Recognition Rate Of Images Research Articles

Synaptic Feature of Quantum Dot Light-Emitting Diodes for Visualization of Learning Process.

Brain-inspired electronics with synaptic functions hold significant promise for advancing artificial intelligent applications. In this study, we demonstrate the synaptic feature of quantum-dot light-emitting diodes (QLEDs), which can convert electrical pulses into synapse-like light signals (the brightness gradually increases as the electrical pulses are prolonged). These features are analogous to learning and forgetting in biological synapses. The enhancement of brightness can be attributed to the reduction of charge transfer from the quantum dots to ZnO electron transport layer and resistive switching effect. With an integrated complementary metal-oxide-semiconductor (CMOS) drive, arrayed synaptic QLEDs can simulate the visualization of brain-like learning processes, which can reduce the noise toward high image recognition rate (>95.0%) by deep neural networks. Our findings introduce a novel brain-inspired optoelectronic approach with potential applications in optical neuromorphic systems.

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed technique can be applied to various field of life like security, surveillance systems.

Research on image recognition of tomato leaf diseases based on improved AlexNet model

Aiming at the problems that the traditional image recognition technology is challenging to extract useful features and the recognition time is extended; the AlexNet model is improved to improve the effect of image classification and recognition. This study focuses on 8 types of tomato leaf diseases and healthy leaves. By using HOG and LBP weighted fusion to extract image features, a tomato leaf disease recognition model based on the AlexNet model is proposed, and transfer learning is used to train the AlexNet model. Transfer the knowledge learned by the AlexNet model on the PlantVillage image dataset to this model while reducing the number of fully connected layers. Keras deep learning framework and programming language Python were used. The model was implemented, and the classification and identification of tomato leaf diseases were carried out. The recognition rate of feature-weighted fusion classification is higher than that of serial and parallel methods, and the recognition time is the shortest. When the weight coefficient ratio of HOG and LBP is 3:7, the image recognition rate is the highest, and its value is 97.2 %. From the model performance curve See, when the number of iterations is more than 150 times, the training set and test accuracy rate both exceed 97 %, the loss rate shows a gradient decline, and the change is relatively stable; compared with the traditional AlexNet model, HOG + LBP + SVM model, and VGG model, improved AlexNet model has the highest recognition rate, and it has high recall value, accuracy, and F1 value; Compared with the latest convolutional neural network disease recognition models, improved AlexNet model recognition accuracy was 98.83 %, and the F1 value was 0.994. It shows that the model has good convergence performance, fast prediction speed, and low loss rate and can effectively identify 8 types of tomato leaf images, which provides a reference for the research on crop disease identification.

Intelligent Monitoring of Protection Devices in Power system with Enhanced Faster R-CNN

Relay protection devices are necessary to guard the power system safety and stability. With the significant number of substations and relay protection devices, the maintenance workload has become difficult due to limited manpower, posing hidden dangers to the reliable operation of protection devices. In view of this, this article proposes an enhanced Faster R-CNN algorithm to diagnose the relay protection devices based on image monitoring. The proposed algorithm uses RestNet50 as the main tool to recognize features from input images to generate feature maps. Meanwhile, to improve the image detection accuracy, the Non-Maximum Sup-pression (NMS) algorithm in regional suggestion network (RPN) is optimized from solid thresh-old to soft threshold. By combining the images captured by the surveillance camera, intelligent inspections are conducted on the status of the pressing plate, fiber bending, external object retention, and other items in the relay protection device. A great number of samples are used to train networks for different tasks. The results indicate that the enhanced Faster R-CNN has a higher image recognition rate, and the recognition accuracy of each inspection item for the protection device is higher than 95%.

Enhanced ferroelectric photovoltaic performance of Bi2FeCrO6 thin films for neuromorphic computing applications

Nowadays, ferroelectric photovoltaic synapses have attracted great attention due to its polarization controllable and self-powered features. However, the large bandgaps of ferroelectric oxide materials limit its application. This study focuses on the enhancement of ferroelectric photovoltaic properties and the synaptic application of Bi2FeCrO6 (BFCO) device. It is found that the bandgap of BFCO can be modulated by Cr alloying, which causes its photovoltaic effect in the visible region to exceed that of BiFeO3 (BFO) significantly. The short-circuit current density (JSC) of BFCO device in the visible region increases by about 100 times than that of BFO. Furthermore, the polarization modulation and multi-states response are demonstrated by an external electric field. For BFCO ferroelectric photovoltaic synapse, long-term potentiation/depression (LTP/LTD) measurements show an excellent synaptic plasticity of the polarization modulation. The simulated image recognition rate using the MNIST dataset reaches a high accuracy of 96.06%. This work has expanded the potential application of ferroelectric photovoltaic synapse in the visible region.

