Direct Differential Photometric Stereo Shape Recovery of Diffuse and Specular Surfaces

This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10851-016-0633-0Recovering the 3D shape of an object from shading is a challenging problem due to the complexity of modeling light propagation and surface reflections. Photometric Stereo (PS) is broadly considered a suitable approach for high-resolution shape recovery, but its functionality is restricted to a limited set of object surfaces and controlled lighting setup. In particular, PS models generally consider reflection from objects as purely diffuse, with specularities being regarded as a nuisance that breaks down shape reconstruction. This is a serious drawback for implementing PS approaches, since most common materials have prominent specular components. In this paper, we propose a PS model that solves the problem for both diffuse and specular components aimed at shape recovery of generic objects with the approach being independent of the albedo values thanks to the image ratio formulation used. Notably, we show that by including specularities, it is possible to solve the PS problem for a minimal number of three images using a setup with three calibrated lights and a standard industrial camera. Even if an initial separation of diffuse and specular components is still required for each input image, experimental results on synthetic and real objects demonstrate the feasibility of our approach for shape reconstruction of complex geometries.The first author acknowledges the support of INDAM under the GNCS research Project “Metodi numerici per la regolarizzazione nella ricostruzione feature-preserving di dati.

Seeing the sound: a new multimodal imaging device for computer vision

Audio imaging can play a fundamental role in computer vision, in particular in automated surveillance, boosting the accuracy of current systems based on standard optical cameras. We present here a new hybrid device for acousticoptic imaging, whose characteristics are tailored to automated surveillance. In particular, the device allows realtime, high frame rate generation of an acoustic map, overlaid over a standard optical image using a geometric calibration of audio and video streams. We demonstrate the potentialities of the device for target tracking on three challenging setup showing the advantages of using acoustic images against baseline algorithms on image tracking. In particular, the proposed approach is able to overcome, often dramatically, visual tracking with state-of-art algorithms, dealing efficiently with occlusions, abrupt variations in visual appearence and camouflage. These results pave the way to a widespread use of acoustic imaging in application scenarios such as in surveillance and security

Machine Learning for Cultural Heritage: A Survey

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML

Virally and physically transgenized equine adipose-derived stromal cells as a cargo for paracrine secreted factors

<p>Abstract</p> <p>Background</p> <p>Adipose-Derived Stromal Cells have been shown to have multiple lineage differentiation properties and to be suitable for tissues regeneration in many degenerative processes. Their use has been proposed for the therapy of joint diseases and tendon injuries in the horse. In the present report the genetic manipulation of Equine Adipose-Derived Stromal Cells has been investigated.</p> <p>Results</p> <p>Equine Adipose-Derived Stromal Cells were successfully virally transduced as well as transiently and stably transfected with appropriate parameters, without detrimental effect on their differentiation properties. Moreover, green fluorescent protein alone, fused to <it>neo </it>gene, or co-expressed as bi-cistronic reporter constructs, driven by viral and house-keeping gene promoters, were tested. The better expressed cassette was employed to stably transfect Adipose-Derived Stromal Cells for cell therapy purposes. Stably transfected Equine Adipose-Derived Stromal Cells with a heterologous secreted viral antigen were able to immunize horses upon injection into the lateral wall of the neck.</p> <p>Conclusion</p> <p>This study provides the methods to successfully transgenize Adipose-Derived Stromal Cells both by lentiviral vector and by transfection using optimized constructs with suitable promoters and reporter genes. In conclusion these findings provide a working platform for the delivery of potentially therapeutic proteins to the site of cells injection via transgenized Equine Adipose-Derived Stromal Cells.</p

Super-resolution 3d reconstruction of thick biological samples: A computer vision perspective

In this paper we present a case-study about recent breakthroughs of three-dimensional (3D) super-resolution live-cell imaging through thick specimens (50 - 150um). This technology is enabling the deep understanding of cellular mechanism by obtaining very detailed 3D descriptions of cells. In particular, we discuss the image analysis problems related to the accurate localization of single molecules. This problem is hard because of the extreme noise conditions, the high and heterogeneous density of the cell molecules and the distortions induced by light-sample interactions on the imaging capabilities. For this reason, robust computational tools are required to obtain the localization of the photo-activated molecules and to enable the super-resolution accuracy. In such context, we show that a novel set of challenges exists and novel Computer Vision approaches are needed for delivering high-performing imaging systems for life science. © 2013 IEEE

Temporal poselets for collective activity detection and recognition

Detection and recognition of collective human activities are important modules of any system devoted to high level social behavior analysis. In this paper, we present a novel semantic-based spatio-temporal descriptor which can cope with several interacting people at different scales and multiple activities in a video. Our descriptor is suitable for modelling the human motion interaction in crowded environments - the scenario most difficult to analyse because of occlusions. In particular, we extend the Pose let detector approach by defining a descriptor based on Pose let activation patterns over time, named TPOS. We will show that this descriptor can effectively tackle complex real scenarios allowing to detect humans in the scene, to localize (in space-time) human activities, and perform collective group activity recognition in a joint manner, reaching state of the art results. \ua9 2013 IEEE