Fittings Detection Method Based on Multi-Scale Geometric Transformation and Attention-Masking Mechanism.

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings' scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings.

[Research progress on medical image dataset expansion methods].

Computer-aided diagnosis (CAD) systems play a very important role in modern medical diagnosis and treatment systems, but their performance is limited by training samples. However, the training samples are affected by factors such as imaging cost, labeling cost and involving patient privacy, resulting in insufficient diversity of training images and difficulty in data obtaining. Therefore, how to efficiently and cost-effectively augment existing medical image datasets has become a research hotspot. In this paper, the research progress on medical image dataset expansion methods is reviewed based on relevant literatures at home and abroad. First, the expansion methods based on geometric transformation and generative adversarial networks are compared and analyzed, and then improvement of the augmentation methods based on generative adversarial networks are emphasized. Finally, some urgent problems in the field of medical image dataset expansion are discussed and the future development trend is prospected.

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data Based on the GRNN-SGTM Ensemble.

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values ​​in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

Faster R-CNN and Geometric Transformation-Based Detection of Driver's Eyes Using Multiple Near-Infrared Camera Sensors.

Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for gaze tracking cannot be observed well in the camera input image owing to the turning of the driver's head during driving. To solve this problem, existing studies have used multiple-camera-based methods to obtain images to track the driver's gaze. However, this method has the drawback of an excessive computation process and processing time, as it involves detecting the eyes and extracting the features of all images obtained from multiple cameras. This makes it difficult to implement it in an actual vehicle environment. To solve these limitations of existing studies, this study proposes a method that uses a shallow convolutional neural network (CNN) for the images of the driver's face acquired from two cameras to adaptively select camera images more suitable for detecting eye position; faster R-CNN is applied to the selected driver images, and after the driver's eyes are detected, the eye positions of the camera image of the other side are mapped through a geometric transformation matrix. Experiments were conducted using the self-built Dongguk Dual Camera-based Driver Database (DDCD-DB1) including the images of 26 participants acquired from inside a vehicle and the Columbia Gaze Data Set (CAVE-DB) open database. The results confirmed that the performance of the proposed method is superior to those of the existing methods.

Reconstruction of Cylindrical Surfaces Using Digital Image Correlation.

A technique for the reconstruction of cylindrical surfaces using optical images with an extension of least squares matching is presented. This technique is based on stereo-image acquisition of a cylindrical object, and it involves displacing the camera following the object length. The basic concept behind this technique is that variations in the camera viewpoint over a cylindrical object produce perspective effects similar to a conic section in an image sequence. Such parallax changes are continuous and can be modelled by a second-order function, which is combined with an adaptive least squares matching (ALSM) for the 3D object reconstruction. Using this concept, a photogrammetric intersection with only two image patches can be used to model a cylindrical object with high accuracy. Experiments were conducted with a cylinder on a panel with coded targets to assess the 3D reconstruction accuracy. The accuracy assessment was based on a comparison between the estimated diameter and the diameter directly measured over the cylinder. The difference between the diameters indicated an accuracy of 1/10 mm, and the cylindrical surface was entirely reconstructed.

Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations.

PURPOSE: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices. METHODS: Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression. RESULTS: The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R2: 0.023-0.04, P > 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and Dmean (R2: 0.689-0.988), 80% of which were P < 0.001. For the random errors, the correlations obtained by the all targets were R2 > 0.384, P < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with P < 0.05. CONCLUSIONS: Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.

Nectar Guide Patterns on Developmentally Homologous Regions of the Subtribe Ligeriinae (Gesneriaceae).

Homology is a crucial concept that should be considered while conducting a comparative analysis between organisms. In particular, in the subtribe Ligeriinae, the nectar guide pattern is associated with high diversity in petal shapes and sizes. This largely limits researchers to exclusively examining the interspecific variation in nectar guide patterns on the developmentally homologous region. Thus, to solve this problem, we proposed an approach for defining a homologous region of interest (ROI) that aligns the petal image between specimens based on petal contours and vasculatures. We identified petal contours and vasculatures from the fresh petal image and its histological image through image processing. The homologous ROI was subsequently obtained by applying geometric transformation to the contour and vasculature. The qualification and quantification of nectar guide patterns were subsequently performed based on the homologous ROI. Four patterning modes, namely vascular, random, distal, and proximal, were defined for the qualitative analysis of nectar guide patterns. In the quantitative analysis, principal component (PC) analysis was applied to homologous ROIs, and the PC score of each specimen served as the trait values of nectar guide patterns. The results of the two analyses coincided, and both showed significant associations between nectar guide patterns and pollination types. The proximal mode (corresponding to PC1) and distal mode (corresponding to PC2) together showed the strongest association with pollination types. Species exhibiting the hummingbird and bee pollination types tended to recruit the distal and proximal modes, respectively. Our study conducted a comparative analysis of nectar guide patterns on the developmentally homologous region and provided a comprehensive view of the variation in the nectar guide patterns of Ligeriinae.

Plasma Amine Oxidase-Induced Nanoparticle-to-Nanofiber Geometric Transformation of an Amphiphilic Peptide for Drug Encapsulation and Enhanced Bactericidal Activity.

