Range Image Segmentation

An assignment on development of Cellular mobile communication Generations, Namely, From 0 G to 5 G. Including a briefing on GSM, EDGE, AMPS, GPRS, And all others categories related to this field. This is totally a briefing that was just created in 15 min. ;)

This paper addresses the problem of parametric representation and estimation of complex planar curves in 2-D, surfaces in 3-D and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, and we use the latter representation. A planar curve is the set of zeros of a smooth function of two variables X-Y, a surface is the set of zeros of a smooth function of three variables X-~-Z, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables X-!/-Z. For example, the surface of a complex object in 3-D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. We show how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of "interest regions" that are more complex than high curvature regions and, hence, more useful as features for object recognition. Fitting implicit curves and surfaces to data would be ideally based on minimizing the mean square distance from the data points to the curve or surface. Since the distance from a point to a curve or surface cannot be computed exactly by direct methods, the approximate distance, which is a first-order approximation of the real distance, is introduced, generalizing and unifying previous results. We fit implicit curves and surfaces to data minimizing the approximate mean square distance, which is a nonlinear least squares problem. We show that in certain cases, this problem reduces to the generalized eigenvector fit, which is the minimization of the sum of squares of the values of the functions that define the curves or surfaces under a quadratic constraint function of the data. This fit is computationally reasonable to compute, is readily parallelizable, and, hence, is easily computed in real time. In general, the generalized eigenvector lb provides a very good initial estimate for the iterative minimization of the approximate mean square distance. Although we are primarily interested in the 2-D and 3-D cases, the methods developed herein are dimension independent. We show that in the case of algebraic curves and surfaces, i.e., those defined by sets of zeros of polynomials, the minimizers of the approximate mean square distance and the generalized eigenvector fit are invariant with respect to similarity transformations. Thus, the generalized eigenvector lit is independent of the choice of coordinate system, which is a very desirable property for object recognition, position estimation, and the stereo matching problem. Finally, as applications of the previous techniques, we illustrate the concept of "interest regions"

We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional information (formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. Visit http:// www.theiet.org/resources/author-hub/books/index.cfm to get all information you need as a contributor to an IET research-level book. Each book is expected to have a total number of 500 printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send me the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new results for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration.

Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: local reference frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100, 98.9, 95.4 and 96.0 % respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari Venezia Datasets.

The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z]E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

We present in this paper a multi-agent approach for range image segmentation. The approach consists in using autonomous agents for the segmentation of a range image in its different planar regions. Agents move on the image and perform local actions on the pixels, allowing robust region extraction and accurate edge detection. In order to improve the segmentation quality, a Bayesian edge regularization is applied to the resulting edges. A new Markov Random Field (MRF) model is introduced to model the edge smoothness, used as a prior in the edge regularization. The experimental results obtained with real images from the ABW database show a good potential of the proposed approach for range image analysis, regarding both segmentation efficiency, and detection accuracy.

The use of close-range, three-dimensional laser scanning for the documentation of ancient Mesoamerican sculpted artifacts (e.g., stone, ceramic, stucco, wood) provides significant advantages in issues of conservation, research, analysis, and dissemination. A series of case studies will illustrate how scan data have been used as a core element in the development of expandable, research databases that offer substantial benefits and advancements to archaeological investigations. Results from this approach have substantially expanded opportunities for international study and collaborative research initiatives. Innovative analytical techniques for the examination and interpretation of Mesoamerican sculpture, the development of research and educational resources, the recording of metadata, and methods of electronic dissemination are discussed.

IET indexes its books and journal in SCOPUS and IEEE Xplore.
Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.

VOLUME 2-DEPLOYMENT AND APPLICATIONS: This tome introduces procedures, standards, and prerequisites for the deployment of Computer Vision (CV) in UAVs from their application point of view. It discusses existing/desirable open source software tools, image banks, benchmarks, Quality of Experience (QoE), Quality of Service (QoS) and how CV can benefit from a Robot Operating System (ROS) in surveillance, remote sensing, inspection, maintenance and repair among other usages, while offering an assessment of current bottlenecks and trends. It will pave the road towards better studies on the necessity and viability of implementing collaborative environments for visualization, knowledge management and teleoperation of UAVs. This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 1-Control and Performance.

