Retrieving shape information from multiple images of a specularsurface

In many remote sensing and machine vision applications, the shape of a specular surface such as water, glass, or polished metal must be determined instantaneously and under natural lighting conditions. Most image analysis techniques, however, assume surface reflectance properties or lighting conditions that are incompatible with these situations. To retrieve the shape of smooth specular surfaces, a technique known as specular surface stereo was developed. The method analyzes multiple images of a surface and finds a surface shape that results in a set of synthetic images that match the observed ones. An image synthesis model is used to predict image irradiance values as a function of the shape and reflectance properties of the surface, camera geometry, and radiance distribution of the illumination. The specular surface stereo technique was tested by processing four numerical simulations-a water surface illuminated by a low- and high-contrast extended light source, and a mirrored surface illuminated by a low- and high-contrast extended light source. Under these controlled circumstances, the recovered surface shape showed good agreement with the known input     

Adaptive thresholding of texture towards white region normalization

Human colour vision has the ability to recover a measure of contextdependent surface reflectance from observed areas. This can be seen as an analogue to radiometric corrections employed in remote sensing. Procedures based on human colour vision applied in the context of remote sensing could simplify image preprocessing and classification. This study evaluates an algorithm based on Land's colour vision or 'retinex' theory. When tested on remotely sensed images, the algorithm had difficulty coping with the presence of image texture. A new framework is presented that adapts Land's procedure to processing of remotely sensed images using adaptive thresholding techniques.     

A vision system based on a laser range-finder applied to robotic fruit harvesting

This paper describes a laser-based computer vision system used for automatic fruit recognition. It is based on an infrared laser range-finder sensor that provides range and reflectance images and is designed to detect spherical objects in non-structured environments. Image analysis algorithms integrate both range and reflectance information to generate four characteristic primitives which give evidence of the existence of spherical objects. The output of this vision system includes 3D position, radius and surface reflectivity of each spherical object. It has been applied to the AGRIBOT orange harvesting robot, where it has obtained good fruit detection rates and unlikely false detections.     

Design and application of industrial machine vision systems

In this paper, the role and importance of the machine vision systems in the industrial applications are described. First understanding of the vision in terms of a universal concept is explained. System design methodology is discussed and a generic machine vision model is reported. Such a machine includes systems and sub-systems, which of course depend on the type of applications and required tasks. In general, expected functions from a vision machine are the exploitation and imposition of the environmental constraint of a scene, the capturing of the images, analysis of those captured images, recognition of certain objects and features within each image, and the initiation of subsequent actions in order to accept or reject the corresponding objects. After a vision system performs all these stages, the task in hand is almost completed. Here, the sequence and proper functioning of each system and sub-systems in terms of high-quality images is explained. In operation, there is a scene with some constraint, first step for the machine is the image acquisition, pre-processing of image, segmentation, feature extraction, classification, inspection, and finally actuation, which is an interaction with the scene under study. At the end of this report, industrial image vision applications are explained in detail. Such applications include the area of automated visual inspection (AVI), process control, parts identification, and important role in the robotic guidance and control. Vision developments in manufacturing that can result in improvements in the reliability, in the product quality, and enabling technology for a new production process are presented. The key points in design and applications of a machine vision system are also presented. Such considerations can be generally classified into the six different categories such as the scene constraints, image acquisition, image pre-processing, image processing, machine vision justification, and finally the systematic considerations. Each aspect of such processes is described here and the proper condition for an optimal design is reported.     

Generalization of the Lambertian model and implications for machine vision

Lambert's model for diffuse reflection is extensively used in computational vision. It is used explicitly by methods such as shape from shading and photometric stereo, and implicitly by methods such as binocular stereo and motion detection. For several real-world objects, the Lambertian model can prove to be a very inaccurate approximation to the diffuse component. While the brightness of a Lambertian surface is independent of viewing direction, the brightness of a rough diffuse surface increases as the viewer approaches the source direction. A comprehensive model is developed that predicts reflectance from rough diffuse surfaces. The model accounts for complex geometric and radiometric phenomena such as masking, shadowing, and interreflections between points on the surface. Experiments have been conducted on real samples, such as, plaster, clay, sand, and cloth. All these surfaces demonstrate significant deviation from Lambertian behavior. The reflectance measurements obtained are in strong agreement with the reflectance predicted by the proposed model. The paper is concluded with a discussion on the implications of these results for machine vision.     

