractal analysis to industrial materials and compare it with other surface-texture probing techniques. The paper also presents a method for determining motion parameters in scenes with translation and rotation, using a hypothesize-and-verify approach. This method combines ideas from Jain and Prazdny to find translational and rotational parameters, and is tested on synthetic and real scenes. The method is sensitive to noise and requires better low-level techniques for real-world applications.