Geometric Calibration of Rotational Vision System for Dynamic Exterior Orientation

Rotational Vision System(RVS)is a common active vision system with only rotational degrees of freedom.Usually,the degree of freedom for rotation is provided by the turntable and pan head.Or the hand to eye(EIH)structure in articulated arm robots.Due to assembly deviations and manufacturing accuracy limitations,the ideal assumption that the rotation axis is fully aligned with the coordinate axis of the local camera is mostly violated.To address this issue,we propose a generalized deviation model that specifies a rotation axis that connects the rotational motion of the platform with the external orientation(EO)of the camera.On this basis,we propose a heuristic estimation algorithm to minimize global reprojection errors and fit circles in space under constraints of global optimization.The experiment shows that the translation and tilt average reprojection errors of dynamic EO reconstruction based on the reprojection error method are 0.14 and 0.08 pixels,respectively.In the absence of angle measurement,the results of the circle fitting method are similar to them(with a relative error of about 2%),meeting the application requirements of general visual measurement.

X-Rite’s ShadeVision System与Vitapan Classical比色板的匹配性分析

目的X-Rite’sShadeVision System和Vitapan Classical比色板的匹配性。方法采用X-Rite’sShadeVision System对Vitapan Classical比色板进行测色。结果X-Rite’s ShadeVision System测量结果排序与原排序一致,排列差异最大有6个色片的差距。相邻比色板的色差ΔEab参差不齐,最大的色差6.92,而最小的色差只有1.21。结论X-Rite’s ShadeVision System与Vitapan Classical比色板不完全匹配。

Global Calibration Method of Multi-sensor Vision System Using Skew Laser Lines

Multi-sensor vision system plays an important role in the 3D measurement of large objects.However,due to the widely distribution of sensors,the problem of lacking common fields of view（FOV） arises frequently,which makes the global calibration of the vision system quite difficult.The primary existing solution relies on large-scale surveying equipments,which is ponderous and inconvenient for field calibrations.In this paper,a global calibration method of multi-sensor vision system is proposed and investigated.The proposed method utilizes pairs of skew laser lines,which are generated by a group of laser pointers,as the calibration objects.Each pair of skew laser lines provides a unique coordinate system in space which can be reconstructed in certain vision sensor＇s coordinates by using a planar pattern.Then the geometries of sensors are computed under rigid transformation constrains by taking coordinates of each skew lines pair as the intermediary.The method is applied on both visual cameras with synthetic data and a real two-camera vision system;results show the validity and good performance.The prime contribution of this paper is taking skew laser lines as the global calibration objects,which makes the method simple and flexible.The method need no expensive equipments and can be used in large-scale calibration.

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.

A global vision system: using hue thresholds to exact feature and recognize

The fast paced nature of robotic soccer necessitates real time sensing coupled with quick behaving and decision making. In the field with real robots, it is important to well perceive the location of ball, team robots and opponent robots through the vision system in real time. In this paper the architecture of global vision system of our small size robotic team and the process of object recognition is described. According to the study on color distribution in different color space and quantitative investigation, a method which uses H (Hue) thresholds as the major thresholds to feature exact and recognize object in real time is presented.

Influence of Data Clouds Fusion From 3D RealTime Vision System on Robotic Group Dead Reckoning in Unknown Terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.The navigation scheme uses the benefits of the original real-time technical vision system(TVS)based on a dynamic triangulation principle.The method uses TVS output data with fuzzy logic rules processing for resolution stabilization.Based on previous researches,the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group.According to the comparative analysis of approximation methods,in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory.The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

A Concept of Dynamically Reconfigurable Real-time Vision System for Autonomous Mobile Robotics

This paper describes specific constraints of vision systems that are dedicated to be embedded in mobile robots. If PC-based hardware architecture is convenient in this field because of its versatility, flexibility, performance, and cost, current real-time operating systems are not completely adapted to long processing with varying duration, and it is often necessary to oversize the system to guarantee fail-safe functioning. Also, interactions with other robotic tasks having more priority are difficult to handle. To answer this problem, we have developed a dynamically reconfigurable vision processing system, based on the innovative features of Cleopatre real-time applicative layer concerning scheduling and fault tolerance. This framework allows to define emergency and optional tasks to ensure a minimal quality of service for the other subsystems of the robot, while allowing to adapt dynamically vision processing chain to an exceptional overlasting vision process or processor overload. Thus, it allows a better cohabitation of several subsystems in a single hardware, and to develop less expensive but safe systems, as they will be designed for the regular case and not rare exceptional ones. Finally, it brings a new way to think and develop vision systems, with pairs of complementary operators.

