COMPUTER VISION, IMAGE PROCESSING, ROBOTICS AND NEUROCOMPUTING USING CLIFFORD GEOMETRIC ALGEBRA Clifford algebras are well-known to pure mathematicians. In this research work we are using an interpretation called geometric algebra which is a coordinate-free approach to geometry. The elements are coordinate-independent objects called multivectors which can be multiplied together using a geometric product. The system deals with rotations in n-dimensional space very efficiently. Since geometric algebra has already been successfully applied to many areas of mathematical physics and engineering, we believe that geometric algebra will throw new lights in modern linear and nonlinear signal processing. The system appears also as the ideal mathematical framework for tasks of the perception action cycle systems. The topics of my work include: - Computer Vision: binocular and trinocular geometry. Affine and projective reconstruction. Trifocal tensor and invariants for matching, object recognition and image coding. - Lie Groups and Lie Algebras: in the geometric algebra frame for the computation of differential invariants, affine structure of image sequences and visual symmetries for visual guided robot navigation. - Robotics: We use the 4D algebra of the motors for 3D kinematics. The motor algebra together with fuzzy logic are being used for geometric reasoning useful for object avoidance and navigation. In terms of motors we represent points, lines and planes and their motion. These entities and their spatial invariants are being used for manoeuvre. Hand-eye calibration using motors for a binocular head on a mobile robot. The control of a binocular head is being formulated as a problem of multivector control. Related controllers, filters and estimators are extended for multidimensional control. - Neural Computing: We believe that neural learning is an issue of geometric learning. We have generalized the standard MLP and RBF neural nets and the back-propagation training rule in the geometric algebra framework. The nets show a much reasonably performance during learning and in the generalization due to the geometric product, the avoidance of redundant components and the coordinate independence of the data coding. We are improving the learning in these architectures using the sub-manifold intrinsic dimensionality. - Teaching on Applied Clifford or Geometric Algebra: We are developing a computer program for teaching applied geometric algebra in schools, colleges, universities and research centers. This involves the representation and manipulation of geometric entities in different geometric algebras G_{p,q,r}. For the symbolic processing and visualization we use Matlab. Our examples involve problems of the above cited fields.