Shape Detection Research Articles

Position Sensitive Detectors with Particle-Type Identification for Recording Images of Neutron and Gamma Sources in Mixed Fields

The design and characteristics of position sensitive detectors implemented in the form of a matrix of individual detectors (modules) with selective registration of electrons and protons are examined. It is proposed that such detectors be used as a basis for considering the possibility of building introscopes with a coded aperture for detecting images of sources of mixed fast-neutron and γ-radiation. Scintillation detectors with digital identification of neutrons and γ-rays according to the pulse shape or Cherenkov detectors registering only electrons (high-energy protons) are used as modular detectors. The registration of analogue pulses of detectors and their conversion into a digital form and subsequent digital processing are performed simultaneously, in parallel, and independently. This is optimal for increasing speed of operation and the loading of the modular scintillation detector to 106 counts/sec in the energy range 0.2–15 MeV for neutron radiation, 0.015–7 MeV for γ-radiation, and to 2∙108 counts/sec for Cherenkov radiation.

Current status of ultra-precision manufacturing of complex curved aluminum reflectors

Due to the unique advantages of complex curved aluminum mirrors, its application in optical systems is becoming more and more widespread. However, the accuracy of optical mirrors that are only processed by ultra-precision turning is limited by the error reflection of ultra-precision turning, which can only meet the application requirements of infrared systems, and its further promotion and application have encountered bottlenecks. The combined processing technology of ultra-precision turning, magnetorheological polishing, and computer-controlled surface forming (CCOS), combined with the computational hologram method (CGH) of the complex optical curved surface (CGH) surface shape detection technology, can further improve the surface shape accuracy of the aluminum reflector, to meet the application requirements of visible light systems, and lay the foundation for the promotion and application of complex curved aluminum alloy mirrors.

DETECTION OF INDOOR ATTACHED EQUIPMENT FROM TLS POINT CLOUDS USING PLANAR REGION BOUNDARY

Abstract. Laser scanning technology is useful to create accurate three-dimensional models of indoor environments for applications such as maintenance, inspection, renovation, and simulations. In this paper, a detection method of indoor attached equipment such as windows, lightings, and fire alarms, from TLS point clouds, is proposed. In order to make the method robust against to the lack of points of equipment surface, a footprint of the equipment is used for detection, because the entire or a part of the footprint boundary shapes explicitly appear as the boundary of base surfaces, i.e. walls for windows, and ceilings for lightings and fire alarms. In the method, first, base surface regions are extracted from given TLS point clouds of indoor environments. Then, footprint boundary points are detected from the region boundary points. Finally, target equipment is detected by fitting or voting using given target footprint shapes. The features of our method are footprint boundary point extraction considering occlusions, shape fitting with adaptive parameters based on point intervals, and robust shape detection by voting from multiple footprint boundary candidates. The effectiveness of the proposed method is evaluated using TLS point clouds.

An adaptive process of reverse engineering from point clouds to CAD models

ABSTRACT In the field of industrial design, most manufactured objects are designed using 3D CAD (Computer-Aided Design) model. However, the original design may sometimes be lost or not available. A reverse engineering approach is then required to generate a geometric CAD model from 3D point clouds obtained by scanning objects. In this paper, an adaptive process for automatic reconstruction of CAD model from point clouds is proposed. First, the approach extracts primitive shapes from point clouds by RANSAC algorithm, then it analyses deviations of points from the fitted primitive shapes by histograms. For point cloud patches segmented unreasonable, the approach updates parameters of segmentation according to the Gaussian noise and repeats the primitive shape detection process. After certain rounds of iteration, the approach can detect reasonable primitive shapes from point clouds. Then a rule for obtaining CAD models by primitive shape alignment is introduced. The proposed approach is evaluated on datasets provided by the AIM@SHAPE Shape Repository.

Apple Shape Detection Based on Geometric and Radiometric Features Using a LiDAR Laser Scanner

