Multi-Functional 3D Curvilinear Self-Folding of Glassy Polymers

The selective light absorption of pre-stretched thermoplastic polymeric films enables wireless photothermal shape morphing from two-dimensional Euclidean geometry of films to three-dimensional (3D) curvilinear architectures. For a facile origami-inspired programming of 3D folding, black inks are printed on glassy polymers that are used as hinges to generate light-absorption patterns. However, the deformation of unpatterned areas and/or stress convolution of patterned areas hinder the creation of accurate curvilinear structures. In addition, black inks remain in the film, prohibiting the construction of transparent 3D architectures. In this study, we demonstrate the facile preparation of transparent 3D curvilinear structures with the selection of the curvature sign and chirality via the selective light absorption of detachable tapes. The sequential removal of adhesive patterns allowed sequential folding and the control of strain responsivity in a single transparent architecture. The introduction of multiple heterogeneous non-responsive materials increased the complexity of strain engineering and functionality. External stimuli responsive kirigami-based bridge triggered the multi-material frame to build the Gaussian curvature. Conductive material casted on the film in a pattern retained the conductivity, despite local deformation. This type of tape patterning system, adopting various materials, can achieve multifunction including transparency and conductivity.

Multifunctional Three-Dimensional Curvilinear Self-Folding of Glassy Polymers

The selective light absorption of prestretched thermoplastic polymeric films enables wireless photothermal shape morphing from two-dimensional Euclidean geometry of films to three-dimensional (3D) curvilinear architectures. For a facile origami-inspired programming of 3D folding, black inks are printed on glassy polymers that are used as hinges to generate light-absorption patterns. However, the deformation of unpatterned areas and/or stress convolution of patterned areas hinder the creation of accurate curvilinear structures. In addition, black inks remain in the film, prohibiting the construction of transparent 3D architectures. In this study, we demonstrate the facile preparation of transparent 3D curvilinear structures with the selection of the curvature sign and chirality via the selective light absorption of detachable tapes. The sequential removal of adhesive patterns allowed sequential folding and the control of strain responsivity in a single transparent architecture. The introduction of multiple heterogeneous nonresponsive materials increased the complexity of strain engineering and functionality. External stimuli responsive kirigami-based bridge triggered the multimaterial frame to build the Gaussian curvature. Conductive material casted on the film in a pattern retained the conductivity, despite local deformation. This type of tape patterning system, adopting various materials, can achieve multifunction including transparency and conductivity.

LIVE EXTRACTION OF CURVILINEAR STRUCTURES FROM LIDAR RAW DATA

In this paper, a general framework is proposed for live extraction of curvilinear structures such as roads or ridges from airborne LiDAR raw data, in the scope of present and past man-environment interaction studies. Unlike most approaches in literature, classified ground points are directly processed here, rather than derived products such as digital terrain models (DTM). This allows to detect possible lacks of ground points due to LiDAR signal occlusions caused by dense coniferous canopies. An efficient and simple solution based on discrete geometry tools is described for supervised context in which the user just indicates where the extraction should take place. Fast response times are required to ensure a good man-system interaction.The framework performance is first evaluated on the example of the extraction of forest roads in a mountainous area, as these objects are well marked in the DTM and hence provide some kind of ground truth. Good execution time and accuracy level are reported. Then this framework is applied to the detection of prominent curvilinear structures, which are much more diffuse objects, but of greater interest than roads in the scope of the present project. Achieved results show high potential of the proposed approach to help archaeologists and geomorphologists in finding areas of interest for future prospection using LiDAR data.

Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels

The reduction of unnecessary details is important in a variety of imaging tasks. Image denoising can be generally formulated as a diffusion process that iteratively suppresses undesirable image features with high variance. We propose a recursive diffusion process that simultaneously computes the local geometrical property of the image features and determines the size and shape of the diffusion kernel, leading to an anisotropic scale-space. In the construction of the proposed anisotropic scale-space, image features due to undesirable noise are suppressed while significant geometrical image features such as edges and corners are preserved across the scale-space. The diffusion kernels are iteratively determined based on the local geometrical properties of the image features. We demonstrate the effectiveness and robustness of the proposed algorithm in the detection of curvilinear features using simple yet effective synthetic and real images. The algorithm is quantitatively evaluated based on the identification of fissures in lung CT imagery. The experimental results indicate that the proposed algorithm can be used for the detection of linear or curvilinear structures in a variety of images ranging from satellite to medical images.