Rational-operator-based depth-from-defocus approach to scene reconstruction

This paper presents a rational-operator-based approach to depth from defocus (DfD) for the reconstruction of three-dimensional scenes from two-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF) and the second based on the generalized Gaussian PSF, are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered for real scenes and show that both approaches outperform existing RO-based methods

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table

Deep Learning and Hybrid Approach for Particle Detection in Defocusing Particle Tracking Velocimetry

The present work aims at the improvement of particle detection in defocusing particle tracking velocimetry (DPTV) by means of a novel hybrid approach. Two deep learning approaches, namely faster R-CNN and RetinaNet are compared to the performance of two benchmark conventional image processing algorithms for DPTV. For the development of a hybrid approach with improved performance, the different detection approaches are evaluated on synthetic and images from an actual DPTV experiment. First, the performance under the influence of noise, overlaps, seeding density and optical aberrations is discussed and consequently advantages of neural networks over conventional image processing algorithms for image processing in DPTV are derived. Furthermore, current limitations of the application of neural networks for DPTV are pointed out and their origin is elaborated. It shows that neural networks have a better detection capability but suffer from low positional accuracy when locating particles. Finally, a novel Hybrid Approach is proposed, which uses a neural network for particle detection and passes the prediction onto a conventional refinement algorithm for better position accuracy. A third step is implemented to additionally eliminate false predictions by the network based on a subsequent rejection criterion. The novel approach improves the powerful detection performance of neural networks while maintaining the high position accuracy of conventional algorithms, combining the advantages of both approaches

Task Specific Uncertainty in Coordinate Measurement

Task specific uncertainty is the measurement uncertainty associated with the measurement of a specific feature using a specific measurement plan. This paper surveys techniques developed to model and estimate task specific uncertainty for coordinate measuring systems, primarily coordinate measuring machines using contacting probes. Sources of uncertainty are also reviewed