Automatic Extraction Algorithm Research Articles

光栅型波前曲率传感器光斑自动定位提取算法

光栅型波前曲率传感器光斑自动定位提取算法

基于曲率突变分析的点云特征线自动提取

基于曲率突变分析的点云特征线自动提取

Automatic contour extraction algorithm for swept-source optical coherence tomography cornea image

In a swept source-optical coherence tomography system, the telecentric scanning mode gives rise to central saturation artifacts,partial structural loss, and low SNR (signal-to-noise ratio) area in the corneal image, which affects the accuracy of corneal contour extraction. In order to solve this problem, in this paper we propose an automatic extraction algorithm for corneal image of low quality. This algorithm divides the image into high and low SNR region according to the standard deviation distribution of the cornea image. For the high SNR region, we localize the peak point to extract the contour. For the low SNR region, image enhancement is achieved by the registration and superposition of successive frames, which provides reference contour points for low SNR areas. Then corneal contour localization is achieved by weighing the advantages and disadvantages of reference contour points and local line fitting results. Finally, global polynomial fitting is used to achieve the whole corneal contour information. Experiments on the optical eye model show that comparing with the existing algorithms, the accuracy of corneal contour extraction is improved by 4.9% on average.

An enhanced dynamic modeling of PV module using Levenberg-Marquardt algorithm

An improved dynamic modeling of PV cell/modules based on automatic parameters extraction is proposed in this paper. For the sake of clarity, three models are compared in this study including, Single Diode (SDM), Double Diode (DDM) and the empirical model developed by Sandia National Laboratory (SANDIA). The use of nominal parameters or the values given by manufacturer in both SDM and DDM diode saturation current I0 and photo-generation current Iph equations can engender a significant error depending on the operating conditions and the consumed lifetime. Hence, these values can be handled as model parameters, and can be adjusted using automatic parameters extraction algorithms. Moreover, parameters based on static extraction methods (with fixed irradiation and temperature) namely, Rs, Rsh and n do not give satisfactory results under variable irradiation and temperature, which involve the use of a dynamic adjustment method to improve these parameters. In this way, static parameters extraction using genetic algorithm (GA) is proposed as a first stage for both SDM and DDM. After that, a dynamic parameters extraction based on the Levenberg-Marquardt algorithm (LMA) has been employed in the purpose to adjust some nominal parameters provided by the literature and the manufacturer, and those given by the static method. The idea consists of considering the PV module and the MPPT as a single system with dynamic inputs (irradiation and temperature) and output (Impp, Vmpp and Pmpp) to minimize the error between the measured and the simulated outputs. The validity of the proposed approach is compared with dynamic LMA models, nominal parameters based models, and the models based on static GA extracted parameters under of different weather conditions and out-door measurements. The improved models show promising results in terms of agreement with real data.

GREEN INDEXING OF HISAR MUNICIPAL CORPORATION USING GEOSPATIAL TECHNIQUES

Abstract. Urban Green Space (UGS) enhances the structure of the landscape of a city. It provides economic, social and health benefits to the inhabitants. Rapid urbanization and increase in population has put extreme pressure on urban green spaces. Therefore, mapping of urban green spaces is important to facilitate the urban planning sustainably. This study has been taken up to map the UGS in the Hisar Municipal Corporation (HMC) area of Haryana State constituting an area of 4197.32 ha using high resolution LISS-IV data of November, 2017. Three different methods namely (a) Onscreen Digitization (b) Support Vector Machine-based classification (SVM) and (c) Multi Resolution Segmentation (MRS) have been tested to extract the green spaces. The onscreen digitization method has been considered as best method as compared to SVM and MRS because of the intervention of human mind and full control on manual editing. The SVM under estimated the green space for HMC with average relative deviation of 15%. However, it extracted the green area precisely without mixing of open spaces. MRS have given good results in terms of total area (relative deviation was 5%), however, spatial distribution of green spaces have been mixed with open spaces. The study identified the green index of HMC and suggested the suitability and sustainability of the city based on World Health Organization (WHO) defined standards. Green space for Hisar Municipal Corporation was 1690.96 ha with average urban green space index of 0.40. Per capita green cover for HMC was 9.69 m2/inhabitant, which is as per the standard reported by WHO i.e. 9 m2/inhabitant. Further study may suggest some other algorithm for better results and automatic green space extraction.

