Image Processing Vision Research Articles

Logistics Information Traceability Mechanism of Fresh E-Commerce Based on Image Recognition Technology.

Logistics migration and movement require precise information updates for traceability and visibility of goods through E-commerce platforms. Computer vision and digital image processing techniques are used for visual identification and tracking through different warehouses and delivery points. In this article, an incessant visualized tracking scheme (IVTS) is designed for identifying and tracking E-commerce logistics throughout the migration points. This scheme endorsed computer vision technology for logistics recognition and labelled data detection. In this scheme, the labelled logistics data is verified for its similarity in different migrating locations and to the endpoint. Based on the dimensional features and regional-pixel similarity factor, it is verified using the deep neural network. This learning process identifies dimensional variations due to logistics displacement and position suppressing the similarity variations. It is performed based on the migration and information available to prevent tracking errors. For the varying locations and logistics displacement, the error pixel regions are identified and trained for possible similarity detection. The proposed scheme effectively improves visual accuracy, tracking maximization, and logistics detection by reducing dimensional errors.

Effect of Graphene Oxide on Aging Properties of Polyurethane-SBS Modified Asphalt and Asphalt Mixture.

In order to clarify the effect of the new nano-material graphene oxide on the performance of Polyurethane-SBS modified asphalt and asphalt mixture under the effect of thermal aging, the cracking process of semicircular bending test (SCB) specimens was monitored in situ based on computer vision image processing technology (OpenCV), and the modified asphalt and the cracking characteristics of the modified asphalt and mixture were further analyzed by the tests of semicircular three-point bending and aggregate contact angle measurement. The test results showed that the thermal aging effect severely damaged the composite structure formed by the cross-linking effect of Polyurethane and SBS modifier in asphalt, which intensified the degradation of Polyurethane and SBS modifier and led to great changes in the rheological properties of asphalt after aging. However, the incorporation of the new nanomaterial Graphene oxide can slow down the degradation of Polyurethane and SBS modifiers and the change of asphalt cross-linked composite structure, making the anti-cracking and anti-aging properties of Graphene oxide-Polyurethane-SBS modified asphalt mixes better than those of Polyurethane-SBS modified asphalt mixes. Therefore, the new nano-material graphene oxide added to Polyurethane-SBS modified asphalt is meaningful and feasible. Graphene oxide-polyurethane-sbs composite modified asphalt, as a new nano-material modified asphalt, is stronger against the ultraviolet and light asphalt that is prone to aging. With regards to improving the application of road projects, the results are very promising.

Landscape Ecological Construction and Spatial Pattern Optimization Design Based on Genetic Algorithm Optimization Neural Network

With the development of computer visual information processing technology, designers use visual image processing methods to analyze and extract the features of landscape ecological space environment, establish visual reconstruction models of landscape ecological construction and spatial pattern optimization design images, and improve their ability to identify and reconstruct the artistic features of landscape ecological space environment. In this paper, a landscape ecological construction and spatial pattern optimization design scheme based on NN and GA is put forward. In the process of image visual reconstruction of landscape ecological construction and spatial pattern optimization design, urban ecological landscape information fusion perception and block area template matching are carried out to realize urban ecological landscape design. Finally, simulation test and analysis are carried out. The results show that the accuracy of this algorithm is high, which is 9.34% higher than that of traditional GA. This method can effectively reduce the iteration times and running time of the algorithm and improve the efficiency of landscape space optimization design. Carrying out the research on ecological planning and design with landscape ecological protection and restoration as the core has important practical guiding significance for guiding all localities to construct landscape ecological security pattern and promote green urbanization.

Relation of local window size in a model of modules with estimation of the size of visual images and their segmentation

