Contour Recognition Research Articles

GMAlignNet: multi-scale lightweight brain tumor image segmentation with enhanced semantic information consistency

Although the U-shaped architecture, represented by UNet, has become a major network model for brain tumor segmentation, the repeated convolution and sampling operations can easily lead to the loss of crucial information. Additionally, directly fusing features from different levels without distinction can easily result in feature misalignment, affecting segmentation accuracy. On the other hand, traditional convolutional blocks used for feature extraction cannot capture the abundant multi-scale information present in brain tumor images. This paper proposes a multi-scale feature-aligned segmentation model called GMAlignNet that fully utilizes Ghost convolution to solve these problems. Ghost hierarchical decoupled fusion unit and Ghost hierarchical decoupled unit are used instead of standard convolutions in the encoding and decoding paths. This transformation replaces the holistic learning of volume structures by traditional convolutional blocks with multi-level learning on a specific view, facilitating the acquisition of abundant multi-scale contextual information through low-cost operations. Furthermore, a feature alignment unit is proposed that can utilize semantic information flow to guide the recovery of upsampled features. It performs pixel-level semantic information correction on misaligned features due to feature fusion. The proposed method is also employed to optimize three classic networks, namely DMFNet, HDCNet, and 3D UNet, demonstrating its effectiveness in automatic brain tumor segmentation. The proposed network model was applied to the BraTS 2018 dataset, and the results indicate that the proposed GMAlignNet achieved Dice coefficients of 81.65%, 90.07%, and 85.16% for enhancing tumor, whole tumor, and tumor core segmentation, respectively. Moreover, with only 0.29 M parameters and 26.88G FLOPs, it demonstrates better potential in terms of computational efficiency and possesses the advantages of lightweight. Extensive experiments on the BraTS 2018, BraTS 2019, and BraTS 2020 datasets suggest that the proposed model exhibits better potential in handling edge details and contour recognition.

Study on Reservoir Characteristics and 3D Spatial Carving of Fault Solution Reservoirs Based on Geophysical Methods.

At present, research studies on the description of fracture characterization elements in fault solution reservoirs are relatively limited, and further research is needed on contour recognition and characterization methods. In this paper, first, the regional fault system is investigated and the faults are finely identified and characterized. Second, the volume of contour-sensitive attributes of the fault solver is optimized using tensor attributes, amplitude variation, discontinuity detection, and other attributes. Finally, a comprehensive evaluation of the fault solution reservoir is carried out by combining the dynamic production characteristics. Results show that (a) the interior details of fractured reservoirs can be mainly divided into two categories: cave-type reservoirs and fracture-pore-type reservoirs. (b) Fractured and porous reservoirs mainly utilize discontinuous properties and combine well data to calibrate and determine threshold values, ultimately achieving the characterization of interior details of fractured solution bodies. (c) After anisotropic diffusion filtering and fault enhancement, the seismic data was subjected to amplitude gradient disorder detection attribute calculation for multiscale fractures.

Automatic Particle Recognition Based on Digital lmage Processing

The purpose of the research is to develop and compare various methods and algorithms for effective particle analysis based on their visual characteristics. Тhe purpose of this article is to develop and compare various methods and algorithms for effective particle analysis based on their visual characteristics. Тhe paper considers two fundamentally different approaches: the analysis of grayscale gradients and the machine learning method.Methods.Тhe research methodology includes the analysis of particle images obtained by precipitation from colloidal solutions after laser ablation and images of powder particles for selective laser melting. Тhe materials were obtained using a Quanta 200 3D electron microscope (FЕ/). For the analysis, threshold brightness binarization, contour recognition methods by the Kenny operator and the Hough algorithm are used to combine boundary points into connected contours. For comparison, the U-Net neural network solution was used, and a dataset generator was created to train the neural network. Hand-cut images of aluminum alloy powder particles and micro and nanoparticles of various metals are used as data for generation.Results.Тhe results of the study show that the Hough method provides recognition of the number of particles at the level of 80%, and the machine learning method achieves 95% accuracy in recognizing the shape of particles. Both methods can be used to analyze microand nanoparticles, including irregularly shaped particles.Conclusion.Тhe findings of the work confirm that neural networks are the optimal solution for automatic particle recognition in digital images. However, in order to create a dataset of sufficient volume, it is necessary to develop a generator of labeled images, which requires a detailed study of the subject area.