Calibration of line structured light vision system based on camera’s projective center

Based on the characteristics of line structured light sensor, a speedy method for the calibration was established. With the coplanar reference target, the spacial pose between camera and optical plane can be calibrated by using of the camera’s projective center and the light’s information in the camera’s image surface. Without striction to the movement of the coplanar reference target and assistant adjustment equipment, this calibration method can be implemented. This method has been used and decreased the cost of calibration equipment, simplified the calibration procedure, improved calibration efficiency. Using experiment, the sensor can attain relative accuracy about 0.5%, which indicates the rationality and effectivity of this method.

Research on Night Vision System Based on Range-Gated Imaging

A design of low-light-level night vision system is described,which can image objects selectively in the specific space. The system can selectively image some objects in specific distances,meanwhile ignore those shelters on the way of observation by combining an intensifying charge coupled device(ICCD) with a near infrared laser assisted in vision,whose operation wavelength matches with the photocathode of the image tube,and adopting the gated mode and adjustable time-delay. A semiconductor laser diode of 100 W in peak power is chosen for illumination. The laser and the image tube operate in 150 ns pulse width and 2 kHz repeat frequency. Some images of different objects at the different distances within 100 m can be obtained clearly,and even behind a grove by using a sampling circuit and a delay control device at 100 W in peak power of semiconductor laser diode,150 ns in pulse width of laser and image tube,2 kHz in repeat frequency.

Research on DSO vision positioning technology based on binocular stereo panoramic vision system

In the visual positioning of Unmanned Ground Vehicle(UGV),the visual odometer based on direct sparse method(DSO) has the advantages of small amount of calculation,high real-time performance and high robustness,so it is more widely used than the visual odometer based on feature point method.Ordinary vision sensors have a narrower viewing angle than panoramic vision sensors,and there are fewer road signs in a single frame of image,resulting in poor road sign tracking and positioning capabilities,and severely restricting the development of visual odometry.Based on these considerations,this paper proposes a binocular stereo panoramic vision positioning algorithm based on extended DSO,which can solve these problems well.The experimental results show that the binocular stereo panoramic vision positioning algorithm based on the extended DSO can directly obtain the panoramic depth image around the UGV,which greatly improves the accuracy and robustness of the visual positioning compared with other ordinary visual odometers.It will have widely application prospects in the UGV field in the future.

Developing a Machine Vision System Equipped with UV Light to Predict Fish Freshness Based on Fish-Surface Color

This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) during storage. The APC values were tested and images of the fish surface were taken when fish were stored at room temperature. Then, images’ color-space conversion among RGB, HSV, and L*a*b* color spaces was carried out and analyzed. The results revealed that a* and b* values from the UV-light image decreased linearly during storage. A further regression analysis of these two parameters with APC value demonstrated a good exponential relationship between the a* value and the APC value (R2 = 0.97), followed by the b* (R2 = 0.85). Therefore, our results suggest that the change in color of the fish surface under UV light can be used to assess fish freshness during storage.

Robot Vision System for Coordinate Measurement of Feature Points on Large Scale Automobile Part

In this paper,we present a robot vision based system for coordinate measurement of feature points on large scale automobile parts.Our system consists of an industrial 6-DOF robot mounted with a CCD camera and a PC.The system controls the robot into the area of feature points.The images of measuring feature points are acquired by the camera mounted on the robot.3D positions of the feature points are obtained from a model based pose estimation that applies to the images.The measured positions of all feature points are then transformed to the reference coordinate of feature points whose positions are obtained from the coordinate measuring machine（CMM）.Finally,the point-to-point distances between the measured feature points and the reference feature points are calculated and reported.The results show that the root mean square error（RMSE） of measure values obtained by our system is less than 0.5 mm.Our system is adequate for automobile assembly and can perform faster than conventional methods.

