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This paper describes a 3D object classification method by 3D-3D comparison using the numerical surface point signatures on interest points of 3D objects point cloud. Interest or salient points of 3D point cloud were found by Heat Kernel Signature method. The numerical point signatures used for classification were composed only on these points. To investigate the objects classification resistance to the data measurement noise, additionally to original 3D data was added 1.5 % of continuity distributed noise. Object classification was carried out using forty three 3D objects point cloud database. Study of 3D object interest points recognition has shown that the standard Surface Point Signatures methodology is sensitive to the normal vector used for signature composition as well as the object's surface normal is very sensitive to objects mesh error. In order to reduce the sensitivity to the object surface measurement error we have proposed to use one constant vector as average from all object mesh normal's. Such approach on average improved interest point's recognition rate by ~16 % and allowed to reach 95.9 % of classification accuracy on used 43 objects database.

The objective of this study is to create computer vision algorithms for autonomous multiclass identification of amber nuggets by their colour. By applying the proposed methods an automated production sorting system has been developed. This system can be used, for example in combination with conveyor systems, and in any other case that requires distinguishing objects of many classes in a high-rate flow of objects. In order to achieve this, the proposed system operates with colour features selection, algorithm for classifier training, grouping, and voting with reject option have been developed. The developed system has been used in an automated amber sorting line to increase the quantities of sorted amber nuggets. The applied algorithms gave 88.21% as the highest accuracy for the amber nugget expert database consisting of 30 classes.