Data Analyzing via Probabilistic Modeling

Object recognition is one of the topics of artificial intelligence, computer vision, image processing, and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. An important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation are possible using a new method of Hurwitz-Radon matrices.

The Influence of Image Processing and Layer-to-Background Contrast on the Reliability of Flatbed Scanner-Based Characterisation of Additively Manufactured Layer Contours

Flatbed scanners (FBSs) provide non-contact scanning capabilities that could be used for the on-machine verification of layer contours in additive manufacturing (AM) processes. Layer-wise contour deviation assessment could be critical for dimensional and geometrical quality improvement of AM parts, because it would allow for close-loop error compensation strategies. Nevertheless, contour characterisation feasibility faces many challenges, such as image distortion compensation or edge detection quality. The present work evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality, under a metrological perspective. Considered factors include noise filtering, edge detection algorithms, and threshold levels, whereas the distance between the target layer and the background is used to generate different contrast scenarios. Completeness of contour reconstruction is evaluated by means of a coverage factor, whereas its accuracy is determined by comparison with a reference contour digitised in a coordinate measuring machine. Results show that a reliable contour characterisation can be achieved by means of a precise adjustment of image processing parameters under low layer-to-background contrast variability. Conversely, under anisotropic contrast conditions, the quality of contour reconstruction severely drops, and the compromise between coverage and accuracy becomes unbalanced. These findings indicate that FBS-based characterisation of AM layers will demand developing strategies that minimise the influence of anisotropy in layer-to-background contrast.

Three-dimensional contact point determination and contour reconstruction during active whisking behavior of the awake rat

The rodent vibrissal (whisker) system has been studied for decades as a model of active touch sensing. There are no sensors along the length of a whisker; all sensing occurs at the whisker base. Therefore, a large open question in many neuroscience studies is how an animal could estimate the three-dimensional location at which a whisker makes contact with an object. In the present work we simulated the exact shape of a real rat whisker to demonstrate the existence of a unique mapping from triplets of mechanical signals at the whisker base to the three-dimensional whisker-object contact point. We then used high speed video to record whisker deflections as an awake rat whisked against a peg and used the mechanics resulting from those deflections to extract the contact points along the peg surface. A video shows the contour of the peg gradually emerging during active whisking behavior.