An automatic shadow detection method for high-resolution remote sensing imagery based on polynomial fitting

ABSTRACTMost existing shadow detection models and algorithms require extensive calculations and have difficulties effectively removing features, such as water bodies, some dark objects and bluish ground objects. In this paper, we propose a high-resolution automatic shadow extraction algorithm based on the process of histogram fitting. First, the histogram of the whole image is fitted by fourth and fifth-degree polynomials according to the histogram difference of the near-infrared bands of different shadow areas in the remotely sensed image. Second, the shadow area is preliminarily extracted based on the relationships between the shadow features of the remote sensing image and the intersections of the fourth- and fifth-degree polynomials. Then, the normalized difference water index (NDWI) is applied to extract the water bodies. Finally, to obtain the shaded area, the scanning line seed filling algorithm is applied to remove the water bodies falsely detected as shadows in the preliminary shading extraction. The proposed algorithm is evaluated by using the various high-resolution images including GaoFen-1 (GF-1), GaoFen-2 (GF-2), QuickBird2, and ZiYuan-3 (ZY-3), as well as an elaborate comparison to histogram threshold segmentation algorithms such as Component 3 (C3) algorithm, multi-elements extraction algorithm multi-band detection algorithm, and spectral correlation algorithm based on spectral features. The results of experiment showed that the proposed algorithm could extract the shadows of various images, achieve satisfied results, and completely remove water bodies.

Precise initial weld position identification of a fillet weld seam using laser vision technology

Full autonomy is the ultimate goal in robotic welding. Heretofore, to weld a fillet weld seam formed by two steel plates, the initial weld position had to be found by procedures that involved some manual steps. In this paper, the authors propose a completely autonomous method to find the initial weld position for a fillet weld seam formed by two steel plates. This method employs an automatic dynamic programming-based laser light inflection point extraction algorithm. This algorithm needs no information provided about the region of interest (ROI), even when the inflection point is near the border of the image. The algorithm for this method can overcome unstable factors induced by natural light (e.g., a strong light reflection, a light intensity difference), which may be present during the processing of laser vision images. An auxiliary algorithm to compensate for a time delay is also presented. The algorithm proposed in this paper has been validated in actual industrial environments and can satisfy any real industrial application requirements in terms of efficiency and reliability. With this algorithm for the initial weld position, the entire robotic welding process for a fillet weld seam formed by two steel plates can now be fully autonomous.

Algorithm of sheep body dimension measurement and its applications based on image analysis

Body size parameters of sheep can reflect its growth development, production performance and genetic characteristics. Therefore, the body size measurement is great significance in sheep breeding. In this study, a measuring method based on vision image analysis for the body size of sheep is proposed, which can be practically applied in farm environment without disturbing the animals. This approach is based on computer-assisted visual image capture in a position-limit apparatus, and based on an automatic foreground area extraction algorithm known as simple linear iterative clustering (SLIC) SuperPixels and Fuzzy c-means (FCM) clustering, a center line of flexible symmetrical body extraction algorithms, as well as measuring points extraction algorithms. The test results for 27 small-tailed Han sheep chosen randomly from herd show that the method for foreground extraction can get segmentation images with well-remained boundaries. Detection algorithm of the center line of sheep body in top view has a relatively high adaptability. The extraction of measurement points in different postures for sheep's body size has a better stability and accuracy, the maximum average relative errors between the detected and measured values of body height, rump height, body length, chest depth, chest width and rump width are 1.13%, 1.54%, 2.03%, 4.45%, 2.25% and 2.41%, respectively. The use of both left view and right view can improve the precision of the measurement, and the values from one test may have a greater deviation from the actual values due to the variety of sheep body posture, but the accuracy can be improved by averaging the measurements repeated for many times. The results also show that measurement of sheep size based on vision image analysis is feasible, and it can ensure accuracy, reduce workload and sheep stress compared to the method conducted by man. Prediction result of live-sheep weight based on body size shown that the parameters got by image processing can be used for monitoring the growth of sheep.

An automatic extraction algorithm of high voltage transmission lines from airborne LIDAR point cloud data

To improve the effectiveness and generality of existing methods of high voltage (HV) transmission lines extraction, this paper proposes a novel automatic extraction method of HV transmission lines using airborne LIDAR point cloud data by incorporating the geography of transmission corridors and the distribution characteristics of airborne LIDAR point cloud data. The proposed method results in the separation of ground objects by using a differentiation height threshold segmentation algorithm based on subspace features, which divide long-distance space into several small-distance space sets to improve the generality of the algorithm. A height density segmentation algorithm is used to locate transmission towers and extract point cloud data of HV transmission lines to improve the efficiency of the algorithm. Feasibility test cases show that the proposed method can automatically extract HV transmission lines from airborne LIDAR point cloud data in flat terrains, canyon terrains, and steep slope terrains in an efficient manner, with high accuracy and generality.

Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography

Devolatilization is an important process in pulverized coal combustion because it affects the ignition, volatile combustion, and subsequent char burning and ash formation. In this study, high-speed digital in-line holography is employed to visualize and quantify the particle and volatile evolution during pulverized coal combustion. China Shanxi bituminous coal particles sieved in the range of 105–154 µm are entrained into a flat flame burner through a central tube for the study. Time-resolved observations show the volatile ejection, accumulation, and detachment in the early stage of coal combustion. Three-dimensional imaging and automatic particle extraction algorithm allow for the size and velocity statistics of the particle and stringy volatile tail. The results demonstrate the smaller particle generation and coal particle swelling in the devolatilization. It is found that the coal particles and volatiles accelerate due to the thermal buoyancy and the volatiles move faster than the coal particles. On average, smaller particles move faster than the larger ones while some can move much slower possibly because of the fragmentation.

Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy, such as leaf area, leaf distribution, and 3D model. The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds. This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive, unorganized LiDAR point clouds. In order to mine the distinct geometry of corn leaves and stalk, the Difference of Normal (DoN) method was proposed to extract corn leaf points. Firstly, the normals of corn leaf surface for all points were estimated on multiple scales. Secondly, the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution. Finally, the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points. The quantitative accuracy assessment showed that the overall accuracy was 94.10%, commission error was 5.89%, and omission error was 18.65%. The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive, unorganized terrestrial LiDAR point clouds using the proposed DoN method. Keywords: corn leaves, terrestrial LiDAR, cloud points, automatic extraction, crop growth monitoring, phenotyping, difference of normal (DoN), directional ambiguity of the normals DOI: 10.25165/j.ijabe.20181103.3177 Citation: Su W, Zhang M Z, Liu J M. Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds. Int J Agric & Biol Eng, 2018; 11(3): 166–170.

Versatile system for single cell acquisition and analysis

Abstract Single cell analysis (SCA) has become an essential part of basic and clinical research. A prerequisite to SCA studies is the isolation of individual cells from cell cultures. There are commercially available technologies including laser based systems and cell sorting instruments. However, they are typically complex and expensive making integration within standard lab/clinical sample processing workflows difficult. We have developed a universal platform for rapid acquisition of single cells from cultures and deposition into the single wells for further molecular analysis or clonal expansion. The instrument may also be used for tissue microdissection and measurements of individual cell adhesion. The main working principle is based on the capillary based vacuum pulse assisted technology (CTAS; UP8797644). Developed instrument collects individual cells from any adherent cultures grown in standard cell culture dishes in as small as 15 nl volume. It may be used with a wide range of inverted microscopes, so that the cells of interest can be identified based on morphology, location or fluorescent label. Automatic extraction and deposition algorithms were developed for acquisition of targeted cells and tissue regions of interest. Here, individual cells were collected from human neuroblastoma SH-SY5Y, CHO, and 3T3 cell lines. Collected cells were dispensed immediately into individual wells for clonal expansion. Clonal expansion and trypan blue assay revealed minimal effect on cellular viability (up to 99% when compared to controls). The benefits of the proposed technology include cost-efficiency, simple operation, complete workflow, and compatibility with a wide range of inverted microscopes and standard culture dishes.

GLACIER FRONTAL LINE EXTRACTION FROM SENTINEL-1 SAR IMAGERY IN PRYDZ AREA

Abstract. Synthetic Aperture Radar (SAR) can provide all-day and all-night observation of the earth in all-weather conditions with high resolution, and it is widely used in polar research including sea ice, sea shelf, as well as the glaciers. For glaciers monitoring, the frontal position of a calving glacier at different moments of time is of great importance, which indicates the estimation of the calving rate and flux of the glaciers. In this abstract, an automatic algorithm for glacier frontal extraction using time series Sentinel-1 SAR imagery is proposed. The technique transforms the amplitude imagery of Sentinel-1 SAR into a binary map using SO-CFAR method, and then frontal points are extracted using profile method which reduces the 2D binary map to 1D binary profiles, the final frontal position of a calving glacier is the optimal profile selected from the different average segmented profiles. The experiment proves that the detection algorithm for SAR data can automatically extract the frontal position of glacier with high efficiency.

