Study case: ear reconstruction

The example that we present in this article concerns the reconstruction of an ear. The approach is based on optical acquisition and modeling of the shapes. The final prosthesis is directly obtained from the model by means of rapid prototyping.

The patient’s defect is shown in the Fig. 1. The left ear is seriously damaged in consequence of a burn. To fabricate the prosthetic element, the right ear, shown in Fig. 2, was used as the template.

The test was performed as follows: first, we acquired the right, safe ear. We configured the digital scanner (Vivid 910 , Konica Minolta Inc.) in the MIDDLE configuration. Four views were acquired and aligned together. Then the triangle mesh was obtained and mirrored, in view of using it to model the prosthesis. The corresponding models are shown in Fig 3a, 3b and 3c. As a second step, the defect was gauged. The system configuration was the same as the one in the previous acquisition. Two views were sufficient to cover the whole surface. The mesh was created over the aligned views. The result is presented in Fig 3d.

The third step was the acquisition of the whole patient face in three views, as shown in Fig. 4. This model was used as the skeleton to align the mesh in Fig. 3d to the one in Fig. 3c. The model of the defect was aligned to the skeleton. Then the model of the ear was interactively aligned until the aesthetical appearance on the whole face was judged optimal. At this point, the skeleton was discarded. The two models were edited to fill residual holes and to reconstruct missing surface parts (mainly due to undercuts). Finally, they were finely connected in correspondence with their borders. The result of this step is shown in Fig. 5.

The mesh has been topologically controlled to produce the physical copy. This has been fabricated by means of rapid prototyping technology. The Connex 500 3D Printing System (Objet-Geometries Inc.) has been used. This machine is capable of printing parts and assemblies made of multiple model materials all in a single build. The materials used to fabricate the ear prosthetic element are the TangoBlackPlus Shore A85 for the area corresponding to the auricle surface, and the TangoBlackPlus Shore A27 for the areas at the borders of the ear. The ear was obtained in about one hour; the process is very cheap (the cost is in the order of 70 Euros). Fig. 6 shows the front and the back side of the final prosthesis.

Fig. 7 shows the patient’s face after the application of the prosthetic element. It is worth noting that, in this figure, the prosthesis color is not optimized yet. In fact, we wanted to check its functionality before optimizing it under the aesthetical point of view.

Fig. 7 - Patient face with prosthesis on.

In this process, patient comfort was optimal, since the acquisition step was quick, contactless and safe. The prosthesis try-in was unnecessary. The prototyping step was very cheap, and the overall time required was about six hours, plus the machining of the prosthesis.

Real time acquisition of silhouettes

The aim of this project is to design, implement and charaterise a vision system for the 3D reconstruction of objects rotating with respect to their axis by means of the Silhouette method. The system has been developed using LabView Real-Time 8.6.1 (National Instruments, NI).

 The hardware of the system is composed by the NI Embedded Vision System (EVS), the  Basler Scout scA1390-17gm/gc Gigabit Ethernet camera, and a telecentric lighting system combined with a telecentric optics (Optoengineering, Italy). The software tools dedicated to image acquisition and elaboration have been developed by using the NI-IMAQ vision libraies. The vision system is called  ‘OPTOSILHO‘.

A vision system for a labelling machine

In this work, a vision system specifically designed to monitor the labels on bottling lines for wine production industries is presented. The system is based on smart cameras, Real Time LabView sofware and IMAQ vision libraries. The work has been developed in the frame of a thesis work, in collaboration with Studio Progetti Automation srl (Italy). 

Bottling lines for wine are complex systems, which provide bottle rinsing, filling, capping, labelling, wrapping and pallettising. In this work, a vision system has been studied and implemented for the automatic control of the labelling machine. The bottling line, that is installed at Cantine Leonardo da Vinci (Tuscan, Italy), is characterized by a high level of automation, and is able to output 10.000 bottles per hour. However, the labeling machine is completely operator-dependant: three blocks of labels must be manually positioned on the machine for each wine brand. These are the front label, the back label and the DOCG label. The position of the labels with respect to the bottles is visually controlled and adjusted by the operators.

Due to human errors, it is possible that the operator mount on the machine labels corresponding to a wine brand different with respect to the one programmed on the bottling line. Early detection of this situation is mandatory to reduce downtime and, in the worse case, to avoid that the whole bottling process is performed from scratch.