Camera Placement Planning Avoiding Occlusion: Test Results Using a Robotic Hand/Eye System

Camera placement experiments are presented that demonstrate the effectiveness of a viewpoint planning algorithm that avoids occlusion of a visual target. A CCD camera mounted on a robot in a hand-eye configuration is placed at planned unobstructed viewpoints to observe a target on a real object. The validity of the method is tested by placing the camera inside the viewing region, that is constructed using the proposed new sensor placement planning algorithm and observing whether the target is truly visible. The accuracy of the boundary of the constructed viewing region is tested by placing the camera at the critical - locations of the viewing region boundary and confirming that the target is barely visible. The corresponding scenes from the candidate viewpoints are shown demonstrating that occlusions are properly avoided

Model-Based Planning of Sensor Placement and Optical Settings

We present a model-based vision system that automatically plans the placement and optical settings of vision sensors in order to meet certain generic task requirements common to most industrial machine vision applications. From the planned viewpoints, features of interest on an object will satisfy particular constraints in the image. In this work, the vision sensor is a CCD camera equipped with a programmable lens (i.e. zoom lens) and the image constraints considered are: visibility, resolution and field of view. The proposed approach uses a geometric model of the object as well as a model of the sensor. in order to reason about the task and the environment The sensor planning system then computes the regions in space as well as the optical settings that satisfy each of the constraints separately. These results are finally combined to generate acceptable viewing locations and optical settings satisfying all constraints simultaneously. Camera planning experiments are described in which a robot-arm positions the camera at a computed location and the planned optical settings are set automatically. The corresponding scenes from the candidate viewpoints are shown demonstrating that the constraints are indeed satisfied. Other constraints, such as depth of focus, as well as other vision sensors can also be considered resulting in a fully integrated sensor planning system

The MVP sensor planning system for robotic vision tasks

The MVP (machine vision planner) model-based sensor planning system for robotic vision is presented. MVP automatically synthesizes desirable camera views of a scene based on geometric models of the environment, optical models of the vision sensors, and models of the task to be achieved. The generic task of feature detectability has been chosen since it is applicable to many robot-controlled vision systems. For such a task, features of interest in the environment are required to simultaneously be visible, inside the field of view, in focus, and magnified as required. In this paper, we present a technique that poses the vision sensor planning problem in an optimization setting and determines viewpoints that satisfy all previous requirements simultaneously and with a margin. In addition, we present experimental results of this technique when applied to a robotic vision system that consists of a camera mounted on a robot manipulator in a hand-eye configuration

Automated sensor planning for robotic vision tasks

A method is presented to determine viewpoints for a robotic vision system for which object features of interest will simultaneously by visible, inside the field-of-view, in-focus, and magnified as required. A technique that poses the problem in an optimization setting in order to determine viewpoints that satisfy all requirements simultaneously and with a margin is presented. The formulation and results of the optimization are shown, as well as experimental results in which a robot vision system is positioned and its lens is set according to this method. Camera views are taken from the computed viewpoints in order to verify that all feature detectability requirements are satisfied

Caveolin-1 protects B6129 mice against Helicobacter pylori gastritis.

Caveolin-1 (Cav1) is a scaffold protein and pathogen receptor in the mucosa of the gastrointestinal tract. Chronic infection of gastric epithelial cells by Helicobacter pylori (H. pylori) is a major risk factor for human gastric cancer (GC) where Cav1 is frequently down-regulated. However, the function of Cav1 in H. pylori infection and pathogenesis of GC remained unknown. We show here that Cav1-deficient mice, infected for 11 months with the CagA-delivery deficient H. pylori strain SS1, developed more severe gastritis and tissue damage, including loss of parietal cells and foveolar hyperplasia, and displayed lower colonisation of the gastric mucosa than wild-type B6129 littermates. Cav1-null mice showed enhanced infiltration of macrophages and B-cells and secretion of chemokines (RANTES) but had reduced levels of CD25+ regulatory T-cells. Cav1-deficient human GC cells (AGS), infected with the CagA-delivery proficient H. pylori strain G27, were more sensitive to CagA-related cytoskeletal stress morphologies ("humming bird") compared to AGS cells stably transfected with Cav1 (AGS/Cav1). Infection of AGS/Cav1 cells triggered the recruitment of p120 RhoGTPase-activating protein/deleted in liver cancer-1 (p120RhoGAP/DLC1) to Cav1 and counteracted CagA-induced cytoskeletal rearrangements. In human GC cell lines (MKN45, N87) and mouse stomach tissue, H. pylori down-regulated endogenous expression of Cav1 independently of CagA. Mechanistically, H. pylori activated sterol-responsive element-binding protein-1 (SREBP1) to repress transcription of the human Cav1 gene from sterol-responsive elements (SREs) in the proximal Cav1 promoter. These data suggested a protective role of Cav1 against H. pylori-induced inflammation and tissue damage. We propose that H. pylori exploits down-regulation of Cav1 to subvert the host's immune response and to promote signalling of its virulence factors in host cells

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Functional Fluorescent Ca(2+) Indicator Proteins in Transgenic Mice under TET Control

Genetically encoded fluorescent calcium indicator proteins (FCIPs) are promising tools to study calcium dynamics in many activity-dependent molecular and cellular processes. Great hopes—for the measurement of population activity, in particular—have therefore been placed on calcium indicators derived from the green fluorescent protein and their expression in (selected) neuronal populations. Calcium transients can rise within milliseconds, making them suitable as reporters of fast neuronal activity. We here report the production of stable transgenic mouse lines with two different functional calcium indicators, inverse pericam and camgaroo-2, under the control of the tetracycline-inducible promoter. Using a variety of in vitro and in vivo assays, we find that stimuli known to increase intracellular calcium concentration (somatically triggered action potentials (APs) and synaptic and sensory stimulation) can cause substantial and rapid changes in FCIP fluorescence of inverse pericam and camgaroo-2

An agent-based model of the response to angioplasty and bare-metal stent deployment in an atherosclerotic blood vessel

Purpose: While animal models are widely used to investigate the development of restenosis in blood vessels following an intervention, computational models offer another means for investigating this phenomenon. A computational model of the response of a treated vessel would allow investigators to assess the effects of altering certain vessel- and stent-related variables. The authors aimed to develop a novel computational model of restenosis development following an angioplasty and bare-metal stent implantation in an atherosclerotic vessel using agent-based modeling techniques. The presented model is intended to demonstrate the body's response to the intervention and to explore how different vessel geometries or stent arrangements may affect restenosis development. Methods: The model was created on a two-dimensional grid space. It utilizes the post-procedural vessel lumen diameter and stent information as its input parameters. The simulation starting point of the model is an atherosclerotic vessel after an angioplasty and stent implantation procedure. The model subsequently generates the final lumen diameter, percent change in lumen cross-sectional area, time to lumen diameter stabilization, and local concentrations of inflammatory cytokines upon simulation completion. Simulation results were directly compared with the results from serial imaging studies and cytokine levels studies in atherosclerotic patients from the relevant literature. Results: The final lumen diameter results were all within one standard deviation of the mean lumen diameters reported in the comparison studies. The overlapping-stent simulations yielded results that matched published trends. The cytokine levels remained within the range of physiological levels throughout the simulations. Conclusion: We developed a novel computational model that successfully simulated the development of restenosis in a blood vessel following an angioplasty and bare-metal stent deployment based on the characteristics of the vessel crosssection and stent. A further development of this model could ultimately be used as a predictive tool to depict patient outcomes and inform treatment options. © 2014 Curtin, Zhou