Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

A novel method for eliminating rotation deviation in sequential images mosaic

To eliminate rotation deviation of sequential images mosaic when measuring linear dimensions of large scale parts with computer vision, a novel algorithm based on the chain code searching method is proposed. After image preprocessing, including image filtering, image segmentation, and edge detection, the chain code length of the contour line can be searched out by the proposed method. Then, the angle from the contour line to the coordinate axis is computed with the length of the contour line. After that, the sequence is rotated in the opposite direction and the rotation deviation is eliminated. It is prepared for the next mosaic of sequences in eliminating shifting deviation. Experiments are carried out on parts with a linear profile rotating angle from 0° to 9°. The results show that compared with the commonly used Hough transform, the new method has higher precision and faster speed, which is important in realizing online high precision measurements of large scale parts with a linear profile.

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

Rotational parameters estimation of maneuvering target in ISAR imaging

The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM （frequency modulation） parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT （Radon-Wigner transform）. The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.

Center of rotation estimation for rocket nozzle by infrared reflective makers and image sequences

The determination of an accurate center of rotation of rocket motor nozzle or other object to be measured is of great interest across a wide range of applications,such as rocket,missile,robotics,industry,spaceflight,aviation and human motion analysis fields,particularly for clinical gait analysis.A new approach was proposed to estimate the moving objects' instantaneous center of rotation and other motion parameters.The new method assumes that the two segment of object to be measured are rigid body which rotates around a center of rotation between each other relatively.The center of rotation varies with time in the global coordinate system but is fixed in the local coordinate system attached to each segment.The models of rocket motor nozzle and its movement were established.The arbitrary moving object's corresponding to motion equations were deduced,and the least square closed-form solutions of the object's motion parameters were figured out.It is assumed that the two high speed CCD cameras mounted on the 750 nm infrared(IR) filter are synchronized and calibrated in advance.The virtual simulation experiment using 3D coordinates of markers was conducted by synchronized stereo image sequences based on 6-DOF motion platform and the experimental results prove the feasibility of our algorithm.The test results show that the precision of x,y,z component on center of rotation is up to 0.14 mm,0.13 mm,0.15 mm.

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

Content-Based Image Retrieval with Feature Extraction and Rotation Invariance

Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.

Rotator cuff disorders:How to write a surgically relevant magnetic resonance imaging report?

Evaluation of rotator cuff is a common indication for magnetic resonance imaging(MRI) scanning of the shoulder. Conventional MRI is the most commonly used technique, while magnetic resonance(MR) arthrogra-phy is reserved for certain cases. Rotator cuff disorders are thought to be caused by a combination of internal and external mechanisms. A well-structured MRI report should comment on the relevant anatomic structures including the acromial type and orientation, the pres-ence of os acromiale, acromio-clavicular degenerative spurs and fluid in the subacromial subdeltoid bursa. In addition, specific injuries of the rotator cuff tendons and the condition of the long head of biceps should be accurately reported. The size and extent of tendon tears, tendon retraction and fatty degeneration or at-rophy of the muscles are all essential components of a surgically relevant MRI report.

Autofocus technique for ISAR imaging of uniformly rotating targets based on the ExCoV method

The inverse synthetic aperture radar (ISAR) imaging can be converted into a sparse reconstruction problem and solved by the l1norm minimization algorithm. The basis matrix in sparse ISAR imaging is usually characterized by the unknown rotation rate of a moving target, thus the rotation rate and the sparse signal should be jointly estimated. Especially due to the imperfect coarse motion compensation, we consider the phase error correction problem in the context of the sparse signal reconstruction. To address this issue, we propose an iterative reweighted method, which jointly estimates the rotation rate, corrects the phase error and reconstructs a high resolution ISAR image. The proposed method gives a gradual and interweaved iterative process to refine the unknown parameters to achieve the best sparse representation for the ISAR signals. Particularly, in ISAR image reconstruction, the l1norm minimization algorithm is sensitive to user parameters. Setting these user parameters are not trivial and the reconstruction performance depends significantly on their choices. Then, we consider an expansion-compression variance-component (ExCoV) based method, which is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. Both numerical and electromagnetic data experiments are implemented to show the effectiveness of the proposed method. It is shown that the proposed method can estimate the rotation rate and correct the phase errors simultaneously, and its superior performance is proved in terms of high resolution ISAR image. © 2017 Beijing Institute of Aerospace Information.