Patients with cancer have reduced immune function and are susceptible to bacterial infection after surgery, chemotherapy, or radiotherapy. Spherical nanoparticles formed by the self-assembled peptide V6K3 can be used as carriers for poorly soluble antitumor drugs to effectively deliver drugs into tumor cells. V6K3 was designed to achieve nanoparticle-to-nanofiber geometric transformation under induction by plasma amine oxidase (PAO). PAO is commercially available and functionally similar to lysyl oxidase (LO), which is widely present in serum. After the addition of fetal bovine serum (FBS) or PAO, the secondary structure of the peptide changed, while the spherical nanoparticles stretched and transformed into nanofibers. The conversion of the self-assembled morphology reveals the susceptibility of this amphiphilic peptide to subtle chemical modifications and may lead to promising strategies to control self-assembled architecture via enzyme induction. Enzymatically self-assembled V6K3 had bactericidal properties after PAO addition that were surprisingly superior to those before PAO addition, enabling this peptide to be used to prevent infection. The amphiphilic peptide V6K3 displayed antitumor properties and low toxicity in mammalian cells, demonstrating good biocompatibility, as well as bactericidal properties, to prevent bacterial contamination. These advantages indicate that enzymatically self-assembled V6K3 has great biomedical application potential in cancer therapy.

Radio-morphology: Parametric shape-based features in radiotherapy.

PURPOSE: In radiotherapy, it is necessary to characterize dose over the patient anatomy to target areas and organs at risk. Current tools provide methods to describe dose in terms of percentage of volume and magnitude of dose, but are limited by assumptions of anatomical homogeneity within a region of interest (ROI) and provide a non-spatially aware description of dose. A practice termed radio-morphology is proposed as a method to apply anatomical knowledge to parametrically derive new shapes and substructures from a normalized set of anatomy, ensuring consistently identifiable spatially aware features of the dose across a patient set. METHODS: Radio-morphologic (RM) features are derived from a three-step procedure: anatomy normalization, shape transformation, and dose calculation. Predefined ROI's are mapped to a common anatomy, a series of geometric transformations are applied to create new structures, and dose is overlaid to the new images to extract dosimetric features; this feature computation pipeline characterizes patient treatment with greater anatomic specificity than current methods. RESULTS: Examples of applications of this framework to derive structures include concentric shells based around expansions and contractions of the parotid glands, separation of the esophagus into slices along the z-axis, and creating radial sectors to approximate neurovascular bundles surrounding the prostate. Compared to organ-level dose-volume histograms (DVHs), using derived RM structures permits a greater level of control over the shapes and anatomical regions that are studied and ensures that all new structures are consistently identified. Using machine learning methods, these derived dose features can help uncover dose dependencies of inter- and intra-organ regions. Voxel-based and shape-based analysis of the parotid and submandibular glands identified regions that were predictive of the development of high-grade xerostomia (CTCAE grade 2 or greater) at 3-6 months post treatment. CONCLUSIONS: Radio-morphology is a valuable data mining tool that approaches radiotherapy data in a new way, improving the study of radiotherapy to potentially improve prognostic and predictive accuracy. Further applications of this methodology include the use of parametrically derived sub-volumes to drive radiotherapy treatment planning.

A new method for CT dose estimation by determining patient water equivalent diameter from localizer radiographs: Geometric transformation and calibration methods using readily available phantoms.

PURPOSE: Water equivalent diameter (Dw) reflects patient's attenuation and is a sound descriptor of patient size, and is used to determine size-specific dose estimator from a CT examination. Calculating Dw from CT localizer radiographs makes it possible to utilize Dw before actual scans and minimizes truncation errors due to limited reconstructed fields of view. One obstacle preventing the user community from implementing this useful tool is the necessity to calibrate localizer pixel values so as to represent water equivalent attenuation. We report a practical method to ease this calibration process. METHODS: Dw is calculated from water equivalent area (Aw) which is deduced from the average localizer pixel value (LPV) of the line(s) in the localizer radiograph that correspond(s) to the axial image. The calibration process is conducted to establish the relationship between Aw and LPV. Localizer and axial images were acquired from phantoms of different total attenuation. We developed a program that automates the geometrical association between axial images and localizer lines and manages the measurements of Dw and average pixel values. We tested the calibration method on three CT scanners: a GE CT750HD, a Siemens Definition AS, and a Toshiba Acquilion Prime80, for both posterior-anterior (PA) and lateral (LAT) localizer directions (for all CTs) and with different localizer filters (for the Toshiba CT). RESULTS: The computer program was able to correctly perform the geometrical association between corresponding axial images and localizer lines. Linear relationships between Aw and LPV were observed (with R2 all greater than 0.998) on all tested conditions, regardless of the direction and image filters used on the localizer radiographs. When comparing LAT and PA directions with the same image filter and for the same scanner, the slope values were close (maximum difference of 0.02 mm), and the intercept values showed larger deviations (maximum difference of 2.8 mm). Water equivalent diameter estimation on phantoms and patients demonstrated high accuracy of the calibration: percentage difference between Dw from axial images and localizers was below 2%. With five clinical chest examinations and five abdominal-pelvic examinations of varying patient sizes, the maximum percentage difference was approximately 5%. CONCLUSIONS: Our study showed that Aw and LPV are highly correlated, providing enough evidence to allow for the Dw determination once the experimental calibration process is established.

A depth estimation method based on geometric transformation for stereo light microscope.

Stereo light microscopes (SLM) with narrow vision and shallow depth of field are widely used in micro-domain research. In this paper, we propose a depth estimation method of micro objects based on geometric transformation. By analyzing the optical imaging geometry, the definition of geometric transformation distance is given and the depth-distance relation express is obtained. The parameters of geometric transformation and express are calibrated with calibration board images captured in aid of precise motorized stage. The depth of micro object can be estimated by calculating the geometric transformation distance. The proposed depth-distance relation express is verified using an experiment in which the depth map of an Olanzapine tablet surface is reconstructed.