一种基于光谱知识库的TM影像地物识别方法

地物的波谱特征是识别地物的主要标志之一,在实际应用中发现,由于遥感过程中存在着复杂的大气辐射影响,常常造成光谱知识库中的地物光谱曲线同由遥感图像上获得的光谱曲线不相匹配,进而引起地物识别误差。针对这一问题,从曲线形状分析入手,定义了曲线的形状因子,提出对形状因子构成的曲线进行匹配分析的算法。将该方法应用于TM影像的地物识别,由于形状因子曲线较好地反映了地物的特征信息,它能自动地挖掘遥感图像的细节信息,对地物有较高的识别精度,试验区内总体识别精度达到88.96%。     

A new shadow detection index for 8-band very high-resolution satellite images

ABSTRACT

The new generation of remote sensing satellite with very high-resolution images has provided a high level of details, which make them a reliable source of information. Presence of shadow can reduce the amount of information that can be extracted from these images. Shadow can be confused with dark objects such as water and dark vegetation. The main aim of this research is to develop a new index to detect shadow in the presence of dark objects using the capabilities of the new remote sensing satellite images. For this study, WorldView-2 (WV-2) remote sensing satellite images with eight spectral bands were used. A spectral reflectance analysis for the main ground features has been studied along the eight spectral bands to determine the most effective bands for shadow detection. These bands are employed with the Hue-Saturation-Intensity colour model for producing the new proposed Saturation Intensity Shadow Detection Index (SISDI). The proposed index is applied to four study areas and compared with two state-of-the-art indices of shadow detection. Results of this comparison demonstrate the more accuracy effectiveness and feasibility of that proposed index. The proposed index achieves the highest overall accuracy (average of 97.8%) and has the ability for detecting small shadow areas.     

A CAD Model Based System for Object Recognition

3D object recognition is a difficult and yet an important problem in computer vision. A 3D object recognition system has two major components, namely: an object modeller and a system that performs the matching of stored representations to those derived from the sensed image. The performance of systems wherein the construction of object models is done by training from one or more images of the objects, has not been very satisfactory. Although objects used in a robotic workcell or in assembly processes have been designed using a CAD system, the vision systems used for recognition of these objects are independent of the CAD database. This paper proposes a scheme for interfacing the CAD database of objects and the computer vision processes used for recognising these objects. CAD models of objects are processed to generate vision oriented features that appear in the different views of the object and the same features are extracted from images of the object to identify the object and its pose.     

Multiplexing for optimal lighting

Imaging of objects under variable lighting directions is an important and frequent practice in computer vision, machine vision, and image-based rendering. Methods for such imaging have traditionally used only a single light source per acquired image. They may result in images that are too dark and noisy, e.g., due to the need to avoid saturation of highlights. We introduce an approach that can significantly improve the quality of such images, in which multiple light sources illuminate the object simultaneously from different directions. These illumination-multiplexed frames are then computationally demultiplexed. The approach is useful for imaging dim objects, as well as objects having a specular reflection component. We give the optimal scheme by which lighting should be multiplexed to obtain the highest quality output, for signal-independent noise. The scheme is based on Hadamard codes. The consequences of imperfections such as stray light, saturation, and noisy illumination sources are then studied. In addition, the paper analyzes the implications of shot noise, which is signal-dependent, to Hadamard multiplexing. The approach facilitates practical lighting setups having high directional resolution. This is shown by a setup we devise, which is flexible, scalable, and programmable. We used it to demonstrate the benefit of multiplexing in experiments.     