Ferroelectric Modulation of ReS2‐Based Multifunctional Optoelectronic Neuromorphic Devices for Wavelength‐Selective Artificial Visual System

AbstractNeuromorphic optoelectronic vision system inspired by the biological platform displays potential for in‐sensor computing. However, it is still challenge to process multiwavelength image in noisy environment with simple device configuration and light‐tunable biological plasticity. Here, a prototype visual sensor is demonstrated based on ferroelectric copolymer poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE)) and 2D rhenium disulfide (ReS2) with integration of recognition, memorization, and pre‐processing functions in the same device. Such synaptic devices achieve impressive electronic characteristics, including a current on/off ratio of 109 and mobility of 45 cm2 V−1 s−1. Various synaptic plasticity behaviors have been achieved owing to the switchable ferroelectricity, enabling them to establish an artificial neural network (ANN) with high digit recognition accuracy of 89%. Through constructing optoelectronic device array, object extraction is achieved with wavelength‐selective capability in noisy environment, closely resembling human retina for color recognition. Above outcomes bring a notable improvement in the image recognition rate from 72% to 96%. Besides, low energy consumption comparable to single biological event can be realized. With these multifunctional features, this work inspires highly integrated neuromorphic systems and the development of wavelength‐selective artificial visual platform.

LPSRGAN: Generative adversarial networks for super-resolution of license plate image

This paper proposes a super-resolution algorithm for reconstructing license plate images based on generative adversarial networks (GAN) for improving the recognition rate of low-resolution license plate images. To this end, this paper first designs a new image degradation model, called the n-stage random combination degradation model (n-RCD), which extends the traditional degradation model horizontally and vertically to better simulate the features of low-resolution license plate images in natural scenes. Subsequently, to address the problem that existing GAN models cannot adapt to the complex degradation space in natural scenes, this paper proposes a new network structure called LPSRGAN, which optimizes the SRGAN model from the generator and discriminator to improve its super-resolution capability. In addition, this paper also proposes a perceptual optical character recognition (OCR) loss for license plate images, which defines the connectionist temporal classification (CTC) loss of the output of the OCR network with character label values to better preserve the character details and features of the license plate images. Finally, the experiments reported in this paper show that the proposed algorithm improves the recognition rate of the original low-resolution license plate image by 12.48%, and the recognition rate of the reconstructed image reaches 93.90%. Based on this, this paper also tests the algorithm on actual captured data, and the results show that it performs well in natural scenes, demonstrating the algorithm has broad application prospects.

Exploring the Application of Image Recognition Technology in Evidence Analysis for Legal Professions

Abstract Image recognition technology is widely used in the field of criminal investigation or in the civil field, in which fingerprint image recognition technology, as a means of verification, has a very high legal authority and plays an irreplaceable role. In this paper, image recognition technology is specifically applied to the extraction of fingerprint features from crime scenes. The proposed fingerprint image curve changes are used in the image patch recognition algorithm. By analyzing the changes in the fingerprint curve profile, the evolution law of the fingerprint curve is calculated. Then, using the discrete iteration method, the missing fingerprint image is repaired for recognition. Experiments show that the method in this paper can effectively repair the missing fingerprint image part. Despite the low recognition rate for fingerprint images of 88.1%, other quality images are correctly recognized at more than 95% and have a relatively good recognition effect. Legal practitioners are most satisfied with the accuracy of feature extraction using this technique after using it.

Review of image recognition systems for noisy environments

One of the major challenges faced by image recognition systems in practical applications is the presence of noise in the physical world. To address this challenge, this paper proposes two different approaches. The first approach involves constructing a noisy dataset and training the image recognition system to tolerate noise. The second approach utilizes a combination of denoising methods and pre-trained image recognition systems. Experimental tests and analyses are conducted to evaluate the advantages and disadvantages of each approach. Additionally, this paper investigates the relationship between different noise reduction methods and their impact on improving the recognition rate and resource consumption of images containing Gaussian noise, specifically when neural networks are used for recognition.

Adversarially Learning Occlusions by Backpropagation for Face Recognition.

