Set Of Contour Points Research Articles

SatellStitch: Satellite Imagery-Assisted UAV Image Seamless Stitching for Emergency Response without GCP and GNSS

Rapidly stitching unmanned aerial vehicle (UAV) imagery to produce high-resolution fast-stitch maps is key to UAV emergency mapping. However, common problems such as gaps and ghosting in image stitching remain challenging and directly affect the visual interpretation value of the imagery product. Inspired by the data characteristics of high-precision satellite images with rich access and geographic coordinates, a seamless stitching method is proposed for emergency response without the support of ground control points (CGPs) and global navigation satellite systems (GNSS). This method aims to eliminate stitching traces and solve the problem of stitching error accumulation. Firstly, satellite images are introduced to support image alignment and geographic coordinate acquisition simultaneously using matching relationships. Then a dynamic contour point set is constructed to locate the stitching region and adaptively extract the fused region of interest (FROI). Finally, the gradient weight cost map of the FROI image is computed and the Laplacian pyramid fusion rule is improved to achieve seamless production of the fast-stitch image map with geolocation information. Experimental results indicate that the method is well adapted to two representative sets of UAV images. Compared with the Laplacian pyramid fusion algorithm, the peak signal-to-noise ratio (PSNR) of the image stitching results can be improved by 31.73% on average, and the mutual information (MI) can be improved by 19.98% on average. With no reliance on CGPs or GNSS support, fast-stitch image maps are more robust in harsh environments, making them ideal for emergency mapping and security applications.

Methods and experiments for collecting information and constructing models of bottom-layer contours in paddy fields

To realize intelligent, accurate, and efficient operation of paddy field machinery that drive the paddy field bottom-layer below the tillage layer, the paddy field bottom-layer contours must be known. However, surface measurement systems cannot directly measure paddy field bottom-layer contours, making it difficult to collect and digitally model. In this study, an agricultural machine without a shock-absorbing suspension on the rear axle was used as an acquisition platform. Combined with a global navigation satellite system (GNSS) and attitude and heading reference system (AHRS) positional measurement technology, a 1/4 single-degree-of-freedom model of the vehicle body collected bottom-layer contour information of paddy fields. Based on the Delaunay triangulation principle, a digital model of the bottom-layer contour was established based on the collected bottom-layer contour point set. Subsequently, the ray method, used to extract specific triangles containing predicted points, and the point-plane intersection principle, used to estimate the elevation of arbitrary points in the whole area of bottom-layer, were used. Straight line contour acquisition on concrete road slope, contour acquisition on the bottom-layer surface of a paddy field, and elevation estimation of arbitrary points in the whole area were performed as verification tests. The results indicate that, in the x, y, and z axes, the average errors of the cement slope profile collection were 0.86, 0.68, and 0.55 cm, respectively, with standard errors of 0.99, 0.78, and 0.67 cm, respectively. Further, the average errors of the bottom-layer contour collection of the paddy field were 1.57, 0.91, and 1.25 cm, respectively, with standard errors of 1.87, 1.01, and 1.55 cm, respectively. The average error of elevation estimation at any point in the whole area was 1.25 cm with a standard error of 1.49 cm. The presented method can continuously and accurately acquire bottom-layer contour information, construct a digital model of the bottom-layer contour, accurately predict the location coordinates of any point within the field, and provide a reference for the walking environment of intelligent farming machines and measurement methods for the production of high-precision topographic maps.

Contour deformation network for instance segmentation

• GCN-based contour deformation network is proposed. • The refined contour of an object mask is achieved for instance segmentation . • To deal with various sizes of objects in scenes, adaptive deformation-scale selection strategy presented. • Automatically constructs the local neighborhood graph and selects multiscale features. • Extensive experimental results provided to demonstrate the performance of the proposed network. To improve the precision of the contour in instance segmentation, this study proposes an iterative contour deformation network (CD-Net) based on a graph convolutional network (GCN). The proposed method treats the segmentation results of the Mask R-CNN model as the initial contours and refines the instances contour iteratively. Specifically, a contour point set is first sampled from the initial contour. Considering the various sizes of the instances, and according to the size of corresponding bounding boxes determined by the Mask R-CNN, a local neighborhood graph is constructed for each selected contour point. Subsequently, multi-scales features are automatically selected and combined with features learned in Mask R-CNN for each point in the local neighborhood graph. The local neighborhood graphs with features are then fed into the GCN to learn the deformation vectors, and the instance contours are refined accordingly. Finally, the refined contour is treated as the initial contour, and the above process is repeated to obtain the final instance contours. The experimental results on the COCO and Cityscapes datasets demonstrate that the proposed method achieves state-of-the-art performance.

Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields.

Logging-while-drilling formation dip interpretation based on long short-term memory

Azimuth gamma logging while drilling (LWD) is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult. This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing. By examining formation response characteristics of azimuth gamma ray (GR) curve, the preliminary formation change position is detected based on wavelet transform modulus maxima (WTMM) method, then the dynamic threshold is determined, and a set of contour points describing the formation boundary is obtained. The classification recognition model based on the long short-term memory (LSTM) is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification. Finally, relative dip angle is calculated by nonlinear least square method. Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes, improve the accuracy of formation dip interpretation, and meet the needs of real-time LWD geosteering.

A contour-guided pose alignment method based on Gaussian mixture model for precision assembly

Purpose The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system. Design/methodology/approach A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets. Findings The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance. Originality/value Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

Filling Algorithm of Polygons and Embedded Polygons

In this paper, by studying the laser marking trajectory model, for the two different marking modes of zigzag parallel and contour parallel, with or without contour nesting, different laser marking path generation algorithms are designed. And compare the time-consuming of the two algorithms, discuss and propose methods to optimize the algorithm to improve the efficiency of the algorithm to meet the needs of industrial applications. According to the requirements of the distance between the retracted boundary and the hatch line, the zigzag parallel trajectory generation algorithm and the contour parallel trajectory generation algorithm are designed, and the hatching curve is generated according to the given contour point set. Based on the algorithm above, in the face of contour nesting, the algorithm is improved, and the hatching curve is generated according to the given contour point set. For the zigzag parallel trajectory generation algorithm, considering the intersection of the scan line and the inner contour. For contour parallel trajectories, this paper uses a vertex-based offset algorithm, which takes a long time. This is because the vertex-based offset algorithm is more complicated to solve the problem of self-intersection. This paper proposes that for the zigzag parallel method, the angle of the workpiece can be adjusted to find the optimal angle, so that the marking path is the smallest. And for the contour parallel trajectory generation algorithm, this paper proposes an optimization too.

High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations

Background and ObjectiveHigh-quality vascular modeling is crucial for blood flow simulations, i.e., computational fluid dynamics (CFD). As without an accurate geometric representation of the smooth vascular surface, it is impossible to make meaningful blood flow simulations. The purpose of this work is to develop high-quality vascular modeling and modification method for blood flow computations. MethodsWe develop a new technique for the accurate geometric modeling and modification of vasculatures using implicit extrusion surfaces (IES). In the proposed method, the skeleton of the vascular structure is subdivided into short curve segments, each of which is then represented implicitly locally as the intersection of two mutually orthogonal implicit surfaces defined by distance functions. A set of contour points is extracted and fitted with an implicit curve for accurately specifying the vessel cross-section profile, which is then extruded locally along the skeleton to fill the gaps between two vascular tube cross sections. We also present a new implicit geometric editing technique to modify the constructed vascular model with pathology for virtual stenting. ResultsExperimental results and validations show that accurate vascular models with highly smooth surfaces can be generated by the proposed method. In addition, we conduct some blood flow simulations to indicate the effectiveness of proposed method for hemodynamic simulations. ConclusionsThe proposed technique can achieve precise geometric models of vasculatures with any required degree of smoothness for reliable blood flow simulations.

Joint classification and contour extraction of large 3D point clouds

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours.Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

A Characteristic Statistic Based Femoral Contour Point-set Registration Algorithm

The point-set registration technology has been widely applied in the field of medical image registration. This paper proposed a characteristic statistic based femoral contour point-set registration algorithm, in order to overcome the flaw of getting into local extrema and of poor global performance in the iterative optimization process in existing registration algorithms. First, it uses the Gaussian mixture model to represent the correspondence of points between two point-sets, and selects the global parameter associated with the objective function as the characteristic statistical items. Then, it leverages the statistical variation in the optimization process to guide the optimal search, yielding the global optimal solution of statistical items, and realizing the registration of two point-sets. In the end, this algorithm is applied to solve the registration problem of femoral contour characteristic point-set. The experimental results reveal that the proposed registration method performs more effective, faster, and more accurate compared with Coherent Point Drift (CPD) algorithm.