ECOLOGICAL SENSITIVITY EVALUATION OF TOURIST REGION BASED ON REMOTE SENSING IMAGE – TAKING CHAOHU LAKE AREA AS A CASE STUDY

Abstract. Remote sensing technology is of significant advantages for monitoring and analysing ecological environment. By using of automatic extraction algorithm, various environmental resources information of tourist region can be obtained from remote sensing imagery. Combining with GIS spatial analysis and landscape pattern analysis, relevant environmental information can be quantitatively analysed and interpreted. In this study, taking the Chaohu Lake Basin as an example, Landsat-8 multi-spectral satellite image of October 2015 was applied. Integrated the automatic ELM (Extreme Learning Machine) classification results with the data of digital elevation model and slope information, human disturbance degree, land use degree, primary productivity, landscape evenness , vegetation coverage, DEM, slope and normalized water body index were used as the evaluation factors to construct the eco-sensitivity evaluation index based on AHP and overlay analysis. According to the value of eco-sensitivity evaluation index, by using of GIS technique of equal interval reclassification, the Chaohu Lake area was divided into four grades: very sensitive area, sensitive area, sub-sensitive areas and insensitive areas. The results of the eco-sensitivity analysis shows: the area of the very sensitive area was 4577.4378 km2, accounting for about 37.12 %, the sensitive area was 5130.0522 km2, accounting for about 37.12 %; the area of sub-sensitive area was 3729.9312 km2, accounting for 26.99 %; the area of insensitive area was 382.4399 km2, accounting for about 2.77 %. At the same time, it has been found that there were spatial differences in ecological sensitivity of the Chaohu Lake basin. The most sensitive areas were mainly located in the areas with high elevation and large terrain gradient. Insensitive areas were mainly distributed in slope of the slow platform area; the sensitive areas and the sub-sensitive areas were mainly agricultural land and woodland. Through the eco-sensitivity analysis of the study area, the automatic recognition and analysis techniques for remote sensing imagery are integrated into the ecological analysis and ecological regional planning, which can provide a reliable scientific basis for rational planning and regional sustainable development of the Chaohu Lake tourist area.

CONSTRUCTION OF GREEN TIDE MONITORING SYSTEM AND RESEARCH ON ITS KEY TECHNIQUES

Abstract. As a kind of marine natural disaster, Green Tide has been appearing every year along the Qingdao Coast, bringing great loss to this region, since the large-scale bloom in 2008. Therefore, it is of great value to obtain the real time dynamic information about green tide distribution. In this study, methods of optical remote sensing and microwave remote sensing are employed in Green Tide Monitoring Research. A specific remote sensing data processing flow and a green tide information extraction algorithm are designed, according to the optical and microwave data of different characteristics. In the aspect of green tide spatial distribution information extraction, an automatic extraction algorithm of green tide distribution boundaries is designed based on the principle of mathematical morphology dilation/erosion. And key issues in information extraction, including the division of green tide regions, the obtaining of basic distributions, the limitation of distribution boundary, and the elimination of islands, have been solved. The automatic generation of green tide distribution boundaries from the results of remote sensing information extraction is realized. Finally, a green tide monitoring system is built based on IDL/GIS secondary development in the integrated environment of RS and GIS, achieving the integration of RS monitoring and information extraction.

Development, Implementation and Validation of an Automatic Centerline Extraction Algorithm for Complex 3D Objects

Although centerlines (aka ‘skeletons’ or ‘medial axes’) are among the most efficient ways to represent items in digital images, and obtaining these from 2D shapes is relatively straight forward, extracting centerlines from 3D objects has remained a challenge—particularly if they are not smooth or uniform. Therefore, we have developed a novel 3D centerline extraction method for discrete binary objects using a ‘divide and conquer’ approach, in which any 3D object is sliced into a series of 2D images (in the X, Y and Z directions), a geometric (Voronoi) algorithm is applied to each planar image to extract the 2D centerlines, and the information is recombined (using an intersection technique) to obtain the centerline of the original 3D object. Validation of this approach was performed using a ground truth benchmark, standard 3D database objects, and more complex anatomical structures (segmented from medical imaging data). The algorithm consistently performed well for objects of moderate complexity, but occasionally left discontinuities in the extracted 3D skeletons of the most complex objects. Therefore, in order to deal with such cases, an optional 3D interpolation step—based on Delaunay triangles and a spherical search to establish the nearest neighboring points in 3D space—was developed to allow continuous centerlines to be extracted from even the most complex anatomical structures tested. As a result, we anticipate that this approach could have wide-ranging applications, including data reduction for large microscopy and other 3D imaging datasets, automatic quantification of medical imaging data along anatomical structures, and others.