Subject of study. The fundamental mechanisms of visual image processing, such as size estimation and segmentation, are considered. The possibility of describing these mechanisms using the model of modules proposed by V. D. Glezer and based on electrophysiological data from studies of the receptive fields of neurons in the visual cortex was investigated. Aim of study. This study aimed to investigate mechanisms of size estimation and the segmentation processes by comparing the experimental data obtained by the authors with modeling results. Methods. Psychophysical methods were used for the experimental part. Two different paradigms were used for size estimation: a modified Ebbinghaus illusion with different surroundings of the test stimuli or a comparison of the sizes of upright and oblique crosses previously used in experiments on segmentation. Spatial-frequency filtering of images in local areas of the visual field was performed during modeling using a finite set of filters. Main results. The dependence of the size estimate on the distance between images and on their shape and the relationship of those estimates with segmentation was demonstrated for the first time to our knowledge. Another new result shows that images with different shapes are perceived as equal in size if the modules that optimally describe these images (i.e., maximum energy is preserved in images when filtered with a limited number of filters) are of equal sizes. The obtained results demonstrate that the model of modules can describe to a first approximation the mechanisms performing the estimation of the sizes of the images and their segmentation. Correspondence between the data of neurophysiological, psychophysical, and modeling investigations was shown for the first time to our knowledge. The conclusions were supported by a comparison of the experimental results, and the modeling, with patterns of traditional Byzatntine icon paintings, as well as Russian avant-garde paintings of the early 20th century, which were inspired by the former. Practical significance. Application and further enhancement of the model of modules as an artificial neural network that ensures the segmentation, image size estimation, and recognition of visual objects can be of practical significance.

DEVELOPMENT OF AUTONOMOUS UNDERWATER VEHICLE (AUV) BASED ON ROBOTIC OPERATING SYSTEM FOR FOLLOWING UNDERWATER CABLE

In addition to satellites, the Marine Cable Communication Channel (SKKL) which is located under the sea is also one of the backbones of the communication network to connect from one island to another. However, there are often irresponsible parties who damage or commit acts of vandalism. This action resulted in the disruption of the communication process on the submarine cable network. So, it is necessary to periodically check the condition of the underwater communication network cables. Regular checking of underwater cables is very risky, so we propose an underwater robot to handle it. This paper presents the development of Autonomous Underwater Vehicle (AUV) based on Robotic Operating System (ROS) for Following Underwater Cable to monitor the condition of the cables automatically. The AUV is equipped with a camera, Nvidia Jetson Nano, Arduino, Flight Controller, ESC, and brushless DC motors that used to assist the tracking process on the cable. The camera is used as the main visual sensor. Visual image processing methods are carried out using thresholding and contours detection methods, then the obtained data are processed to drive the motors on the AUV so that they can move on the direction of the cable. The experiment results show that the object detection method can be used under conditions with light intensity more than 25 lux. It works optimally at speeds of 0.27 m/s to 0.42 m/s. In the horizontal motion control test, the overshoot parameter value is ±60%, rise time is 2s, settling time is 16s, and steady state error is ±20%. The AUV can track on a straight and winding path of 2 meters with a bright light intensity of ±493 lux, a dim light of ±107 lux and a dark light intensity of ±25 lux with the help of an LED beam with a light intensity of ±773 lux. The percentage of success of scoping experiments on a straight track and a winding track with three trials is 75%. This performance shows that the developed AUV works well to follow underwater cable.

Robust Detection Model of Vascular Landmarks for Retinal Image Registration: A Two-Stage Convolutional Neural Network.

Registration is useful for image processing in computer vision. It can be applied to retinal images and provide support for ophthalmologists in tracking disease progression and monitoring therapeutic responses. This study proposed a robust detection model of vascular landmarks to improve the performance of retinal image registration. The proposed model consists of a two-stage convolutional neural network, in which one segments the retinal vessels on a pair of images, and the other detects junction points from the vessel segmentation image. Information obtained from the model was utilized for the registration. The keypoints were extracted based on the acquired vascular landmark points, and the orientation features were calculated as descriptors. Then, the reference and sensed images were registered by matching keypoints using a homography matrix and random sample consensus algorithm. The proposed method was evaluated on five databases and seven evaluation metrics to verify both clinical effectiveness and robustness. The results established that the proposed method showed outstanding performance for registration compared with other state-of-the-art methods. In particular, the high and significantly improved registration results were identified on FIRE database with area under the curve (AUC) of 0.988, 0.511, and 0.803 in S, P, and A classes. Furthermore, the proposed method worked well on poor quality and multimodal datasets demonstrating an ability to achieve high AUC above 0.8.

The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO)

The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform enabling it to operate in an environment autonomously, recognize the objects, and avoid obstacles in its path. In this study, an open-source ground robot called SROBO was designed to accurately identify its position and navigate certain areas using a deep convolutional neural network and transfer learning. The framework uses an RGB-D MYNTEYE camera, a 2D laser scanner and inertial measurement units (IMU) operating through an embedded system capable of deep learning. The real-time decision-making process and experiments were conducted while the onboard signal processing and image capturing system enabled continuous information analysis. State-of-the-art Real-Time Graph-Based SLAM (RTAB-Map) was adopted to create a map of indoor environments while benefiting from deep convolutional neural network (Deep-CNN) capability. Enforcing Deep-CNN improved the performance quality of the RTAB-Map SLAM. The proposed setting equipped the robot with more insight into its surroundings. The robustness of the SROBO increased by 35% using the proposed system compared to the conventional RTAB-Map SLAM.

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level.

As compared with the traditional visual discrimination methods, deep learning and image processing methods have the ability to detect plants efficiently and non-invasively. This is of great significance in the diagnosis and breeding of plant disease resistance phenotypes. Currently, the studies on plant diseases and pest stresses mainly focus on a leaf scale. There are only a few works regarding the stress detection at a complex canopy scale. In this work, three tea plant stresses with similar symptoms that cause a severe threat to the yield and quality of tea gardens, including the tea green leafhopper [Empoasca (Matsumurasca) onukii Matsuda], anthracnose (Gloeosporium theae-sinensis Miyake), and sunburn (disease-like stress), are evaluated. In this work, a stress detection and segmentation method by fusing deep learning and image processing techniques at a canopy scale is proposed. First, a specified Faster RCNN algorithm is proposed for stress detection of tea plants at a canopy scale. After obtaining the stress detection boxes, a new feature, i.e., RGReLU, is proposed for the segmentation of tea plant stress scabs. Finally, the detection model at the canopy scale is transferred to a field scale by using unmanned aerial vehicle (UAV) images. The results show that the proposed method effectively achieves canopy-scale stress adaptive segmentation and outputs the scab type and corresponding damage ratio. The mean average precision (mAP) of the object detection reaches 76.07%, and the overall accuracy of the scab segmentation reaches 88.85%. In addition, the results also show that the proposed method has a strong generalization ability, and the model can be migrated and deployed to UAV scenarios. By fusing deep learning and image processing technology, the fine and quantitative results of canopy-scale stress monitoring can provide support for a wide range of scouting of tea garden.

Detection of Skin Cancer Based on Image Processing using Machine Learning

Early recognition of malignant melanoma is critical for effective therapy. Melanoma, of the many basal cell carcinomas, is now widely known as the most harmful because it will spread to the parts of the body if it is not identified and given treatment in early stage. Medical computer vision or medical image processing, which is non-invasive, is becoming increasingly important in the clinical diagnosis of many disorders. Melanoma diagnosis is done using both clinical and automated methods. Early detection of malignant melanoma has a lot of potential using image-based computer-aided diagnosis methods. Automatically identifying the type of skin cancer from photos can help with quick diagnosis and increased accuracy, saving time. Using machine learning and image processing techniques, this project will detect and classify types of skin cancer. Dermoscopic images are used as feed in the pre-processing step.

EARLY PEST DETECTION FROM CROP USING IMAGE PROCESSING AND COMPUTATIONAL INTELLIGENCE

Early pest detection is a major challenge in agriculture field. The easiest way, to control the pest infection is the use of pesticides. But the excessive use of pesticides are harmful to plants, animals as well as human beings. Integrated pest management combines biological and physical methods to prevent pest infection. The techniques of machine vision and digital image Processing are extensively applied to agricultural science and it have great perspective especially in the plant protection field, which ultimately leads to crops management. This paper deals with a new type of early detection of pest’s system. Images of the leaves affected by pests are acquired by using a digital camera. The leaves with pest images are processed for getting a gray colored image and then using feature extraction, image classification techniques to detect pests on leaves. The images are acquired by using a digital camera. The images are then transferred to a PC and represented in python software. The RGB image is then converted into gray scale image and the feature extraction techniques are applied on that image. The Support Vector Machine classifier is used to classify the pest types. Keywords- Parking Slot, Deep Learning, Automated Parking, CNN, Mask R-CNN, YOLO, Image Processing

Framework Design of Sports Image Analysis System Based on Player Behavior Information

Obtaining the data and information urgently needed by coaches and athletes through the processing of visual continuous images of sports is the most effective means and method in the current sport’s technical analysis. The sports image photography framework is an important tool for transforming the two-dimensional coordinates of sports technology images into three-dimensional space coordinates. The article proposes a calibration algorithm suitable for the two-plane calibration frame. The calculation is simpler than the DLT algorithm, and it is easy to program. The experimental results show that the system has good tracking effect of moving image key frames, high comprehensiveness, low redundancy and high recall rate.

