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The manufacture of In Vitro Fertilization (IVF) needles is subject to the most stringent quality demands. This makes automated inspection challenging due to difficulty in reliably classifying conforming and non-conforming (defective) products due to factors including multidimensional variation of their tip geometry and the lack of an explicit quality standard. In addition, developing an IVF needle image dataset, which broadly contains the visual characteristics of qualified and defective products, is difficult without commissioning large and costly production runs. The most important original contribution of this work is a new solution to investigate and quantify the uncertainty in the quality standard of IVF needles by integrating inter-disciplinary techniques. This work utilizes a low-cost, virtual dataset of synthetic images, generated by the automated photo-realistic rendering of a three-dimensional (3D) parametric model to simulate manufacturing variation. Then, the unknown numerical (critical) quality thresholds are obtained by estimating the relationship between quality response and measurement predictors using an Ordinal Logistic Regression (OLR) algorithm on the synthetic images. The fitted models exhibited increased overall predictive accuracy of up to 11.02% than the machine learning models (available in MATLAB) and could provide objective guidance on classifying specific quality aspects of a product.

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This paper presents a sensor-control method for choosing the best next state of the sensor(s), that provide(s) accurate estimation results in a multi-target tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy.