In automated visual surface inspection based on statistical pattern recognition, the collection of training material for setting up the classifier may appear to be difficult. Getting a representative set of labelled training samples requires scanning through large amounts of image material by the training personnel, which is an error prone and laborious task. Problems are further caused by the variations of the inspected materials and imaging conditions, especially with color imaging. Approaches based on adaptive defect detection and robust features may appear inapplicable because of losing some faint or large area defects. Adjusting the classifier to adapt to the changed situation may appear difficult because of the inflexibility of the classifiers’ implementations. This may lead to impractical often repeated training material collection and classifier retraining cycles. In this paper we propose a non-segmenting defect detection technique combined with a self-organizing map (SOM) based classifier and user interface. The purpose is to avoid the problems with adaptive detection techniques, and to provide an intuitive user interface for classification, helping in training material collection and labelling, and with a possibility of easily adjusting the class boundaries. The approach is illustrated with examples from wood surface inspection.

Abstract—This paper presents a multiresolution general-purpose high-speed machine vision sensor with on-chip image processing capabilities. The sensor comprises an innovative multiresolution sensing area, 1536 A/D converters, and a SIMD array of 1536 bit-serial processors with corresponding memory. The sensing area consists of an area part with 1536 512 pixels, and a line-scan part with a set of rows with 3072 pixels each. The SIMD processor array can deliver more than 100 GOPS sustained and the on-chip pixel-analysing rate can be as high as 4 Gpixels/s. The sensor is ideal for high-speed multisense imaging where, e.g., color, greyscale, internal material light scatter, and 3-D profiles are captured simultaneously. When running only 3-D laser triangulation, a data rate of more than 20 000 profiles/s can be achieved when delivering 1536 range values per profile with 8 bits of range resolution. Experimental results showing very good image characteristics and a good digital to analog noise isolation are presented. Index Terms—APS, CMOS image sensors, laser triangulation, machine vision, MAPP, multiresolution, multisense, smart vision sensors, 3-D.

This paper shows the power of the combination of smart sensor technique and real-time parallel processing. This implementation results in a very compact system which consists only of four cameras and an extension board to a PC. 1