基于高程或气溶胶厚度与6S模型校正参数回归方程的遥感图像大气校正模型

为了消除影像中的大气影响,反演地表真实反射率,需要进行大气校正。6S是通用的大气校正模型之一,但是对于覆盖面积较广、下垫面较复杂的影像,若用平均的气溶胶厚度和高程参数来对影像进行校正,将出现较大误差。提出了建立一种基于6S模型,以高程或气溶胶厚度为参数的大气校正模型与方法,并以广州Landsat ETM+为例,对校正前后的反射率直方图、NDVI灰度直方图以及各类地物的光谱特征进行分析。结果证明了该方法简单实用,校正精度高,可直接用于该地区相似气候条件下影像的大气校正,对类似的研究有借鉴意义。&     

Robust tracking for augmented reality using retroreflective markers

In this paper, an optical tracking system is introduced for the use in augmented reality (AR). Only a few AR-systems currently exist which are using stereo-vision techniques to estimate the viewing pose. The presented system uses binocular images and retroreflective markers in order to speed up the tracking process and make it more precise and robust. The camera calibration as well as the pose estimation technique is presented. This new optical tracking system, which is based on standard PC hardware, is even suitable to make it portable. In addition, the system is evaluated with regard to its pixel accuracy and depth measurements. This paper shows that the computer vision techniques, which will be presented, are a good choice in order to create a flexible, accurate and easy to use tracking system.     

Automated packing systems-a systems engineering approach

The ability to manipulate previously unseen objects under visual control is one of the key tasks in the successful implementation of robotic, automated assembly and adaptive material handling systems. The automation of such complex industrial environments will require the development of machine vision systems that are highly adaptable and capable of dealing with a wide range of variable products. An important group of applications of this type is found in the automated packing and nesting of arbitrary shapes. The aim of this work has been to produce an efficient packing strategy that is flexible enough for a wide variety of industrial uses and which can be implemented using fast moderately priced hardware. A systems approach, as distinct from a purely algorithmic one, has been deliberately adopted since the work is concerned with industrial vision applications in which significant problem constraints exist. This paper also outlines the background to this research, and reviews a selection of industrial packing applications. The packing procedure that has been devised, consists of two major components. The first is a geometric packing technique that is based on morphological image processing operations. This is used in conjunction with a prolog based heuristic packing procedure. Some of the factors considered at the heuristic level include shape ordering and shape orientation, both of which must be carried out prior to the implementation of the geometric packer. The heuristic procedures deal with problem constraints that are specific to a given application     

Encoded moire inspection based on a computer solid model

An investigation into a system linking a computer solid modeler to an active stereo imaging inspection station is presented. Given the computer solid model of a surface, a projected pattern can be encoded so that the reflections from the object's surface exhibit a uniformly simple pattern when the object's surface and the modeled surface are identical. Research concerning the method of generating projection patterns, as well as the sensitivity of the system, is discussed. A prototype system consisting of a computer graphics workstation; a flexible projector, and a machine vision system was used to inspect two different manufactured parts     

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.     

Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction

We present a method—termed Helmholtz stereopsis—for reconstructing the geometry of objects from a collection of images. Unlike existing methods for surface reconstruction (e.g., stereo vision, structure from motion, photometric stereopsis), Helmholtz stereopsis makes no assumptions about the nature of the bidirectional reflectance distribution functions (BRDFs) of objects. This new method of multinocular stereopsis exploits Helmholtz reciprocity by choosing pairs of light source and camera positions that guarantee that the ratio of the emitted radiance to the incident irradiance is the same for corresponding points in the two images. The method provides direct estimates of both depth and surface normals, and consequently weds the advantages of both conventional stereopsis and photometric stereopsis. Results from our implementation lend empirical support to our technique.     

