Object recognition using shape-from-shading

This paper investigates whether surface topography information extracted from intensity images using a recently reported shape-from-shading (SFS) algorithm can be used for the purposes of 3D object recognition. We consider how curvature and shape-index information delivered by this algorithm can be used to recognize objects based on their surface topography. We explore two contrasting object recognition strategies. The first of these is based on a low-level attribute summary and uses histograms of curvature and orientation measurements. The second approach is based on the structural arrangement of constant shape-index maximal patches and their associated region attributes. We show that region curvedness and a string ordering of the regions according to size provides recognition accuracy of about 96 percent. By polling various recognition schemes. including a graph matching method. we show that a recognition rate of 98-99 percent is achievable

Laser driven self-assembly of shape-controlled potassium nanoparticles in porous glass

We observe growth of shape-controlled potassium nanoparticles inside a random network of glass nanopores, exposed to low-power laser radiation. Visible laser light plays a dual role: it increases the desorption probability of potassium atoms from the inner glass walls and induces the self-assembly of metastable metallic nanoparticles along the nanopores. By probing the sample transparency and the atomic light-induced desorption flux into the vapour phase, the dynamics of both cluster formation/evaporation and atomic photo-desorption processes are characterized. Results indicate that laser light not only increases the number of nanoparticles embedded in the glass matrix but also influences their structural properties. By properly choosing the laser frequency and the illumination time, we demonstrate that it is possible to tailor the nanoparticles'shape distribution. Furthermore, a deep connection between the macroscopic behaviour of atomic desorption and light-assisted cluster formation is observed. Our results suggest new perspectives for the study of atom/surface interaction as well as an effective tool for the light-controlled reversible growth of nanostructures.Comment: 14 pages,6 figures, http://iopscience.iop.org/1612-202X/11/8/085902

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

Differences in protein mobility between pioneer versus follower growth cones

Navigating growth cones need to integrate, process and respond to guidance signals, requiring dynamic information transfer within and between different compartments. Studies have shown that, faced with different navigation challenges, growth cones display dynamic changes in growth kinetics and morphologies. However, it remains unknown whether these are paralleled by differences in their internal molecular dynamics. To examine whether there are protein mobility differences during guidance, we developed multiphoton fluorescence recovery after photobleaching methods to determine molecular diffusion rates in pathfinding growth cones in vivo. Actively navigating growth cones (leaders) have consistently longer recovery times than growth cones that are fasciculated and less actively navigating (followers). Pharmacological perturbations of the cytoskeleton point to actin as the primary modulator of diffusion in differently behaving growth cones. This approach provides a powerful means to quantify mobility of specific proteins in neurons in vivo and reveals that diffusion is important during axon navigation

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft