Approximate computing is an emerging design paradigm that trades in accuracy for resource consumption. Due to the limits of human perception, a certain amount of error is tolerated in most applications that involve the human perception. These include, for instance, image and audio processing. In this master thesis, approximate computing techniques are employed on an image processing pipeline embedded in a 3D scanner that extracts laser line data from a camera sensor with the goal of computing the shape of the object being scanned. To do this, the position of the laser line must be determined. In the pipeline, the data stream is manipulated by FIR filters that first smooth it to reduce noise levels and then transform the light peak generated by the laser (point of maximum intensity) into a zero by performing a derivative on the smoothed data.