Improvement of Spatial Resolution with Staggered Arrays As Used in The Airborne Optical Sensor Ads40

Using pushbroom sensors onboard aircrafts or satellites requires, especially for photogrammetric applications, wide image swaths with a high geometric resolution. One approach to satisfy both demands is to use staggered line arrays, which are constructed from two identical CCD lines shifted against each other by half a picel in line direction. Practical applications of such arrays in remote sensing include SPOT, and in the commercial environment the Airborne Digital Sensor, or ADS40, from Leica Geosystems. Theoretically, the usefulness of staggered arrays depends from spatial reslution, which is defined by the total point spread function of the imaging system and Shannon's sampling theorem. Due to the two shifted sensor lines staggering results in a double number of sampling points perpendicular to the flight direction. In order to simultaneously double the sample number in the flight direction, the line readout rate, or integration time, has to produce half a pixel spacing on ground. Staggering in combination with a high-resolution optical system can be used to fulfil the sampling condition, which means that no spectral components above the critical spatial frequency 2/D are present. Theoretically, the resolution is as good for a non-staggered line with half pixel size D/2, but radiometric dynamics should be twice as high. In practice, the slightly different viewing angle of both lines of a staggered array can result in a deteration of image quality due to aircraft motion, attitude fluctuations or terrain undulation. Fulfilling the sampling condition further means that no aliasing occurs. This is essential for the image quality in quasiperiodical textured image areas and for photogrammetric sub-pixel accuracy. Furthermore, image restoration methods for enhancing the image quality can be applied more efficently. The panchromatic resolution of the ADS40 opties is optimised for image collection by a staggered array. This means, it transfers spatial frequencies of twice the Nyquist frequency of its 12k sensors. First experiments, which were carried out some years ago, indicated alrady a spatial resolution improvement by using image restitution the ADS 40 staggered 12k pairs. The results of the restitution algorithm, which is integrated in the ADS image processing flow, has now been analysed quantitatively. This paper presents the theory of high resolution image restitution from staggered lines and practical results with ADS40 high resolution panchromatic images and high resolution colour images, created by sharpening 12k colour images with high resolution pan-chromatic ones

Automated pick-up of suturing needles for robotic surgical assistance

Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis

Local sensory control of a dexterous end effector

A numerical scheme was developed to solve the inverse kinematics for a user-defined manipulator. The scheme was based on a nonlinear least-squares technique which determines the joint variables by minimizing the difference between the target end effector pose and the actual end effector pose. The scheme was adapted to a dexterous hand in which the joints are either prismatic or revolute and the fingers are considered open kinematic chains. Feasible solutions were obtained using a three-fingered dexterous hand. An algorithm to estimate the position and orientation of a pre-grasped object was also developed. The algorithm was based on triangulation using an ideal sensor and a spherical object model. By choosing the object to be a sphere, only the position of the object frame was important. Based on these simplifications, a minimum of three sensors are needed to find the position of a sphere. A two dimensional example to determine the position of a circle coordinate frame using a two-fingered dexterous hand was presented

Parameter identification problems in the modelling of cell motility

We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg–Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data. Our numerical tests allow us to compare the method with the two different formulations of the objective functional and we conclude that the results with both objective functionals seem to agree

DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions. The network estimates not only depth and motion, but additionally surface normals, optical flow between the images and confidence of the matching. A crucial component of the approach is a training loss based on spatial relative differences. Compared to traditional two-frame structure from motion methods, results are more accurate and more robust. In contrast to the popular depth-from-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included. Project page: http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet