There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, position-based and image-based systems, are then discussed in detail. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking. We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.

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A tutorial on visual servo control

A surgical stapling device particularly suited for endoscopic procedures is described The device includes a handle assembly and an elongated body extending distally from the handle assembly The distal end of the elongated body is adapted to engage a disposable loading unit A control rod having a proximal end operatively connected to the handle assembly includes a distal end extending through the elongated body A control rod locking member is provided to prevent movement of the control rod until the disposable loading unit is fully secured to the elongated body of the stapling device

Surgical Stapling Apparatus

A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping community. The focus of the grasp analysis has been on force-closure grasps, which are useful for pick-and-place type tasks. This work discusses the different types of world elements and the general robot definition, and presented the robot library. The paper also describes the user interface of Graspit! and present the collision detection and contact determination system. The grasp analysis and visualization method were also presented that allow a user to evaluate a grasp and compute optimal grasping forces. A brief overview of the dynamic simulation system was provided.

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Graspit! A versatile simulator for robotic grasping

The present invention provides a system and single or multi-functional element device that can be inserted and temporarily placed or implanted into a structure having a lumen or hollow space, such as a subject's abdominal cavity to provide therewith access to the site of interest in connection with minimally invasive surgical procedures. The insertable device may be configured such that the functional elements have various degrees of freedom of movement with respect to orienting the functional elements or elements to provide access to the site from multiple and different orientations/perspectives as the procedure dictates, e.g., to provide multiple selectable views of the site, and may provide a stereoscopic view of the site of interest.

Insertable device and system for minimal access procedure

Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.

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Automatic grasp planning using shape primitives

A survey of research in the area of vision sensor planning is presented. The problem can be summarized as follows: given information about the environment as well as information about the task that the vision system is to accomplish, develop strategies to automatically determine sensor parameter values that achieve this task with a certain degree of satisfaction. With such strategies, sensor parameters values can be selected and can be purposefully changed in order to effectively perform the task at hand. The focus here is on vision sensor planning for the task of robustly detecting object features. For this task, camera and illumination parameters such as position, orientation, and optical settings are determined so that object features are, for example, visible, in focus, within the sensor field of view, magnified as required, and imaged with sufficient contrast. References to, and a brief description of, representative sensing strategies for the tasks of object recognition and scene reconstruction are also presented. For these tasks, sensor configurations are sought that will prove most useful when trying to identify an object or reconstruct a scene. >

/pdf/a-survey-of-sensor-planning-in-computer-vision-4l384q143l.pdf

A survey of sensor planning in computer vision

An attempt to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object is described. The interplay of hand-eye coordination in dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system is explored. The goal is to build an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system built addresses three distinct problems in using robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system operates at approximately human arm movement rates. Experimental results in which a moving model train is tracked, stably grasped, and picked up by the system are presented. The algorithms developed to relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks. >

/pdf/automated-tracking-and-grasping-of-a-moving-object-with-a-4gi8l4udod.pdf

Peter K. Allen

Papers

Graspit! A versatile simulator for robotic grasping

Insertable device and system for minimal access procedure

Automatic grasp planning using shape primitives

A survey of sensor planning in computer vision

Automated tracking and grasping of a moving object with a robotic hand-eye system