Editor(s):
Dr. Vania V. Estrela, https://www.linkedin.com/in/vania-v-estrela-96b9bb29/
Universidade Federal Fluminense (UFF), RJ, Brazil
[email protected]

Dr. Jude Hemanth,https://www.karunya.edu/ece/drjude.html
Karunya University, Coimbatore, India
[email protected]

Dr. Osamu Saotome, https://www.linkedin.com/in/osamu-saotome-83935818
Instituto Tecnológico de Aeronáutica, CTA-ITA-IEEA, São José dos Campos, SP, Brazil
[email protected]

CONTENTS:
1. Image Acquisition and Restoration in UAVs
2. Image Fusion in UAVs
3. Super-Resolution Imaging in UAVs
4. 2D/3D/4D Imaging in UAVs
5. Multi-view Image and ToF Sensor Fusion in UAVs
6. Range Imaging in UAVs
7. Multispectral and Hyperspectral Imaging in UAVs
8. Imaging Standards and UAVs
9. Virtual/Augmented Reality in UAVs
10. Collaborative Environments in UAVs
11. Archiving, Storage, and Compression in UAVs
12. Analysis, Indexing, Retrieval in UAVs
13. Multicast/Broadcast/Streaming in UAVs
14. Modelling, Simulation and UAVs
15.  Image-Oriented Estimation and Identification in UAVs
16. Open Source Software in UAVs
17. Image Banks and Benchmarks in UAVs
18. Quality of Experience (QoE) and Quality of Service (QoS) in UAVs
19. Robot Operating System (ROS) in UAVs
20. Cloud Computing in UAVs

Specification and Schedule

July 1st, 2017: Call for Chapter Abstracts
September 1, 2017: One-Page Chapter Abstract (up to 1000 words) Submission Deadline. Free style.
          A proposal must outline one of topics fromthe list above (mention its number, for instance 1. and reference PBCE120B).
November 30, 2017: Last Day for Notification of Acceptance
Jan 30, 2018: Full Chapter Submissions
March 30 2018: Review Chapter Submissions and send comments to authors
May 31, 2018: Receive revised Chapter Submissions
June 30, 2018: Notification of Final Acceptance
July 31, 2018: Gather all material, figure files and copyrights permission forms
Aug 30, 2018: Book editors to finalize introduction and conclusion chapters
Sept 15th, 2018, Delivery of full manuscript to the IET
Scheduled publication: Feb/March 2019

Readership: Graduate students and Researchers in the fields of Electrical and Computer Engineering, Computer Science, Mechanical Engineering, Civil Engineering, Humanitarian Engineering, Control Systems, Geoscience and Remote Sensing, Instrumentation and Measurement, Intelligent Transportation Systems, Oceanic Engineering, Safety Engineering, Reliability, Robotics and Automation, Signal Processing, Technology and Engineering Management, Environmental Engineering, Public Health Management, Non-Invasive Testing/Monitoring and Vehicular Technology.

Additional Information: Dr. Vania V. Estrela,    [email protected]
                                          Dr. Jude Hemanth,      [email protected]

The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.

Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of the 3D range images. This 3D gradient detector is a generalization of the classical sobel operator used with 2D images, which is based on the differences of normal vectors or geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure method to handle large amount of unordered sets of points and determine neighborhood points. It segments the 3D range images directly using gradient edge detectors without any further computations like mesh generation. Our algorithm focuses on extracting important linear structures such as doors, stairs and windows from terrestrial 3D range images these structures are common in indoors and outdoors in many environments. Results: Experimental results showed that the proposed algorithm provides a new approach of 3D range image segmentation with the characteristics of low computational complexity and less sensitivity to noise. The algorithm is validated using seven artificially generated datasets and two real world datasets. Conclusion/Recommendations: Experimental results showed that different segmentation accuracy is achieved by using higher Grid resolution and adaptive threshold.

Pedestrian detection is essential to avoid dangerous traffic situations. In this paper, we present a fast and robust algorithm for detecting pedestrians in a cluttered scene from a pair of moving cameras. This is achieved through stereo-based segmentation and neural network-based recognition. The algorithm includes three steps. First, we segment the image into sub-image object candidates using disparities discontinuity. Second, we merge and split the sub-image object candidates into sub-images that satisfy pedestrian size and shape constrains. Third, we use intensity gradients of the candidate sub-images as input to a trained neural network for pedestrian recognition. The experiments on a large number of urban street scenes demonstrate that the proposed algorithm: 1) can detect pedestrians in various poses, shapes, sizes, clothing, and occlusion status; 2) runs in real-time; and 3) is robust to illumination and background changes.

Due to the wide use and key importance of mathematical models in process engineering, applications of experiment design are becoming essential as a way of turning their realization viable and speed up development time. Several experiment design techniques have been developed in the past and applied successfully to a wide range of systems. In order to provide an adequate background to this technique, a detailed description of the key elements of a model-oriented procedure (the model itself, the experiment, the statistical tools, etc.) and the major steps of a model-building strategy are introduced before focusing on the experiment design for parameter precision. An overview and critical analysis of the state of the art in this sector is done.