Development of Feature Extraction Technique

A problem of personal verification and identification is an actively growing area of research in pattern recognition, computer vision, and digital image processing. A Face acknowledgment system is largely significant move toward in our life. Face acknowledgment method must be proficient to robotically identify images of face. This system mainly used to recognize a person and make available a protection during various features of our life. It is extremely complicated job for investigator to obtain mainly excellent face acknowledgment velocity in a variety of circumstances and benchmarks. Face recognition is grassland of computer visualization to uses faces to recognize or authenticate a human being. Principal Component Analysis (PCA) is accomplished and used for feature extraction and measurement lessening. The feature extraction is used to reduce the dimension of the face space by transforming it into feature representation. Features may be symbolic, numerical or both. The symbolic feature is color and numerical feature is weight. The combined feature extraction of PCA, LDA and Wavelet are used in proposed feature extraction algorithm for human face recognition system. The structure is tested and achieves high recognition rates. Information regarding individuals was stored in a database.

Long‐term automated visual monitoring of Antarctic benthic fauna

Abstract The rapid changes in the climate of Antarctica are likely to pose challenges to living communities, which makes monitoring of Antarctic fauna an urgent necessity. Benthos is particularly difficult to monitor, and is sensitive to local environmental changes. At the same time, long‐term monitoring is complicated by logistical factors. It is therefore urgent to develop advanced instruments to set up autonomous and long‐term monitoring programmes to obtain the lacking biological knowledge needed to understand this complex and remote marine environment. We present a pilot study to set up a non‐invasive and sustainable autonomous monitoring activity in Antarctica, leveraging on a specifically designed automated camera recording, computer vision and machine learning image processing techniques. We also present and analyse the high‐resolution image dataset acquired for an extended period of time encompassing both the summer and the Antarctic night and the corresponding transition periods. The results of this study demonstrate both the effectiveness of such an autonomous imaging devices for acquiring relevant long‐term visual data and the effectiveness of the proposed image analysis algorithms for extracting relevant scientific knowledge from such data. The presented results show how the extracted knowledge discloses dynamics of the observed ecosystems that can be obtained only through continuous observations extended in time, not achievable with the state‐of‐the‐art monitoring approaches commonly implemented in Antarctica. The success of this pilot study is a step towards the collection of continuous data near shore in Antarctic areas and in general in all the remote and extreme underwater habitats. Moreover, the presented stand‐alone and autonomous imaging device can be used for increasing the number of the monitoring sites in remote environments and when complemented with the acquisition of physical and bio‐chemical variables it can be used for obtaining data collections of great scientific value difficult to acquire otherwise.

Covering, corner-searching and occupying: A three-stage intelligent algorithm for the 2d multishape part packing problem.

The bin packing problem has a wide range of applications in industry. With the upgrade of the task difficulty, the traditional 2d rectangular layout algorithm can no longer meet the needs of modern industry, such as express packing task and exoplanet ore collection task. The express or ore samples come in heterogeneous shapes so they cannot all be treated as rectangular pieces. In this paper, we propose a three-stage method called covering, corner-searching and occupying (C,S&O) to solve the two-dimensional multishape part packing problem. The objective of the packing problem variant is to ensure maximum use of the raw material and minimize the trim loss. The algorithm cannot make use of information about the sequence of future objects that are going to arrive, only knowing the shape and size of the coming one, and the coming part must be packed into the bin immediately after its arrival without buffering or readjusting. The method of C,S&O hybridizes the idea of “gold corners, silver edges and grass belly” in the Chinese game Go and the method of finding picture corners in machine vision. In the first stage, the rectangular bin and the coming part are transformed into matrix representation, and generating the position matrix that indicates possible ways of packing the part into the bin. In the second stage, the suitable layout position of the coming part is obtained using machine vision image processing technology for reference. The third stage is calculating the environment matching degree to determine the current optimal placement orientation. In order to facilitate the display of the simulation results, only three shapes of parts are considered in the simulation, rectangle, circle and triangle. The experimental results show the effectiveness of this method. Consulting the literature, it is found that this paper is the first to propose a layout method for multishape manufacturing parts.

Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible

Replacing electrons with photons is a compelling route toward high-speed, massively parallel, and low-power artificial intelligence computing. Recently, diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical transformations. However, the existing architectures often comprise bulky components and, most critically, they cannot mimic the human brain for multitasking. Here, we demonstrate a multi-skilled diffractive neural network based on a metasurface device, which can perform on-chip multi-channel sensing and multitasking in the visible. The polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items. The areal density of the artificial neurons can reach up to 6.25 × 106 mm−2 multiplied by the number of channels. The metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor, providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision, autonomous driving, and precision medicine.