3D modeling of optically challenging objects

We present a system for constructing 3D models of real-world objects with optically challenging surfaces. The system utilizes a new range imaging concept called multi-peak range imaging, which stores multiple candidates of range measurements for each point on the object surface. The multiple measurements include the erroneous range data caused by various surface properties that are not ideal for structured-light range sensing. False measurements generated by spurious reflections are eliminated by applying a series of constraint tests. The constraint tests based on local surface and local sensor visibility are applied first to individual range images. The constraint tests based on global consistency of coordinates and visibility are then applied to all range images acquired from different viewpoints. We show the effectiveness of our method by constructing 3D models of five different optically challenging objects. To evaluate the performance of the constraint tests and to examine the effects of the parameters used in the constraint tests, we acquired the ground truth data by painting those objects to suppress the surface-related properties that cause difficulties in range sensing. Experimental results indicate that our method significantly improves upon the traditional methods for constructing reliable 3D models of optically challenging objects.     

Parameter nets

This paper describes the nucleus of a connectionist theory of low-level and intermediate-level vision. The theory explains segmentation in terms of massively parallel cooperative computation among intrinsic images and a set of feature networks at different levels of abstraction.Explaining how parts of an image are perceived as a meaningful whole or gestalt is a problem central to vision. A stepping stone towards a solution is recent work showing how to calculate images of physical parameters from intensity data. Such images are known as intrinsic images, and examples are images of velocity (optical flow), surface orientation, occluding contour, and disparity. Intrinsic images show great promise; they are distinctly easier to work with than the original intensity image, but they are not grouped into objects. A general way in which such groupings can be detected is to represent possible groupings as networks whose nodes signify explicit parameter values. In this case the relation between parts of an intrinsic image and the gestalt parameters can be specified by active, two-way connections between an intrinsic image network and a gestalt parameter network. The active connections will be many-to-one onto parameter value nodes that represent object features. The virtues of the methodology are that it can handle occlusion and noise and can specify intrinsic image boundaries. Furthermore, it can be made practical for high-dimensional feature spaces.     

Color Subspaces as Photometric Invariants

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ‘features’ that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent ‘subspaces’ of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, material-based segmentation, and motion estimation.     

Development of an algorithm for fast corner points detection

A method for detecting corner points in digital images is presented. The method is distinguished by high stability and efficiency compared with many method for detecting corner points developed earlier. The stability of corner detection is especially important in computer vision tasks connected with matching images of the same object, recovering digital surface models based on a set of images, and tracking objects. The overwhelming majority of algorithms detect equally well both correct corners and excessive points not corresponding to real corners of objects. The presented algorithm does have this disadvantage, and it can be used in frame-to-frame processing video in real time, e.g. in navigation systems of mobile robots and unmanned aerial vehicles. In addition, the proposed algorithm may be adapted to any data set since it is based on the machine learning method. The advantages of the developed method are demonstrated by an example of detection of corners in images of a typical hangar and in images with the international space station.     

A Novel In situ Recognition of Misalignment between Mating Parts in Robotic Assembly Processes

A visual sensing system is utilized mainly to estimate the misalignment between mating parts, the recognition of which is the integral part of any assembly process. The recognition, however, requires the information on the state of the misalignment that includes the shapes of parts in mating motion and instantaneous relative position and angular orientation between mating parts. Normally, this information has been given in advance by an operator to facilitate assembly action. Therefore, in order to recognize the assembly state in sequence without intervention of an operator, it requires an effective sensing system and algorithm capable of working well even without a priori information on part shape and location. In this paper, we propose a novel system that can assemble parts under such uncertain environments. The system, composed of an omnidirectional sensing module and a recognition module, is capable of acquiring information on the sequential state of parts assembly motion from which instantaneous, relative location and orientation between the mating parts can be determined. Since the system does not utilize a priori knowledge on the shape of mating parts, it greatly reduces the degree of human intervention, thus increasing autonomy and flexibility. To evaluate the performance of the proposed system, a series of assembly experiments are performed. The results show that the proposed system, indeed, demonstrates effectiveness of vision guided assembly action. © 2002 Wiley Periodicals, Inc.