In this study, the potential of remote sensing in tropical forests is examined in relation to the diversification of sensors. We report here on the comparison of alternative methods that use multisource data from Airborne Laser Scanning (ALS), Airborne CIR and ALOS AVNIR-2 to estimate stem volume and basal area, in Laos. Multivariate linear regression analyses with stepwise selection of predictors were implemented for modelling. The predictors of ALS metrics were calculated by means of the canopy height distribution approach, while predictors from both spectral and textual features were respectively generated for Airborne CIR and ALOS AVNIR-2 data. With respect to the estimation capacity from individual data sources after leave-one-out cross-validation, the ALS data proved superior, with the lowest RMSE of 36.92% for stem volume and 47.35% for basal area, whereas Airborne CIR and ALOS AVNIR-2 remained at similar accuracy levels, but fell well behind the ALS data. By integrating ALS metrics with other predictors from Airborne CIR or ALOS AVNIR-2, hybrid modelling was further tested respectively. The results showed that only the hybrid model for stem volume involving ALS and Airborne CIR improved the accuracy of 1.9% in terms of relative RMSE than that of using ALS alone.

If a person with carious lesions needs or requests crowns or inlays, these dental fillings have to be manufactured for each tooth and each person individually. We survey computer vision techniques which can be used to automate this process. We introduce three particular applications which are concerned with the reconstruction of surface information. The first one aims at building up a database of normalized depth images of posterior teeth and at extracting characteristic features from these images. In the second application, a given occlusal surface of a posterior tooth with a prepared cavity is digitally reconstructed using an intact model tooth from a given database. The calculated surface data can then be used for automatic milling of a dental prosthesis, e.g. from a preshaped ceramic block. In the third application a handmade provisoric wax inlay or crown can be digitally scanned by a laser sensor and copied three dimensionally into a different material such as ceramic. The results are converted to a format required by the computer-integrated manufacturing (CIM) system for automatic milling.

A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (a) a common set of 40 laser range nder images and 40 structured light scanner images that have manually speci ed ground truth and (b) a set of de ned performance metrics for instances of correctly segmented, missed and noise regions, over-and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the speci ed ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.

ABSTRACT An algorithm for fast automatic acquisition of virtual models of planar-faced textured objects from registered range and colour images is introduced. The algorithm consists of four main steps-range image segmentation, texture analysis, virtual model geometry inference and texture synthesis. An efficient and robust range image segmentation is based on the combination of range profile modelling and line-based region growing. The texture is modelled using a multi-resolution Markov random field based model

This paper focuses on the experimental evaluation of a range image segmentation system which partitions range data into homogeneous surface patches using estimates of the sign of the mean and Gaussian curvatures. We report the results of an extensive testing programme aimed at investigating the behavior of important experimental parameters such as the probability of correct classi cation and the accuracy of curvature estimates, measured over variations of signi cant segmentation variables. Evaluation methods in computer vision are often unstructured and subjective: this paper contributes a useful example of extensive experimental assessment of surface-based range segmentation.

Automatic acquisition of CAD models from existing objects requires accurate extraction of geometric and topological information from the input data. This paper presents a range image segmentation method based on local approximation of scan lines. The method employs edge models that are capable of detecting noise pixels as well as position and orientation discontinuities of varying strengths. Region-based techniques are then used to achieve a complete segmentation. Finally, a geometric representation of the scene, in the form of a surface CAD model, is produced. Experimental results on a large number of real range images acquired by different range sensors demonstrate the efficiency and robustness of the method.

We present in this paper a multi-agent approach for range image segmentation. The approach consists in using autonomous agents for the segmentation of a range image in its different planar regions. Agents move on the image and perform local actions on the pixels, allowing robust region extraction and accurate edge detection. In order to improve the segmentation quality, a Bayesian edge regularization is applied to the resulting edges. A new Markov Random Field (MRF) model is introduced to model the edge smoothness, used as a prior in the edge regularization. The experimental results obtained with real images from the ABW database show a good potential of the proposed approach for range image analysis, regarding both segmentation efficiency, and detection accuracy.

A new type of range image segmentation method is introduced. The image segmentation is based on a recursive adaptive regression model prediction for detecting range image step discontinuities which are present at object face borders. Border pixels are detected in two perpendicular directions and detection results are combined together. Two predictors in each direction use identical contextual information from the pixel's neighbourhood and they mutually compete for the most optimal discontinuity detection. The method suggested can be successfully applied also to other image segmentation applications, e.g. panchromatic or multispectral image data, etc.