Yield monitoring systems in fruit production mostly rely on color features, making the discrimination of fruits challenging due to varying light conditions. The implementation of geometric and radiometric features in three-dimensional space (3D) analysis can alleviate such difficulties improving the fruit detection. In this study, a light detection and range (LiDAR) system was used to scan apple trees before (TL) and after defoliation (TD) four times during seasonal tree growth. An apple detection method based on calibrated apparent backscattered reflectance intensity (RToF) and geometric features, capturing linearity (L) and curvature (C) derived from the LiDAR 3D point cloud, is proposed. The iterative discretion of apple class from leaves and woody parts was obtained at RToF > 76.1%, L < 15.5%, and C > 73.2%. The position of fruit centers in TL and in TD was compared, showing a root mean square error (RMSE) of 5.7%. The diameter of apples estimated from the foliated trees was related to the reference values based on the perimeter of the fruits, revealing an adjusted coefficient of determination (R2adj) of 0.95 and RMSE of 9.5% at DAFB120. When comparing the results obtained on foliated and defoliated tree’s data, the estimated number of fruit’s on foliated trees at DAFB42, DAFB70, DAFB104, and DAFB120 88.6%, 85.4%, 88.5%, and 94.8% of the ground truth values, respectively. The algorithm resulted in maximum values of 88.2% precision, 91.0% recall, and 89.5 F1 score at DAFB120. The results point to the high capacity of LiDAR variables [RToF, C, L] to localize fruit and estimate its size by means of remote sensing.

PVANET-HOUGH: DETECTION AND LOCATION OF CENTER PIVOT IRRIGATION SYSTEMS FROM SENTINEL-2 IMAGES

Abstract. Irrigation systems play an important role in agriculture. As being labor-saving and water consumption efficient, center pivot irrigation systems are popular in many countries. Monitoring the distribution of center pivot irrigation systems can provide important information for agriculture production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems, PVANET-Hough, is proposed. The proposed method combines a lightweight real-time object detection network PVANET based on deep learning and accurate shape detection Hough transform to detect and accurately locate center pivot irrigation systems. The method proposed in this paper does not need any preprocessing, PVANET is lightweight and fast, Hough transform can accurately detect the shape of center pivot irrigation systems, and reduce the false alarms of PVANET at the mean time. Experiments with the Sentinel-2 images in Mato Grosso demonstrated the effectiveness of the proposed method.

Monotonicity in inverse obstacle scattering on unbounded domains

We consider an inverse obstacle scattering problem for the Helmholtz equation with obstacles that carry mixed Dirichlet and Neumann boundary conditions. We discuss far field operators that map superpositions of plane wave incident fields to far field patterns of scattered waves, and we derive monotonicity relations for the eigenvalues of suitable modifications of these operators. These monotonicity relations are then used to establish a novel characterization of the support of mixed obstacles in terms of the corresponding far field operators. We apply this characterization in reconstruction schemes for shape detection and object classification, and we present numerical results to illustrate our theoretical findings.

The Use of a Stereovision System in Shape Detection of the Side Surface of the Body of the Mining Machine Working Unit

Abstract Ensuring the compliance of the finished product with the project during the manufacturing of cutting heads/drums of the mining machines, largely determines the efficiency of rock mining, especially hard-to-cut rocks. The manufacturing process of these crucial elements of cutting machines is being robotized in order to ensure high accuracy and repeatability. This determines, among others the need to assess in real-time the degree of the approach of pick holders positioned by the industrial robot to the side surface of the working unit of the cutting machine in their target position. This problem is particularly important when in the manufacturing process are used the bodies of decommissioned cutting heads/drums, from which old pick holders have been removed. The shape and external dimensions of these hulls, unless they are subjected to regeneration, may differ quite significantly from the nominal ones. The publication, on the example of a road header cutting head, presents the procedure for automatically identifying and indexing markers displayed on its side surface, recorded on measuring photos by two digital cameras of a 3D vision system. Experimental research of the developed method was carried out using the KUKA VisionTech vision system installed on the test stand in the robotics laboratory of the Department of Mining Mechanization and Robotization at the Faculty of Mining, Safety Engineering and Industrial Automation of the Silesian University of Technology. Data processing was carried out in the Matlab environment using the libraries of the Image Processing Toolbox. The functions provided in this library were used in the developed algorithm, implemented in the software. This algorithm allows automatic identification of markers located in the images of the side surface of the cutting head. This is the basis for determining their location in space. The publication presents a method of segmenting images recorded by cameras into homogeneous areas. The method of separating interesting areas from the image by comparison to the pattern was presented. Also shown is the method of the automatic numbering of mutually matching pairs of markers on photos from two cameras included in the vision system depending on the spatial orientation of the marker grid in the measuring images.

An Approach to the Computation of the Euler Number by means of the Vertex Chain Code

We present an approach to compute the number of holes in binary images using the Vertex Chain Code (VCC); the VCC was developed for representing and analyzing 2D shapes composed of cells. Using this code, it is possible to relate the outer to inner vertices of any 2D shape and to find interesting properties. Now, in this paper, we describe more properties of the VCC, such as the computation of the connected regions in a hole, the analysis of complementary chains, the computation of the number of holes in a binary shape or image, the computation of the Euler number, and the detection of convex and concave shapes. Finally, in order to illustrate the capabilities of proposed methods, we present the computation of topological properties of examples of objects of the real world.

