In this paper we introduce a new method for text detection in natural images. The method comprises two contributions: First, a fast and scalable engine to generate synthetic images of text in clutter. This engine overlays synthetic text to existing background images in a natural way, accounting for the local 3D scene geometry. Second, we use the synthetic images to train a Fully-Convolutional Regression Network (FCRN) which efficiently performs text detection and bounding-box regression at all locations and multiple scales in an image. We discuss the relation of FCRN to the recently-introduced YOLO detector, as well as other end-toend object detection systems based on deep learning. The resulting detection network significantly out performs current methods for text detection in natural images, achieving an F-measure of 84.2% on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per second on a GPU.

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NAND Flash memory-based Solid-State Disks (SSDs) are becoming popular as the storage media in domains ranging from mobile laptops to enterprise-scale storage systems due to a number of benefits (e.g., lighter weights, faster access times, lower power consumption, higher resistance to vibrations) they offer over the conventionally popular Hard Disk Drives (HDDs). While a number of well-regarded simulation environments exist for HDDs, the same is not yet true for SSDs. This is due to SSDs having been in the storage market for relatively less time as well as the lack of information (hardware configuration and software methods) about state-of-the-art SSDs that is publicly available. We describe the design and implementation of FlashSim, a simulator aimed at filling this void in performance evaluation of emerging storage systems that employ SSDs. FlashSim is an event-driven simulator that follows the objected-oriented programming paradigm for modularity. We have validated the performance of FlashSim against a number of commercial SSDs for behavioral similarity. We have also used FlashSim to compare the performance of SSD devices employing different Flash Translation Layer (FTL) schemes, and analyzed the energy consumption of different FTL schemes in the SSD. FlashSim has been written to be inter-operable with the well-regarded DiskSim simulator, thus enabling the simulation of a variety of "hybrid" storage systems employing combinations of SSDs and HDDs. Given the current interest in such hybrid systems as opposed to systems with SSDs replacing HDDs (due to higher price), we believe this to be an especially useful feature of FlashSim. We have made FlashSim freely available for download with the hope that it would be of use to researchers exploring the design of SSD-based systems.

We propose a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses. Video frames differ primarily in the pose of the objects they contain, so our method distils the pose information by analyzing the differences between frames. The distillation uses a new dual representation of the geometry of objects as a set of 2D keypoints, and as a pictorial representation, i.e. a skeleton image. This has three benefits: (1) it provides a tight 'geometric bottleneck' which disentangles pose from appearance, (2) it can leverage powerful image-to-image translation networks to map between photometry and geometry, and (3) it allows to incorporate empirical pose priors in the learning process. The pose priors are obtained from unpaired data, such as from a different dataset or modality such as mocap, such that no annotated image is ever used in learning the pose recognition network. In standard benchmarks for pose recognition for humans and faces, our method achieves state-of-the-art performance among methods that do not require any labelled images for training

Abstract-Suturing is an important yet time-consuming part of surgery. A fast and robust autonomous procedure could reduce surgeon fatigue, and shorten operation times. It could also be of particular importance for suturing in remote telesurgery settings where latency can complicate the master-slave mode control that is the current practice for robotic surgery with systems like the da Vinci R . We study the applicability of the trajectory transfer algorithm proposed in [12] to the automation of suturing. The core idea of this procedure is to first use non-rigid registration to find a 3D warping function which maps the demonstration scene onto the test scene, then use this warping function to transform the robot end-effector trajectory. Finally a robot joint trajectory is generated by solving a trajectory optimization problem that attempts to find the closest feasible trajectory, accounting for external constraints, such as joint limits and obstacles.Our experiments investigate generalization from a single demonstration to differing initial conditions. A first set of experiments considers the problem of having a simulated Raven II system [5] suture two flaps of tissue together. A second set of experiments considers a PR2 robot performing sutures in a scaled-up experimental setup. The simulation experiments were fully autonomous. For the real-world experiments we provided human input to assist with the detection of landmarks to be fed into the registration algorithm. The success rate for learning from a single demonstration is high for moderate perturbations from the demonstration's initial conditions, and it gradually decreases for larger perturbations.