Вычислительные диэлектрические метаповерхности в фотонных топологических устройствах обработки многомерных сигналов

A single-layer computational dielectric broadband metasurface (MS) with silicon nanodiscs is proposed to implement spatial differentiation and contour recognition operations. The variety of physical mechanisms of dielectric MSs for performing similar mathematical operations is considered. The necessity of such computational nanostructures for improving the numerous methods of topological texture-fractal processing of signals and fields in modern radio physics and radio electronics is revealed.

A CAD-BEM geometry transformation method for face-based primary geometric input based on closed contour recognition

A CAD-BEM geometry transformation method for face-based primary geometric input based on closed contour recognition

Body circumference fitting model based on coupled linear regression and body type classification

Body circumference fitting model is a key technology for personalization in smart manufacturing by the apparel industry. First, the paper collected front and side images plus manual measurement data of 122 young men. Second, through contour recognition and feature point detection of the collected images, the width and thickness of each body part plus the height were estimated. Then, a correlation analysis of these known factors was conducted. The paper proposed that weight was included in initial regression model for circumferences as an additional independent variable along with width and thickness. Finally, according body type classification, a multivariate regression model was established. After validation, the values predicted by the multivariate regression model were within ±1.5 cm of the manual values for 90.9% of the individuals on average. The paper provides theoretical support for body circumference fitting and may also be beneficial to the development of the smart manufacturing of garments.

Comparative Study on Object-Oriented Identification Methods of Plastic Greenhouses Based on Landsat Operational Land Imager

The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control.

Biomaterial-Based Biomimetic Visual Sensors: Inkjet Patterning of Bacteriorhodopsin.

Biomimetic visual sensors utilizing bacteriorhodopsin (bR) were fabricated by using an inkjet method. The inkjet printer facilitated the jetting of the bR suspension, allowing for the deposition of bR films. The resulting inkjet-printed bR film exhibited time-differential photocurrent response characteristics similar to those of a dip-coated bR film. By adjusting the number of printed bR film layers, the intensity of the photocurrent could be easily controlled. Moreover, the inkjet printing technique enabled unconstrained patterning, facilitating the design of various visual information processing functions, such as visual filters. In this study, we successfully fabricated two visual filters, namely, a two-dimensional Difference of Gaussian (DOG) filter and a Gabor filter. The printed DOG filter demonstrated edge detection capabilities corresponding to contour recognition in visual receptive fields. On the other hand, the printed Gabor filter proved effective in detecting objects of specific sizes as well as their motion and orientation. The integration of bR and the inkjet method holds significant potential for the widespread implementation of highly functional biomaterial-based visual sensors. These sensors have the capability to provide real-time visual information while operating in an energy-efficient manner.

An ionic pressure sensor array with digitizable sensitivity

Flexible pressure sensors play an indispensable role in electronic skin. A flexible pressure sensor array with a larger sensing area is an inevitable trend for further developing a single flexible pressure sensor. However, pressure sensor arrays suffer from a key challenge: each sensing unit in the array cannot be individually defined for its pressure sensing range and sensitivity. Here, we present an ionic pressure sensor array with digitizable sensitivity achieved through the synergistic cooperation of an ion gel shell interfacial capacitive sensing mechanism and a filler with digitally tunable mechanical stiffness. Through this design, the sensitivity of each sensing pixel unit in the array can be digitally defined in the range of 20 times, and the fabrication process is greatly simplified. We fabricated sensor arrays for object contour recognition and weight judgment, demonstrating their potential application in electronic skin. In addition, we proposed using a customized 3D printer of elastomer to simplify its fabrication process, which is beneficial for engineering applications.