Surgeon-Operated In-Office Ultrasonography for the Diagnosis of Rotator Cuff Tears: A Comparison with Magnetic Resonance Imaging

Objective: Few investigators have evaluated whether ultrasonography operated by a surgeon during a patient’s clinic visit is capable of obtaining a similar degree of accuracy as magnetic resonance imaging in regard to the diagnosis of rotator cuff tears and lesions of the biceps tendon. The purpose of this study was to clarify the accuracy of in-office ultrasonography for the diagnosis of rotator cuff tears in comparison to magnetic resonance imaging. Methods: One hundred and three patients (105 shoulders) with a clinical diagnosis of impingement and suspected rotator cuff tear, who subsequently underwent arthroscopic surgery were retrospectively enrolled in this study, including 7 males with 89 shoulders, and 33 females with 33 shoulders, and their mean age was 60.9 years (range, 30 to 83 years). The subjects were examined using ultrasonography and magnetic resonance imaging within three months pre-operatively per normal practice of the outpatient clinic. The two modalities were then compared to the reference standard, arthroscopic findings. Results: Intra-operatively, 79 full-thickness and 15 partial-thickness rotator cuff tears were found. The agreement between ultrasonography and magnetic resonance imaging for diagnosis of rotator cuff tears was statistically good;observed degree of agreement was 87% with Kappa coefficient of 0.73. Ultrasonography showed a sensitivity of 94% and a specificity of 100% for full-thickness tears, and a sensitivity of 80% and a specificity of 91% for partial-thickness tears. The agreement of the two modalities for diagnosis of lesions of the biceps tendon was also good;observed degree of agreement was 93% with Kappa coefficient of 0.76. In addition, ultrasonography showed comparable accuracy for classifying the size of rotator cuff tears to that of magnetic resonance imaging. Conclusion: Surgeon-operated in-office ultrasonography is an appropriate technique for the assessment of rotator cuff tears with a comparable sensitivity and specificity to that of magnetic resonance imaging.

Aliasing-free high resolution imaging of fast rotating targets with narrowband radar

Narrowband radar has been successfully used for high resolution imaging of fast rotating targets by exploiting their micro-motion features.In some practical situations,however,the target image may suffer from aliasing due to the fixed pulse repetition interval(PRI)of traditional radar scheme.In this work,the random PRI signal associated with compressed sensing(CS)theory was introduced for aliasing reduction to obtain high resolution images of fast rotating targets.To circumvent the large-scale dictionary and high computational complexity problem arising from direct application of CS theory,the low resolution image was firstly generated by applying a modified generalized Radon transform on the time-frequency domain,and then the dictionary was scaled down by random undersampling as well as the atoms extraction according to those strong scattering areas of the low resolution image.The scale-down-dictionary CS(SDD-CS)processing scheme was detailed and simulation results show that the SDD-CS scheme for narrowband radar can achieve preferable images with no aliasing as well as acceptable computational cost.

Target rotation parameter estimation for ISAR imaging via frame processing

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar(ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error.Effectiveness of the proposed method is also confirmed from real ISAR data experiments.

DIRECT VOXEL-PROJECTION FOR VOLUMETRIC DATA RENDERING IN MEDICAL IMAGERY

The volumetric rendering of 3 D medical image data is very effective method for communication about radiological studies to clinicians. Algorithms that produce images with artifacts and inaccuracies are not clinically useful. This paper proposed a direct voxel projection algorithm to implement volumetric data rendering. Using this algorithm, arbitrary volume rotation, transparent and cutaway views are generated satisfactorily. Compared with the existing raytracing methods, it improves the projection image quality greatly. Some experimental results about real medical CT image data demonstrate the advantages and fidelity of the proposed algorithm.

