Development of non-contact measurement techniques for 3d shape analysis

The research activity focuses on the development of measurement methods for the inspection of complex geometric structures. The potential of combining different scanning techniques with Alpha Shape, Alpha Critical, Alpha Optimal and Convex Hull algorithms for different application and shape complexity has been developed for: invariant shape (e.g., 3D printed object), slow variant shape (e.g., plants), moving object (e.g., human body). Thus, the work is aimed towards the defining procedure and models to quantify the uncertainty of volume measurements with and without references. In addition, the measurement methods have been developed in order to be valid in the case of low-cost 3D scanning techniques. The major contributions of this thesis work are in the possibility of use low-cost 3D scanning approach of complex geometry for shape measurement and volume estimation. The following item are discussed in this thesis: The advantage of Structured Light scanning system on different applications (Cultural heritage, biomedicine, sport training, design); deep Learning algorithms for detecting anomalies in lettuce plant growing in a controlled greenhouse (agriculture); relationship between leaf area and volume of artificial apple tree (agriculture, controlled environment); 3D shape measurement techniques for human body reconstruction (biomedicine): multi-scanning approach with low and high-cost instrument. In all this applications, the main 3D scanning techniques with different cost ranges and resolutions were used, compared and often integrated with each other. Besides improving the geometry of the model, the integration aims on supporting quality of surface measurement and improve interpretation and detail in complex object. Chapter one: This chapter illustrate a theory background and literature covering the different measurement technologies in the integration of scanner technologies and feature extraction, texture mapping and image segmentation. Chapter two: This chapter summarizes data acquisition and pre-processing methods in order to generate the 3d models from the different scanner techniques for complex shape geometry. Here the different 3d measurements technique and volume evaluation algorithms are shown. Chapter three: In this chapter, the evaluation of uncertainty surface measurement reconstruction and volume evaluation by different algorithms will be applied on static geometry in different fields and scenarios. Chapter four: Methodology for 3D reconstruction and volume estimation by the algorithms on artificial trees. Chapter five: A Deep Learning Algorithms for detecting anomalies in lettuce plant growing, 2D and 3D data comparison is presented. Chapter six: In this chapter a methodology for the reconstruction of a complex and movement shape is presented.