Bladder accumulated dose in image-guided high-dose-rate brachytherapy for locally advanced cervical cancer and its relation to urinary toxicity

The purpose of this study was to estimate locally accumulated dose to the bladder in multi-fraction high-dose-date (HDR) image-guided intracavitary brachytherapy (IG-ICBT) for cervical cancer, and study the locally-accumulated dose parameters as predictors of late urinary toxicity.A retrospective study of 60 cervical cancer patients who received five HDR IG-ICBT sessions was performed. The bladder outer and inner surfaces were segmented for all sessions and a bladder-wall contour point-set was created in MATLAB. The bladder-wall point-sets for each patient were registered using a deformable point-set registration toolbox called coherent point drift (CPD), and the fraction doses were accumulated. Various dosimetric and volumetric parameters were calculated using the registered doses, including (minimum dose to the most exposed n-cm3 volume of bladder wall), r Vn Gy (wall volume receiving at least m Gy), and (minimum equivalent biologically weighted dose to the most exposed n-cm3 of bladder wall), where n = 1/2/5/10 and m = 3/5/10. Minimum dose to contiguous 1 and 2 cm3 hot-spot volumes was also calculated. The unregistered dose volume histogram (DVH)-summed equivalent of and parameters (i.e. and ) were determined for comparison. Late urinary toxicity was assessed using the LENT-SOMA scale, with toxicity Grade 0–1 categorized as Controls and Grade 2–4 as Cases. A two-sample t-test was used to identify the differences between the means of Control and Case groups for all parameters. A binomial logistic regression was also performed between the registered dose parameters and toxicity grouping. Seventeen patients were in the Case and 43 patients in the Control group. Contiguous values were on average 16 and 18% smaller than parameters for 1 and 2 cm3 volumes, respectively. Contiguous values were on average 26 and 27% smaller than parameters. The only statistically significant finding for Case versus Control based on both methods of analysis was observed for r V3 Gy (p = 0.01).DVH-summed parameters based on unregistered structure volumes overestimated the bladder dose in our patients, particularly when contiguous high dose volumes were considered. The bladder-wall volume receiving at least 3 Gy of accumulated dose may be a parameter of interest in further investigations of Grade 2+ urinary toxicity.

Research on Iris Localization Algorithm based on the Active Contour Model

An automatic and robust iris segmentation algorithm will be employed when iris recognition is used to identify human efficiently. Many studies have shown that iris localization is one of the most important parts of the iris recognition system. Most current studies are based on the assumption that iris edge is round, and the gray information of eye image is used for iris segmentation, which requires very high quality of image. Based on the active contour model, an algorithm for iris location was proposed. First, a plurality of pupil edge points was located as the initial contour point set. Then, the real contour evolution of pupil was approximated by the approximation of energy function. Finally, the accurate iris edge was located by daugman’s circular gradient operator. Based on CASIA (V2.0) iris database, experiment results show that the location effect of the proposed algorithm is more superior than that of the classical calculus method and hough transform method.

A Remote Sensing Image Registration Algorithm Using Improved Shape Context and Singular Value Decomposition

A remote sensing image registration algorithm is proposed based on improved Shape Context (SC) for feature extraction and the Singular Value Decomposition (SVD) for feature matching. Firstly, according to the nonuniform contour point set, the improved SC descriptor is obtained. Secondly, the similarity of SC descriptors and gray cross-correlation coefficient around the neighborhood of the feature point are given to construct the proximity matrix.Thirdly, the pairing matrix is gained, which is decomposed from proximity matrix with SVD method. Finally, image registration is achieved by affine transformation and bilinear interpolation.The experimental results show that the algorithm inherits 2D invariance of SC descriptor and robustness to nonlinear geometric deformations, which has faster computing speed and higher matching precision.

Contour Points based P2P Algorithm for Shape Matching and Image Retrieval

Based on the extracted contours of objects from images, one point to point (P2P) method is proposed for shape matching and image retrieval. Taking contour of one object as reference, contour of another object is transformed to reach their best match, during which the similarity is evaluated by comparison of two corresponding sets of contour points. Translation, scaling and rotation are all considered in transformation, thus the algorithm is robust to objects with different position, size and posture. The experimental results are presented and compared with those from two popular shape based techniques, Hu invariant moments and Zernike moments. Performance of our new approach has proved its efficiency in both ma tching accuracy and computational expense, and it can be used in related applications together with the other kinds of shape features or eve n color features, texture features, etc.