A New Method for Measurements of the Poloidal Rotation Velocities and Wavelength Calibration of X-ray Imaging Crystal Spectrometer in Magnetic Fusion Devices

A new simple method is presented for the wavelength calibration and measurement of poloidal rotation velocities with X-ray imaging crystal spectrometer（XICS）in magnetic fusion devices.In this method,the toroidal rotation of plasma is applied for high precise alignment and wavelength calibration of the poloidal XICS.The measurement threshold of poloidal rotation velocity can be lowered to 1-3 km/s with this method.

MRI诊断肩袖损伤及撕裂程度的临床应用观察

目的:探究MRI在肩袖损伤诊断及撕裂程度的评估中的应用效果。方法:回顾性分析我院183例肩部损伤患者资料,所有患者均行核磁共振成像(MRI)检查,以关节镜检查为金标准,探究MRI对肩袖损伤的诊断价值,对肩袖不同肌腱损伤及撕裂程度的检出情况,分析MRI与关节镜测量肩袖全层撕裂肌腱撕裂尺寸偏差情况。结果:经关节镜检查,183例患者肩袖损伤阳性136例,阴性47例,MRI检查134例阳性,49例阴性,诊断敏感度95.59%(130/136),特异度91.49%(43/47),准确度为94.54%(173/183),阳性预测值为97.01%(130/134),阴性预测值87.76%(43/49),Kappa值为0.859。183例患者关节镜共诊断373处损伤,其中冈上肌腱136处,冈下肌腱122处,肩胛下肌腱115处。MRI对冈上肌腱、冈下肌腱、肩胛下肌腱的检出率显著低于关节镜检查(P<0.05);136例肩袖损伤患者中全层撕裂45例,部分撕裂91例,MRI诊断全层撕裂准确率为93.33%(42/45),部分撕裂为94.51%(86/91),总准确率为94.12%(128/136),显著低于关节镜检查(P<0.05);MRI测量全层撕裂肌腱断端回缩度、撕裂跨度的准确率分别为86.67%(39/45)、82.22%(37/45)。结论:MRI对肩袖损伤及肌腱撕裂程度均具有较高的诊断价值,可为肩袖损伤提供可靠的术前诊断依据,值得在临床推广应用。

基于旋转等变卷积的航拍红外图像目标识别算法

为提高传统无人机红外目标识别算法对输入图像的旋转鲁棒性,提出一种具有旋转等变性的红外图像目标识别算法。参照可见光三通道结构,将红外图像扩张为三通道以丰富输入图像的细节及边缘信息;以旋转等变卷积为基础,设计并实现能够高度保留图像旋转特征的标准旋转等变卷积模块和旋转残差模块,使得所设计模型FC-YOLOv5对图像及图像中目标旋转具有鲁棒性;加入压缩和激励注意力机制自适应地学习到每个通道的重要性,并且根据任务的需要加权调整特征图中的通道贡献,提取重要的特征信息并抑制不重要的特征信息。在航拍行人车辆数据集和海上船舶数据集上验证模型的性能,以基准模型YOLOv5s及常见轻量级目标识别任务所用模型YOLOv8s、NanoDet作为对照组模型。实验结果表明,所提算法的平均精度均值相较于基准模型能够提升2%~4%,且当输入图像具有不同角度的旋转时,能够比对照组模型识别到更多旋转目标,且识别错误更少。

YOLOv5-LR:一种遥感影像旋转目标检测模型

真实遥感图像中,目标呈现任意方向分布的特点,原始YOLOv5网络存在难以准确表达目标的位置和范围、以及检测速度一般的问题。针对上述问题,提出一种遥感影像旋转目标检测模型YOLOv5-Left-Rotation,首先利用Transformer自注意力机制,让模型更加注意感兴趣的目标,并且在图像预处理过程中采用Mosaic数据增强,对后处理过程使用改进后的非极大值抑制算法Non-Maximum Suppression。其次,引入角度损失函数,增加网络的输出维度,得到旋转矩形的预测框。最后,在网络模型的浅层阶段,增加滑动窗口分支,来提高大尺寸遥感稀疏目标的检测效率。实验数据集为自制飞机数据集CASIA-plane78和公开的舰船数据集HRSC2016,结果表明,改进旋转目标检测算法相比于原始YOLOv5网络的平均精度提升了3.175%,在吉林一号某星推扫出的大尺寸多光谱影像中推理速度提升了13.6%,能够尽可能地减少冗余背景信息,更加准确检测出光学遥感图像中排列密集、分布无规律的感兴趣目标的区域。