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.

Identifying evolving leading edge erosion by tracking clusters of lift coefficients

This work proposes an approach to identify Leading Edge Erosion (LEE) of a wind turbine blade by tracking evolving and emerging clusters of lift coefficients CL time-series signals under uncertain inflow conditions. Most diagnostic techniques today rely on direct visual inspection, image processing, and statistical analysis, e.g. data mining or regression on SCADA output signals. We claim that probabilistic multivariate spatio-temporal techniques could play an eminent role in the diagnostics of LEE specifically leveraging CL time-series signals form multiple sections along the span of the blade. The proposed method extracts clusters’ features based on Variational Bayesian Gaussian Mixture Models (VBGMM) and tracks their spatial and temporal changes, as well as interpret the evolution of the clusters through prior physics-based assumptions. The parameters of the VBGMM are the mean, the eigenvalues and eigenvectors of the covariance matrix, and the angle of orientation of the eigenvectors. We show that the distribution of the CL data may not show statistically separable clusters, however, the parameters of the VBGMM clusters fitted to the CL data, allows to discriminate moving clusters primarily due to varying inflow and operating conditions, versus emerging clusters primarily due to evolving severity of the blade LEE.

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

In recent years, with the development of computer image processing technology and quantitative metallography, quantitative metallographic analysis and image restoration based on machine vision image processing have attracted extensive attention. To deeply study the special ability of machine vision technology in metallographic image restoration and quantitative analysis, we adopt a model construction method, parameter design method and comparative analysis method to collect samples and analyze the machine vision system. Through quantitative metallographic organization, based on the progress in artificial neural networks and classification algorithms, a set of analysis system equipment and image restoration algorithm was finally established. The experimental results show that the recognition rate of this method is 5.69% higher than that of method 3, and the overall recognition rate is 5.1% higher than that of method 3, reaching 98.65%. It basically proves the superiority of the image classification algorithm in this paper and also indirectly proves that the system can play a role in the restoration and quantitative analysis of metallographic tissue images.

Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction in detail. By analyzing the trajectory data under the constraints of the road network, the spatiotemporal characteristics of the existing trajectory clustering methods, and the deficiencies of the existing trajectory clustering methods, a new trajectory clustering method based on trajectory segmentation and spatiotemporal similarity measurement is implemented. A motion capture and reconstruction method for basketball training based on visual image K-means clustering algorithm is proposed. Multiresolution frame scanning technology is used to collect machine images of basketball training movements, and edge contour processing is performed on the collected high-resolution basketball training movement images. Feature detection uses the three-dimensional model reconstruction method to segment the basketball training action area and combines the irregular triangle network model to realize the machine vision block template matching processing of basketball training actions and capture the basketball training action in the Gaussian fuzzy affine space. In time and feature extraction, wavelet lifting technology is used to identify the ambiguity of basketball training movements, image enhancement technology is used to improve the resolution and adaptability of basketball training movement capture, and machine vision image processing methods are used to achieve basketball training movement capture optimization. The simulation results show that the method has better adaptability and higher feature recognition ability for basketball training motion capture and improves the feature extraction and adaptive capture reconstruction ability of basketball training motion.

Deep learning based approach for the instance segmentation of clayey soil desiccation cracks

The identification of clayey soil desiccation cracks is an important practical issue in geotechnical engineering and engineering geology. The desiccation cracks can dramatically increase the hydraulic conductivity and deteriorate the mechanical performances of clayey soils. Traditionally, the analysis of soil desiccation cracks relies on visual inspection and image processing techniques, which lack automation and intelligence. Therefore, there is an increasing need for an automated algorithm to meet accuracy and efficiency requirements for various engineering scenarios. In this study, a state-of-the-art deep-learning algorithm, Mask R-CNN, was utilized for the clayey soil crack detection, localization and segmentation. A comprehensive dataset including 1200 annotated crack images of 256 × 256 resolution was prepared for the algorithm training and validation. The proposed Mask R-CNN algorithm achieved precision, recall and F1 score of 73.29%, 82.76% and 77.74%, respectively. Besides, the algorithm gained a mean localization accuracy (APbb) of 64.14% and a mean segmentation accuracy (APm) of 47.59%. The detection performance of the Mask R-CNN was also compared with that of the U-Net under three different scenarios. The test results have demonstrated the superiority of the Mask R-CNN over the U-Net algorithm in crack detection, localization and segmentation.