P‐2.16: Research on three‐dimensional reconstruction based on VTK

Three‐dimensional reconstruction of medical tomographic images is a technological way to provides a 3D model of biological tissue. As it not only provides a 3D model, but also shows the spatial location of this model, it received widespread attention. By human‐computer interaction, doctors can easily conduct disease analysis and surgical planning.This paper introduces a novel three‐dimensional reconstruction technology of magnetic resonance images, which deals with the brightness, contrast adjustment, and three‐dimensional reconstruction of Dicom images. This paper uses the Visualization Toolkit VTK to implement Dicom 3D image reconstruction. VTK has a large number of callable functions built in and encapsulates some common visualization algorithms. When visualizing medical data, VTK can directly call the related functions. VTK three‐dimensional reconstruction can help people intuitively see the contents of a 3D image. Displaying two‐ dimensional images is easy, but displaying 3D images is not. Previously, because of the limited technical means, even if the data of the 3D image is obtained, it can only be displayed as a slice of the 2D image, which is not intuitive. With the development of computer science and technology, the use of computer graphics technology to model and render 3D objects in real‐time has become the mainstream method of object display, while the past two‐dimensional display method has been gradually phased out.To describe the process of 3D modeling more accurately and intuitively, this paper focuses on a reconstruction technique, polygon drawing. Polygon drawing uses segmentation technology to perform contour recognition, extraction, and other operations on a series of two‐dimensional images, and ultimately restores the three‐dimensional model of the detected object and displays it as a surface.First, the original data is a discrete three‐dimensional spatial rule data field. The images generated by CT and MRI of tomography scanners belong to this data type, that is, the DICOM type image used in this paper. Second, the boundary between the content part and the background part in the three‐dimensional image is found in the unit of volume, and triangles are extracted to fit the boundary. Then, all the voxels are traversed to find out which triangles are finally assembled to form a triangular mesh of the surface of the real point in the image. Finally, a triangle mesh model is built to render the model, and the rendered model is put into the window to interact with users.

Optimized shearing strategy for heavy plate based on contour recognition

Optimized shearing strategy for heavy plate based on contour recognition

Analysis and Measurement of Large Circular Transmitting Arrays for Microwave Power Transmission

Phased arrays have been widely used in microwave power transmission. The efficiency of rectenna is closely related to the received power density. Therefore, it is necessary to accurately analyze the radiation performance of the transmitting array in order to achieve the high-efficiency power transmission. And in order to meet the long-distance and high-efficiency power transmission requirements, the transmitting array needs to have the characteristics of large electricity sizes. The direct numerical analysis method of entire array will bring huge cost in computing time and storage space. Subarray extrapolation is a method to reasonably account for the edge effects of arrays, but only for rectangular arrays. Aiming at the geometric characteristics of the array contour, this communication proposes an irregular contour recognition method. This method can expand the application of subarray extrapolation method and realizes the purpose of accurately evaluating the radiation performance of large array antennas at small calculation cost. Finally, using a large transmitting array as an example, the proposed method is verified by comparing the results of subarray extrapolation, commercial software of entire array, and the experimental measurement to validate the correctness and advancement.

MONITORING OF LAND TRANSFORMATION IN BORDER TERRITORIES USING MATERIALS OF REMOTE SENSING DATA

The article presents the results of the analysis of changes in territories that are bordering to the boundaries of ad-ministrative-territorial units. Conclusions are drawn about the need to timely take into account the ongoing changes in the elements of the natural and ecological framework, as well as types of land that are actively transforming under the influ-ence of anthropogenic factors. The automation of land contour recognition in border areas by means of geographic in-formation systems is proposed. A quantitative assessment of changes in the natural environment objects of border areas is proposed, on the basis of which conclusions are drawn about the need to reduce the interval for adjusting the spatial position of the boundaries of administrative-territorial units.

