Dimensional control by artificial vision is becoming a standard tool for industrialists interested in such remote and without contact measurement methods. The expected accuracy of those systems is dependent on camera resolution. High precision requires very costly charge coupling device sensors and frame grabbers. The proposed method tends to increase significantly the precision of dimensional measurements without increasing the hardware complexity. This algorithm is also quite robust against noisy images as it can be encountered in real world imaging; a precision of 1/16 pixel can easily be obtained with signal to noise ratio=2 dB. Our approach aims at improving the edge detection process involved in dimensional control by artificial vision. A lot of edge detection techniques with pixel resolution are well known and some of them are designed in order to be robust against image corruption. On the other hand B-spline interpolation methods have been considerably improved and popularized by the signal processing techniques proposed by M. Unser et al. An algorithm resulting from the merging of these two ideas is proposed in this paper. In this algorithm, the interpolation is prepared by an optimized filtering and by a detection of local maxima of gradient.