基于改进YOLOv5的鱼眼图像目标检测算法

针对自动驾驶场景下车载鱼眼相机采集到的图像存在畸变严重、场景复杂、尺度变化剧烈、小目标多以及传统的目标检测模型的检测精度不高的问题,提出了一种基于YOLOv5s改进的鱼眼图像检测模型YOLOv5s-R.首先,为解决小目标难识别的问题,提出随机裁剪多尺度训练的数据增强方法,该方法优于消融实验所得的最优数据增强方法.其次,为了提高模型的检测精度,在网络头部添加置换注意力机制与轻量化解耦头,增强模型对特征的提取能力与识别能力,并抑制噪声干扰.最后,模型额外增加角度预测项,实现旋转框目标检测.通过构建环形标签并用高斯函数对标签平滑,解决了旋转框角度的周期性问题;又对损失函数进行了优化,提出了RIOU,在CIOU的基础上增加角度惩罚项,提高了回归精度并加快了模型的收敛.实验结果表明,提出的YOLOv5s-R模型在WoodScape数据集上取得良好的检测效果,相比于原始的YOLOv5s模型,mAP@0.5、mAP@0.5∶0.95分别提升了6.8%、5.6%,达到82.6%、49.5%.

3T MRI半定量冈上肌脂肪浸润程度与慢性肩袖撕裂的相关性分析

目的 探讨3T MRI半定量冈上肌脂肪浸润程度与慢性肩袖损伤的相关性。方法 选取经手术或关节镜证实的95例慢性肩袖损伤患者,所有患者均行3T MRI检查。MRI冠状位测量肱-肩距离、斜矢状位记录肩峰形态以及采用Goutallier分类改良方法评估冈上肌脂肪浸润程度分级。所有患者依据冈上肌腱损伤程度分为退变组,部分撕裂组和完全撕裂组。结果 1)冈上肌腱退变组、部分性撕裂组及完全性撕裂组与冈上肌脂肪浸润有显著相关性(P<0.01)。2)冈上肌腱退变组、部分性撕裂组及完全性撕裂组性别、年龄、肩峰下间隙及肩峰形态差异无统计学意义(P>0.05)。3)进一步两两比较显示,冈上肌腱退变组冈上肌脂肪浸润程度分级小于部分性撕裂组及完全性撕裂组,差异具有统计学意义(P<0.05);部分性撕裂组小于完全性撕裂组,差异有统计学意义(P<0.05)。结论 3T MRI可半定量评估冈上肌脂肪浸润程度,冈上肌脂肪浸润程度与其损伤呈正相关。

基于扫描旋转同步运动的三维成像方法

为满足在役战机隐身性能的快速检测需求,提出了一种基于垂直扫描、方位旋转同步运动的三维成像方法。成像系统主要由射频收发设备、收发天线对、竖直方向导轨、转台和数据处理终端构成。天线发射频率步进信号时,对转台上的被测目标进行水平旋转,同时,收发天线对在垂直方向上扫描被测目标。以点目标组合为例,通过理论分析和仿真计算,确定该成像系统的参数配置,比较不同参数配置下系统的三维成像性能。仿真结果表明:采用垂直扫描、方位旋转同步运动方法能够实现三维成像,该方法与经典圆柱面扫描方法相比,扫描时间可缩短92%,同时保持11 dB峰值旁瓣比率。所提方法具有测试效率高、系统搭建简单、易于调节的特点,为服役阶段的隐身飞机散射源诊断提供了一种有效的测量方法选择。