DeepMatching: Hierarchical Deformable Dense Matching

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al 2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures.We also propose a method for estimating optical flow, called DeepFlow, by integrating DeepMatching in the large displacement optical flow (LDOF) approach of Brox and Malik (2011). Compared to existing matching algorithms, additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation

Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line segment features are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

In-room test results at CNAO of an innovative PT treatments online monitor (Dose Profiler)

The use of C, He and O ions as projectiles in Particle Therapy (PT) treatments is getting more and more widespread as a consequence of their enhanced relative biological effectiveness and oxygen enhancement ratio, when compared to the protons one. The advantages related to the incoming radiation improved efficacy are requiring an accurate online monitor of the dose release spatial distribution. Such monitor is necessary to prevent unwanted damage to the tissues surrounding the tumour that can arise, for example, due to morphological changes occurred in the patient during the treatment with respect to the initial CT scan. PT treatments with ions can be monitored by detecting the secondary radiation produced by the primary beam interactions with the patient body along the path towards the target volume. Charged fragments produced in the nuclear process of projectile fragmentation can be emitted at large angles with respect to the incoming beam direction and can be detected with high efficiency in a nearly background-free environment. The Dose Profiler (DP) detector, developed within the INSIDE project, is a scintillating fibre tracker that allows an online reconstruction and backtracking of such secondary charged fragments. The construction and preliminary in-room tests performed on the DP, carried out using the 12C ions beam of the CNAO treatment centre using an anthropomorphic phantom as a target, will be reviewed in this contribution. The impact of the secondary fragments interactions with the patient body will be discussed in view of a clinical application. Furthermore, the results implications for a pre-clinical trial on CNAO patients, foreseen in 2019, will be discussed

Origin of Small-Scale Anisotropies in Galactic Cosmic Rays

The arrival directions of Galactic cosmic rays (CRs) are highly isotropic. This is expected from the presence of turbulent magnetic fields in our Galactic environment that repeatedly scatter charged CRs during propagation. However, various CR observatories have identified weak anisotropies of various angular sizes and with relative intensities of up to a level of 1 part in 1,000. Whereas large-scale anisotropies are generally predicted by standard diffusion models, the appearance of small-scale anisotropies down to an angular size of 10 degrees is surprising. In this review, we summarise the current experimental situation for both the large-scale and small-scale anisotropies. We address some of the issues in comparing different experimental results and remaining questions in interpreting the observed large-scale anisotropies. We then review the standard diffusive picture and its difficulty in producing the small-scale anisotropies. Having set the stage, we review the various ideas and models put forward for explaining the small-scale anisotropies.Comment: 60 pages, 16 figures; invited review for Progress in Particle and Nuclear Physics (PPNP