Development of Weed Detection Method in Soybean Fields Utilizing Improved DeepLabv3+ Platform

Accurately identifying weeds in crop fields is key to achieving selective herbicide spraying. Weed identification is made difficult by the dense distribution of weeds and crops, which makes boundary segmentation at the overlap inaccurate, and thus pixels cannot be correctly classified. To solve this problem, this study proposes a soybean field weed recognition model based on an improved DeepLabv3+ model, which uses a Swin transformer as the feature extraction backbone to enhance the model’s utilization of global information relationships, fuses feature maps of different sizes in the decoding section to enhance the utilization of features of different dimensions, and adds a convolution block attention module (CBAM) after each feature fusion to enhance the model’s utilization of focused information in the feature maps, resulting in a new weed recognition model, Swin-DeepLab. Using this model to identify a dataset containing a large number of densely distributed weedy soybean seedlings, the average intersection ratio reached 91.53%, the accuracy improved by 2.94% compared with that before the improvement with only a 48 ms increase in recognition time, and the accuracy was superior to those of other classical semantic segmentation models. The results showed that the Swin-DeepLab network proposed in this paper can successfully solve the problems of incorrect boundary contour recognition when weeds are densely distributed with crops and incorrect classification when recognition targets overlap, providing a direction for the further application of transformers in weed recognition.

Multicolor high-resolution whole-brain imaging for acquiring and comparing the brain-wide distributions of type-specific and projection-specific neurons with anatomical annotation in the same brain

Visualizing the relationships and interactions among different biological components in the whole brain is crucial to our understanding of brain structures and functions. However, an automatic multicolor whole-brain imaging technique is still lacking. Here, we developed a multicolor wide-field large-volume tomography (multicolor WVT) to simultaneously acquire fluorescent signals in blue, green, and red channels in the whole brain. To facilitate the segmentation of brain regions and anatomical annotation, we used 4′, 6-diamidino-2-phenylindole (DAPI) to provide cytoarchitecture through real-time counterstaining. We optimized the imaging planes and modes of three channels to overcome the axial chromatic aberration of the illumination path and avoid the crosstalk from DAPI to the green channel without the modification of system configuration. We also developed an automatic contour recognition algorithm based on DAPI-staining cytoarchitecture to shorten data acquisition time and reduce data redundancy. To demonstrate the potential of our system in deciphering the relationship of the multiple components of neural circuits, we acquired and quantified the brain-wide distributions of cholinergic neurons and input of ventral Caudoputamen (CP) with the anatomical annotation in the same brain. We further identified the cholinergic type of upstream neurons projecting to CP through the triple-color collocated analysis and quantified its proportions in the two brain-wide distributions. Both accounted for 0.22%, implying CP might be modulated by non-cholinergic neurons. Our method provides a new research tool for studying the different biological components in the same organ and potentially facilitates the understanding of the processing mechanism of neural circuits and other biological activities.

Research on Autonomous Picking Algorithm for Table Tennis Balls based on Machine Vision

The identification, positioning and grabbing path planning of table tennis balls are the preconditions for the robot to pick up table tennis balls autonomously. A method of table tennis identification, positioning and grabbing path planning based on monocular vision image processing is proposed. By analyzing the characteristics of table tennis images, a table tennis image segmentation method based on color and contour features is designed. The color and contour features of table tennis images are extracted, and HSV is used for image preprocessing. Image segmentation is processed by binarization, and Hough circle is used to detect contour recognition, and the segmentation image of table tennis balls is obtained by color combination. Convert the camera imaging coordinates to obtain the pixel position of the table tennis ball in the image. A ball picking path planning based on ant colony algorithm is designed. This paper provides a theoretical reference for the identification, positioning and path planning of autonomous ball picking.

