A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms' coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type.
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