An evolutionary image matching approach

Bee colony optimization (BCO) is a meta-heuristic technique inspired by natural behavior of the bee colony. In this paper, the BCO technique is exploited to tackle the shape matching problem with the aim to find the matching between two shapes represented via sets of contour points. A number of bees are used to collaboratively search the optimal matching using a proposed proximity-regularized cost function. Furthermore, the proposed cost function considers the proximity information of the matched contour points; this is in the contrast to that these contour points are treated independently in the conventional approaches. Experimental results are presented to demonstrate that the proposed approach is able to provide more accurate shape matching than the conventional approaches.

Matching sequences of salient contour points characterized by Voronoi region features

In this paper, we introduce a shape matching method by matching sequences of salient contour points that are characterized by Voronoi region features. The proposed approach is summarized as follows: (1) a sequence of salient contour points is selected using the Voronoi diagram of the contour point set, (2) the features of the salient points are computed based on the interior and exterior regions of the Voronoi diagram, and (3) a cyclic edit distance is used to match two shapes. Tests on the MPEG-7 and ETH-80 datasets demonstrated the effectiveness and efficiency of the proposed method.

SU‐E‐T‐818: Analysis of Internal Margin Based on Organ Deformation for Prostate IMRT

Purpose: The purpose of this study was to predict organ deformation and the possible CTV‐ITV reduction when adopting daily IGRT for prostate cancer patient. Further, to identify any intra‐fraction time trends in organ deformation. And to analyze internal margin for prostate IMRT based on organ deformation. Methods: It was intended to investigate 30 patients treated with prostate IMRT. The pre‐disposal in radiotherapy was implemented by instructing each patient to drink 500 cc of water each time after defecation and urination. CT images for radiation treatment planning were acquired 3 times at 10‐minute intervals for 30 minutes after drinking water. CT images were transferred to treatment planning system. One physician contoured the prostate, rectum, and bladder for all images. The acquired three‐dimensional contour data of the organ were transferred to a customized data processing program. Then, the prostate, rectum and bladder were divided into eight segments. These regions were analyzed with respect to deformation of the entire set of contour points in the 3‐dimensional outline surface in the region of each of the organs. Results: The deformation of the prostate tended to become amplified in the extension direction accompanied by an increase in deformation of the bladder to the anterior and superior direction. But there were not correlated the rectum deformation. The internal margins which not considered organ deformation, were 2.27 mm, 1.76 mm, and 0.99 mm in the L‐R, S‐I, and A‐P direction respectively. However, respect to the organ deformation, these margins were 1.27 mm, 1.56 mm, and 1.53 mm respectively. Conclusions: This analysis based on organ deformation was found to be useful for determination of internal margin. And it will be possible that the extra margin is reduced.

INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION

By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.

Higher Order Active Contours

We introduce a new class of active contour models that hold great promise for region and shape modelling, and we apply a special case of these models to the extraction of road networks from satellite and aerial imagery. The new models are arbitrary polynomial functionals on the space of boundaries, and thus greatly generalize the linear functionals used in classical contour energies. While classical energies are expressed as single integrals over the contour, the new energies incorporate multiple integrals, and thus describe long-range interactions between different sets of contour points. As prior terms, they describe families of contours that share complex geometric properties, without making reference to any particular shape, and they require no pose estimation. As likelihood terms, they can describe multi-point interactions between the contour and the data. To optimize the energies, we use a level set approach. The forces derived from the new energies are non-local however, thus necessitating an extension of standard level set methods. Networks are a shape family of great importance in a number of applications, including remote sensing imagery. To model them, we make a particular choice of prior quadratic energy that describes reticulated structures, and augment it with a likelihood term that couples the data at pairs of contour points to their joint geometry. Promising experimental results are shown on real images.

Multimodality cardiovascular image segmentation using a deformable contour model

An automatic segmentation method has been developed for cardiovascular multimodality imaging. A ‘snake’ model based on a curve shaping and an energy-minimizing process is used to detect blood-wall interfaces on Cine-CT, MRI and ultrasound images. Deformation of a reduced set of contour points was made according to a discretized global, regional and local minimum energy criterion. A continuous regional optimization process was also intergrated into the deformation model, it takes into account a cubic spline interpolation and adaptive regularity constraints. The constraints provided rapid convergence toward a final contour position by successively stopping spline segments.