Shape detection of multiple subsurface cavities by particle filtering with elastic wave propagation

SummaryA numerical technique for detecting the number and shape of subsurface cavities is presented, applying the particle filter and the parametric level set method to elastic wave propagation under the ground. When subsurface cavities exist, the elastic wave propagating in the ground is reflected at the boundary faces of the cavities. Observing the velocity of the reflection wave at the surface of a ground that includes multiple cavities and parameterizing the shape of the cavities by the parametric level function, both the number and the shape of the cavities can be identified by the particle filter. Numerical experimentation for detecting multiple cavities is conducted with synthetic observation data. The results show that the proposed technique enables the number of cavities to be identified by the number of peaks in the posterior probabilistic distribution function and solves geometric inverse problems by estimating the shape of the cavities through the parameter identification of the level set function.

Development of a variable-stiffness and shape-detection manipulator based on low-melting-point-alloy for minimally invasive surgery.

There is an increasingly popularity for the continuum robot in minimally invasive surgery(MIS), because of the compliance and dexterity. In the first place, a variable stiffness manipulator can resolve the two contradictions of the demands for predominant flexibility and strong payload capacity. In the second place, to control the continuum robot more precisely and avoid the collision between robot and human body, real-time tracking of the shape of the continuum robot is of great significance. A new type of flexible manipulator with variable stiffness is proposed which can track the bending shape timely. The low-melting-point-alloy (LMPA) is used to realize the variable stiffness and shape detection for the flexible manipulator. The concept design for a single module is put forward. Then the stiffness control method and finite element simulation, the method of shape detection are presented. Moreover, the presented method of shape detection is evaluated by experiments.

Icing detection and evaluation of the electro-impulse de-icing system based on infrared images processing

The aircraft icing poses a significant hazard to aircraft safety and it is important to remove the ice in time. The electro-impulse de-icing (EIDI) system is a mechanical de-icing method, which prevents ice accumulation on that part of the aircraft where ice is more likely to form. Since its discovery research on the theory and application of the EIDI system continued to progress. In this paper, the formation of ice is detected using infrared thermography of the heat signal generated on the aircraft skin surface. Ice accretion specimen are produced to explore the effects of seven critical parameters. The performance of the EIDI system is evaluated using the infrared thermal wave test. The infrared thermography sequence of the ice accretion specimen is acquired by the infrared camera during de-icing operation. The ice detection of the EIDI system employs a method that achieves the original icing detection and icing damage detection based on infrared image processing, including principal component analysis (PCA) and Canny edge detection. The measurement of the original icing on center and radius is achieved by the three-point method, which may be further employed to evaluate the icing manufacturing error. An icing damage detection method is also designed and applied. An index based on the infrared thermal image processing results is introduced to evaluate the performance of the EIDI system under different experimental conditions. The quantitative behaviour of de-icing performance in terms of seven important parameters involved in manufacturing the ice has been verified and discussed. The results show that PCA and Canny edge detection may be effectively exploited to implement ice shape detection and, in turn, to improve the design of the EIDI system.

Analysis of Seismic Methods Used for Subsea Hydrocarbon Exploration

Subsea hydrocarbon exploration comprises detection and estimation of shape, depth, volume and other physical properties of hydrocarbon fields within the Earth’s subsurface layers. Marine seismic survey is a process that generally includes sending seismic waves into seabed and recording the intensity and travel time of reflected seismic waves to determine the subsurface features of the Earth. Different methods, equipment and techniques are used to conduct a survey, from sea surface seismic arrays to seabed local seismic station networks. In this paper different widely used seismic methods are presented, together with their advantages and drawbacks. Furthermore, new methods that are being developed and tested for future approach to more advanced and efficient seismic exploration are presented.

Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.

Building outline extraction from ALS point clouds using medial axis transform descriptors

Automatic building extraction and delineation from airborne LiDAR point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or skeleton structure instead of its boundary. Notably, end points of the MAT in principle coincide with corner points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne point clouds challenging. We propose a robust MAT-based method for detecting building corner points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge points acquired by the alpha-shape algorithm to derive a so-called building roof skeleton. We then propose a hierarchical corner-aware segmentation to cluster skeleton points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge point indices. From each segment, a corner point is then estimated by extrapolating the position of the zero radius inscribed circle based on the skeleton point positions within the segment. Our experiment uses point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical point cloud data.