A dual-scale morphological filtering method for composite damage identification using FBP

Delamination is the main type of defect that reduces the performance of carbon fiber-reinforced polymer (CFRP) composite materials and can be detected by traditional linear ultrasonic methods. However, the traditional ultrasonic inspection method only analyzes its position through the waveform change, and it is difficult to intuitively and accurately study the specific shape and position of the defect in the form of images. This study obtains delamination defect images of CFRP composite plates through fan-beam ultrasonic computed tomography (UCT) and finite element simulation. The Filtered back-projection method based on UCT is used as the basic algorithm for reconstructing layered defect images of CFRP plates. To obtain accurate defect size and location, a novel dual-scale morphological filtering (DSMF) method combined with Canny operator is proposed for edge detection of delamination defects. The DSMF method alternately filters by combining two structuring elements of different sizes while suppressing noise as much as possible and preserving the edge details of the image. As the results verify, the use of the DSMF method combined with Canny edge-detection for edge detection significantly reduces the average recognition error from 16.83% to 4.37% compared to existing methods. The results show that the DSMF method can effectively reduce the influence of artifact interference and non-edge pixels on defect-recognition; thus, the size and contour information of the obtained defects are more accurate than existing methods. Experimental studies using realistic composite plates further validated the proposed method. The method of ultrasonic tomography combined with Canny edge detection based on a DSMF method can be well applied to edge detection and contour recognition of layered defects in CFRP materials.

An image processing method for an explosion field fireball based on edge recursion

To overcome the limitations caused by the complex fireball geometry in an explosion field and the smoke interference of fireball contours, a pre-processing strategy for fireball images in an explosion field, combining threshold segmentation, rectangular filtering, nearest-neighbor clustering and morphological processing, as well as an edge-recursive fireball image segmentation algorithm based on the polar coordinate system, is proposed. The overlap rate was used to compare and evaluate the segmentation effect of the proposed method and traditional image-processing methods on the measured explosion fireball image. The results demonstrate that the edge-recursive fireball image segmentation algorithm based on the polar coordinate system is excellent for identifying fireball image segmentation and fireball damage contour recognition under different smoke-obscured conditions in an explosion field, thereby facilitating the accurate recognition and measurement of the fireball image and contour under a smoke plume. This method improves the accuracy of measuring explosive fireball damage power and provides a method for effectively extracting the distribution information of explosive fireball damage.

An ontogenetic study of Eospirigerina putilla (Brachiopoda) surviving the Late Ordovician mass extinction

The ontogeny of the brachiopod can be reflected by the shell outline, size, and their interconnections. As a representative early atrypid brachiopod group of the Edgewood-Cathay Fauna, the genus Eospirigerina survived the Late Ordovician mass extinction (LOME). Abundant E. putilla specimens were collected from the “Wulipo Bed” limestone (upper Hirnantian, Ordovician). As a typical Eospirigerina dominated Edgewood-Cathay Fauna from the upper Hirnantian (Ordovician) of northeastern Yunnan, China, the quantitative ontogenetic study of E. putilla can provide a better understanding of its palaeoecology. Using the image contour recognition, we develop an automated measuring tool, test its robustness, and measure 554 complete and silicified specimens of E. putilla. An abnormal palaeoenvironment that the E. putilla population inhabited in is suggested by the high mortality of small individuals indicated by the size-frequency histogram and survivorship curve. Using quadratic polynomial regression, the negative allometric pattern of the shell length to width in the population is recognized. Using geometric morphometrics with semi-landmarks for 51 specimens from the population, the ontogenic trends are mapped by PCA with thin-plate splines, in which the heterochronic shifts may occur from an elongate shell with relatively larger ventral cardinal area and delthyrium to wider outline and smaller beak. Such heterochronic development is possibly caused by the unstable and stressful environment immediately after the LOME.

Bio-Inspired Models of Convolution Neurons in the Problem of Illusory Contour Recognition

This paper presents the result of designing the architecture of a neural network on bio-inspired neurons, whose task is to work out the mechanism for recognizing an illusory contour using the example of “Kanizsa’s figures”. The neural network made it possible to achieve invariance to the number of corners of the figure and does not lose recognition quality when changing the size of the illusory contour. The main application of the approach can be found in the problem of separating “figure-background” in images.