In this study, the potential of remote sensing in tropical forests is examined in relation to the diversification of sensors. We report here on the comparison of alternative methods that use multisource data from Airborne Laser Scanning (ALS), Airborne CIR and ALOS AVNIR-2 to estimate stem volume and basal area, in Laos. Multivariate linear regression analyses with stepwise selection of predictors were implemented for modelling. The predictors of ALS metrics were calculated by means of the canopy height distribution approach, while predictors from both spectral and textual features were respectively generated for Airborne CIR and ALOS AVNIR-2 data. With respect to the estimation capacity from individual data sources after leave-one-out cross-validation, the ALS data proved superior, with the lowest RMSE of 36.92% for stem volume and 47.35% for basal area, whereas Airborne CIR and ALOS AVNIR-2 remained at similar accuracy levels, but fell well behind the ALS data. By integrating ALS metrics with other predictors from Airborne CIR or ALOS AVNIR-2, hybrid modelling was further tested respectively. The results showed that only the hybrid model for stem volume involving ALS and Airborne CIR improved the accuracy of 1.9% in terms of relative RMSE than that of using ALS alone. Ó

Automatic extraction of CAD descriptions which are ultimately intended for human manipulation requires the accurate inference of geometric and topological information. We present a system which applies segmentation techniques from computer vision to automatically extract CAD models from range images of parts with curved surfaces. The output of the system is a B-rep of the object which is suitable for further manipulation in a modelling system.

ABSTRACT A fast range image segmentation method for scenes comprising general faced objects is introduced. The range segmentation is based on a recursive adaptive probabilistic detection of step discontinuities which are present at object face borders in mutually registered range and intensity data. Detected face outlines guides the subsequent region growing step where the neighbouring face curves are grouped together. Region growing based on curve segments instead of pixels like in the classical approaches considerably speed up the algorithm. The exploitation of multimodal data significantly improves the segmentation quality

This paper focuses on a fast and efiective model for range images segmentation and modeling. The flrst phase is based on the well-known Simoncelli’s steerable pyramid, useful to distinguish image information from noise. Gradient modulus and phase information is then exploited for achieving edges characterizing objects. Modeling is faced through superquadrics recovery. In this case a fast and simple procedure to estimate their free parameters is proposed. Achieved results on simple objects show that our model is simple, fast and robust to noise.

We present a new range image segmentation algorithm based on the extraction of planar and general quadric surfaces, which is of great relevance in man-made environments, built largely of low-order surfaces. We describe how our robust estimator can effectively reject erroneous surface hypotheses, while identifying points describing a general quadric surface, by means of an improved approximation to the true geometric distance between each point and the surface. We present thorough experimental results with quantitative evaluation against ground truth.

This work focuses on creating a framework for objectively evaluating the performance of range image segmentation algorithms. The algorithms are evaluated in terms of correct segmentation, over-and under-segmentation, missed and noise regions. A set of images with ground truth was created for this work. The images were captured using a structured light scanner. Images used in the evaluation contain planar, spherical, cylindrical, toroidal and conical surface patches. The di erent surface patches in each image were manually identi ed to establish ground truth for performance evaluation. Two segmentation algorithms from the literature are compared.

Airborne laser scanner is an integrated system of GPS, INS and laser scanner. It has become an accurate, fast, and versatile measurement technique that can complement or partly replace other existing acquisition technologies and has been used in more diverse applications that concern large-scale and precise topographic Digital Elevation Models (DEMs) and Digital Surface Models (DSMs). Up to now, development of algorithm and methods for acquiring 3D spatial data, interpretation and modeling of this kind data for property application are main research topics. This paper demonstrates an experiment on generation of 3D virtual geographic environment by using experimental flight laser scanning data, based on a set of algorithms and methods that were developed for automatically interpret range images for extracting geo-spatial features to reconstruct geo-objects. The set of algorithms and methods for interpretation and modeling of laser scanner data include the followings: Triangulated Irregular Network (TIN) based range image interpolation; Mathematical Morphology (MM) based range image filtering, feature extraction and range image segmentation, feature generalization and optimization; 3D objects reconstruction and modeling; Computer Graphics (CG) based visualization and animation of geographic virtual reality environment. The results of the experiment of this research demonstrate the operational utilization of the suggested approach and its applicability to various fields of study, such as urban disaster management.

This report presents an experimental evaluation of a plane extraction method using various line extraction algorithms. Four different algorithms are chosen, which are well known in mobile robotics and computer vision. Experiments are performed on two sets of 25 range images either obtained by simulation or acquired by a proprietary 3D laser scanner. The segmentation outcome of the simulated range images is measured in terms of an average segment classification ratio. Moreover, the speed of the method is measured to conclude on the suitability for service robot applications. (a) (b) Figure 1: Range Image Sensor and its Coordinate System. On the left side is depicted the coordinate system of the range image in 3D. On the right side is depicted the 3D range image sensor-a pivoting 2D laser scanner. A measurement point P of the range image is specified in spherical coordinate system (r, θ, ϕ). The range image is the composition of range scans at different elevation angles β. The range scans in 2D are specified in the polar coordinate system (r, α).