Roads Feature Extraction from High Resolution Satellite Images Using Decision Tree Classifier

Extracting roads network data, from satellite images, is important for urban planning, infrastructure development, navigation applications, military purposes and updating topographic GIS databases. Very high spatial resolution remote sensing images are useful source for extracting roads information. In this paper a novel algorithm for roads feature extraction of sub-urban and rural areas from high resolution images is developed. The developed algorithm is based on Decision Tree classifier (DTC). The developed DTC models are trained and applied on corrected Multispectral images acquired by Worldview – II (WV-2) satellite. The experimental work is performed using MATLAB with Graphic User Interface (GUI) for designing and managing the training data .The result of DTC model were evaluated using three of quality measures. The results are compared with a digitized reference roads layer. The obtained results show the possibility of using the developed algorithm for automatic roads feature extraction from high resolution images. The results provide about 82% accuracy of roads extraction.

An Automatic Sparse Pruning Endmember Extraction Algorithm with a Combined Minimum Volume and Deviation Constraint

In this paper, an automatic sparse pruning endmember extraction algorithm with a combined minimum volume and deviation constraint (SPEEVD) is proposed. The proposed algorithm can adaptively determine the number of endmembers through a sparse pruning method and, at the same time, can weaken the noise interference by a minimum volume and deviation constraint. A non-negative matrix factorization solution based on the projection gradient is mathematically applied to solve the combined constrained optimization problem, which makes sure that the convergence is steady and robust. Experiments were carried out on both simulated data sets and real AVIRIS data sets. The experimental results indicate that the proposed method does not require a predetermined endmember number, but it still manifests an improvement in both the root-mean-square error (RMSE) and the endmember spectra, compared to the other state-of-the-art methods, most of which need an accurate pre-estimation of endmember number.

Application of GC/Q-ToF Combined with Advanced Data Mining and Chemometric Tools in the Characterization and Quality Control of Bay Leaves.

Correct identification of the true bay leaf (Laurus nobilis) and its substitutes is important not only for the quality control of the products, but also for the safety of the consumers. L.nobilis is often substituted or confused with other species, such as Cinnamomum tamala, Pimenta racemosa, Syzygium polyanthum, and Umbellularia californica. In the present study, the potential of gas chromatography combined with quadrupole time-of-flight mass spectrometry for the profiling of various bay leaf products was evaluated for the first time. Thirty-nine authenticated samples representing the true bay leaf and the four commonly substituted species were analyzed. An automatic feature extraction algorithm was applied for data mining and pretreatment in order to identify the most characteristic compounds representing different bay leaf groups. This set of data was employed to construct a sample class prediction model based on stepwise reduction of data dimensionality followed by principal component analysis and partial least squares discriminant analysis. The statistical model, with demonstrated excellent accuracies in recognition and prediction abilities, enabled the correct classification of commercial samples including complex mixtures and essential oils. In addition, in-house developed personal compound database and library with retention time locking offered the advantage of combining retention time matching with accurate mass matching, resulting in high confidence of compound identification for each bay leaf subgroup. At least three marker compounds were identified for each bay leaf species that could be used to discriminate among them.

The Utility of Sentinel-1 Data for Ocean Surface Feature Analysis in the Vicinity of the Gulf Stream

ABSTRACTEuropean Space Agency Sentinel-1 (S1) synthetic aperture radar (SAR) datasets are used to assess their suitability for ocean surface feature extraction in the vicinity of the Gulf Stream. A SAR ocean feature detection tool originally developed to extract and classify brightness fronts in RADARSAT-2 (R2) SAR imagery has been updated to extract and classify brightness fronts from S1 ground range-detected products. Results indicate that the features extracted from S1 datasets are largely consistent with those derived from R2. However, more features are extracted from S1 data and, in limited examples, there are differences in how features are classified. In addition, S1 radial velocity (RVL) products were compared with simulated RVL products created from modeled ocean current data. This comparison shows that the orientation and magnitude of the Gulf Stream in S1 RVL products is generally consistent with modeled results, but does indicate that some regions of the Gulf Stream (meanders and ocean eddies) are likely to present challenges for automatic extraction algorithms. Taken together, this research shows that S1 data have utility for ocean surface feature extraction and provide an additional dataset that can be exploited to expand ocean feature analysis over Canadian waters.