Planar Motion Tracking with an Uncalibrated Robot/Vision System

This paper discusses the coordination process for a robot gripper to approach a movingobject with feedback from an uncalibrated visual system. The dynamic of the whole system, includingtarget's random motion and the gripper's tracking motion, is bounded to a 2-D working plane. Acamera, whose relations with the robot system and the 2-D working plane are unknown to the robotcontroller, is fixed aside to observe the object and gripper positions continually. Thus the movementsof the robot gripper can be decided on the positions of the object observed in each visual samplingmoment. The coordination of the vision and robot system is to be shown independently from therelations between the robot and the vision system, which should always be calibrated a prior forthe control of traditional robot/vision coordination system. Simulations are provided to show theproperty of the proposed method.

Studies on Cold Workability Limits of Brass Using Machine Vision System and its Finite Element Analysis

Cold Workability limits of Brass were studied as a function of friction, aspect ratio and specimen geometry. Five standard shapes of the axis symmetric specimens of cylindrical with aspect ratios 1.0 and 1.5, ring, tapered and flanged were selected for the present investigation. Specimens were deformed in compression between two flat platens to predict the metal flow at room temperature. The longitudinal and oblique cracks were obtained as the two major modes of surface fractures. Cylindrical and ring specimen shows the oblique surface crack while the tapered and flanged shows the longitudinal crack. Machine Vision system using PC based video recording with a CCD camera was used to analyze the deformation of 4 X 4 mm square grid marked at mid plane of the specimen. The strain paths obtained from different specimens exhibited nonlinearity from the beginning to the end of the strain path. The circumferential stress component Os increasingly becomes tensile with continued deformation. On the other hand the axial stress Oz , increased in the very initial stages of deformation but started becoming less compressive immediately as barreling develops. The nature of hydrostatic stress on the rim of the flanged specimen was found to be tensile. Finite element software ANSYS has been applied for the analysis of the upset forming process. When the stress values obtained from finite element analysis were compared to the measurements of grids using Machine Vision system it was found that they were in close proximity.

A MACHINE VISION SYSTEM FOR INSPECTING WOOD SURFACE DEFECTS BY USING NEURAL NETWORK

With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable 'defecs' that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.

Structured scene modeling using micro stereo vision system with large field of view

This paper presents a method for structured scene modeling using micro stereo vision system with large field of view. The proposed algorithm includes edge detection with Canny detector, line fitting with principle axis based approach, finding corresponding lines using feature based matching method, and 3D line depth computation.

A Machine Vision System for Ball Grid Array Package Inspection

An optical inspection method of the Ball Grid Array package(BGA) was proposed by using a machine vision system. The developed machine vision system could get main critical factors for BGA quality evaluation, such as the height of solder ball, diameter, pitch and coplanarity. The experiment has proved that this system is available for BGA failure detection.

A Programmable High Speed Vision System with Superscalar PE and Its Parallel Computing Language

Pixel-parallel PE and SIMD architectures are widely used in high-speed image processing to enhance computing power. With fully exploiting the data level parallelism of low- and middle-level image processing, SIMD architecture is able to finish great amount of computation with much less instruction cycle thus satisfy the high-speed system requirement. The main computation parts in those SIMD image processing hardware is known as PE (processing element) and it is responsible for transferring, storing and processing the image data. This paper describes a high-speed vision system with superscalar PE to enhance system performance and its dedicated parallel computing language specifically devel-oped for this vision system. The vision system can achieve motion detection at more than 2000fps and face detection at more than 100 fps which overwhelms some general serial CPUs in the same applications.

Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?

Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.

Recent advances in bioinspired vision systems with curved imaging structures

Limited by the planar imaging structure,the commercial camera needs to introduce additional optical elements to compensate for the curved focal plane to match the planar image sensor.This results in a complex and bulky structure.In contrast,biological eyes possess a simple and compact structure due to their curved imaging structure that can directly match with the curved focal plane.Inspired by the structures and functions of biological eyes,curved vision systems not only improve the image quality,but also offer a variety of advanced functions.Here,we review the recent advances in bioinspired vision systems with curved imaging structures.Specifically,we focus on their applications in implementing different functions of biological eyes,as well as the emerging curved neuromorphic imaging systems that incorporate bioinspired optical and neuromorphic processing technologies.In addition,the challenges and opportunities of bioinspired curved imaging systems are also discussed.