Quantification of measurement error effects on conductivity reconstruction in electrical impedance tomography

Electrical impedance tomography (EIT) is a boundary measurement inverse technique targeting reconstruction of the conductivity distribution of the interior of a physical body based on boundary measurement data. Typically, the measured data are uncertain because of various error sources; thus, there are many uncertainties in the reconstructed image. This study attempts to quantify the effects of these measurement errors on EIT reconstruction. A comprehensive framework that combines uncertainty quantification techniques and EIT reconstruction techniques is proposed. In this framework, a polynomial chaos expansion method is used to construct a surrogate model of the conductivity field with respect to the measurement errors. Two shape detection indices are introduced to show the EIT reconstruction quality. Finally, under certain detection index constraints, statistical and sensitivity analyses are performed using the properties of the surrogate model. Several EIT problems are examined in this study, involving one or two anomalies in a circular domain or two asymmetric anomalies in a body-like domain. The results show that the proposed framework can quantify the effects of measurement errors on EIT reconstruction at reasonable cost. Further, for the test cases, the measurement errors at the electrodes close to the anomalies are shown to have the greatest influence on the image reconstruction.

Traffic sign detection and recognition by improving Region of Interest (ROI) division to support driver assistance system

Traffic signs detection and recognition is a technology that allows automatic identification of traffic signs. In the implementation, it mostly uses a camera to capture video or real-time conditions. The Traffic detection and recognition system can give benefits to improve safety for drivers or passengers. Therefore, traffic signs detection and recognition system are one of the important parts in Driver Assistance Systems (DAS), besides that it is also used in video surveillance to improve traffic safety in the field of Intelligent Transportation System (ITS). In this paper, we propose a method for detecting and recognizing traffic signs by combining color and shape detection and divide the interest object to several regions for object recognition. The first step is image transformation, which aims to determine interest objects in the camera view. Such a process was using hue, saturation, value (HSV) color space, grey level, and black and white (BW) level. The next step is the filter and the segmentation process. The result of the filter and segmentation process then detects area objects by using region determination. The last step is the region of interest (ROI), which is used to recognizing sign traffic by the sum of absolute differences (SAD), and we try to improve area selection of ROI around nine parts. The results of the experiment proved that the proposed method was relatively good with the success rate for detecting objects about 97,38 %, and the true positive of recognition result about 81,67 %.

Automatic Digital Fringe Projection for Advanced Micro-Scale Connector Manufacturing System

The digital fringe projection microscope has been investigated and used to estimate three-dimensional shape of micro-scale connector for advanced manufacturing system. This measurement system consists of digital projector, optical lenses, stereo microscope, and digital camera. In the software, the imaging program based on including black and white structure light, seven-step phase-shifting calculation, and path-independent phase unwrapping is well developed. The imaging results indicate that the three-dimensional shape of micro-scale connector is acquired by using this measurement system. The optical resolution of this measurement system is 3 μm and the measurement speed is 0.6 s. This measurement system has excellent performance including easy operation, fast measurement speed and high resolution. This measurement system can be applied to real-time three-dimensional shape detection in product processing of micro-scale connector.

Adaptive evolution strategy sample consensus for 3D reconstruction from two cameras

RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. In our previous work, we proposed Adaptive Evolution Strategy SAmple Consensus (A-ESSAC) as a new robust estimator, and we applied ESSAC to the homography matrix estimation for 3D SLAM using RGB-D camera. A-ESSAC is based on Evolution Strategy to maintain the genetic diversity. Furthermore, ESSAC has two heuristic searches. One is a search range control for reducing the computational cost of RANSAC. The other is adaptive/self-adaptive mutation for changing the search strategy of A-ESSAC according to the best fitness value. In this paper, we apply A-ESSAC to 3D reconstruction method using two cameras, and we show an experimental result, and discuss the effectiveness of the proposed method.

Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing Map.

Point detection (e.g., the centroid of the body) of species has been conducted in numerous studies. However, line detection (i.e., the line body shape) of elongated species has rarely been investigated under stressful conditions. We analyzed the line movements of an Oligochaeta Lumbriculus variegatus in response to treatments with a toxic chemical, copper sulfate, at low concentrations (0.01 mg/L and 0.1 mg/L). The automatic line-tracking system was devised to identify the movement of body segments (body length) and the movements of segments (i.e., the speed and angles between segments) were recorded before and after treatment. Total body length was shortened from 31.22 (±5.18) mm to 20.91 (±4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (±0.1) to 0.28 (±0.08) after treatment. On the other hand, the body and movement segments did not significantly change after the 0.01 mg/L treatment. Sequential movements of test organisms were further analyzed with a recurrent self-organizing map (RSOM) to determine the pattern of time-series line movements. The RSOM made it feasible to classify sequential behaviors of indicator organisms and identify various continuous body movements under stressful conditions.