With the accomplishment of deep neural networks, face recognition methods have achieved great success in research and are now being applied at a human level. However, existing face recognition models fail to achieve state-of-the-art performance in recognizing occluded face images, which are common scenarios captured in the real world. One of the potential reasons for this is the lack of large-scale training datasets, requiring labour-intensive and costly labelling of the occlusions. To resolve these issues, we propose an Adversarially Learning Occlusions by Backpropagation (ALOB) model, a simple yet powerful double-network framework used to mitigate manual labelling by contrastively learning the corrupted features against personal identity labels, thereby maximizing the loss. To investigate the performance of the proposed method, we compared our model to the existing state-of-the-art methods, which function under the supervision of occlusion learning, in various experiments. Extensive experimentation on LFW, AR, MFR2, and other synthetic masked or occluded datasets confirmed the effectiveness of the proposed model in occluded face recognition by sustaining better results in terms of masked face recognition and general face recognition. For the AR datasets, the ALOB model outperformed other advanced methods by obtaining a 100% recognition rate for images with sunglasses (protocols 1 and 2). We also achieved the highest accuracies of 94.87%, 92.05%, 78.93%, and 71.57% TAR@FAR = 1 × 10-3 in LFW-OCC-2.0 and LFW-OCC-3.0, respectively. Furthermore, the proposed method generalizes well in terms of FR and MFR, yielding superior results in three datasets, LFW, LFW-Masked, and MFR2, and producing accuracies of 98.77%, 97.62%, and 93.76%, respectively.

A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images.

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the "dying ReLU" problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.

Taekwondo motion image recognition model based on hybrid neural network algorithm for wearable sensor of Internet of Things

Conventional IoT wearable sensor Taekwondo motion image recognition model mainly uses Anchor fixed proportion whole body target anchor frame to extract recognition features, which is vulnerable to dynamic noise, resulting in low displacement recognition rate of motion image. Therefore, a new IoT wearable sensor Taekwondo motion image recognition model needs to be designed based on hybrid neural network algorithm. That is, the wearable sensor Taekwondo motion image features are extracted, and the hybrid neural network algorithm is used to generate the optimization model of the wearable sensor Taekwondo motion image recognition of the Internet of Things, so as to achieve effective recognition of Taekwondo motion images. The experimental results show that the designed wearable sensor of the Internet of Things based on the hybrid neural network algorithm has a high recognition rate of the motion image displacement of the Taekwondo motion image recognition model, which proves that the designed Taekwondo motion image recognition model has good recognition effect, reliability, and certain application value, and has made certain contributions to optimizing the Taekwondo movement.

Identification of blurred terahertz images by improved cross-layer convolutional neural network.

Terahertz imaging technology has been gradually used in space communication, radar detection, aerospace and biomedical fields. Nevertheless, there are still some limits in terahertz image, such as single tone, fuzzy texture features, poor image resolution and less data, which seriously affect the application and popularization of Terahertz image technology in many fields. Traditional convolutional neural network (CNN) is an effective method for image recognition, but it is limited in highly blurred terahertz image recognition due to the great difference between terahertz image and traditional optical image. This paper presents a proven method for higher recognition rate of blurred terahertz images by using an improved Cross-Layer CNN model with different definition terahertz image dataset. Compared to employing clear image dataset, the accuracy of blurred image recognition can be improved from about 32% to 90% with different definition dataset. Meanwhile, the recognition accuracy of high blurred image can be improved by approximately 5% in contrast to the traditional CNN, which makes the higher recognition ability of neural network. It can be demonstrated that various types of blurred terahertz imaging data can be effectively identified by constructing different definition dataset combined with Cross-Layer CNN. A new method is proved to improve the recognition accuracy of terahertz imaging and application robustness in real scenarios.

Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

Digital filtering is essential for digital imaging, image recognition, and super-resolution technology. For example, the presence of noise in images captured by digital cameras causes deterioration of the image quality and image recognition rate. In order to improve the image recognition rate, noise reduction and edge preservation must be performed during preprocessing. Noise is generally reduced using low-pass filters, such as the Gaussian filter. Although they reduce noise, such filters also have the properties of blurring edge. A strong edge blur reduces the accuracy of the feature detection in image recognition. Therefore, in our previous study, a fast M-estimation Gaussian filter for images (FMGFI) was proposed as an image filter that simultaneously achieves denoising and edge preservation. In the FMGFI, the setting of the optimal basic width of the 2nd order B-spline basis functions is important for achieving simultaneous denoising and edge preservation. In this method, the optimal basic width of the FMGFI was determined not only by manually setting the basic width but also by human judgment of the filtered images. Consequently, the inability to automatically determine the optimal basic width hindered efficient denoising during image processing Therefore, in this research, we develop and propose a method that can automatically determine the optimal basic width of the FMGFI. The previously proposed method calculates using the same basic width for all the pixels over the entire image; in contrast, the proposed method calculates using the basic width automatically determined for each pixel. The experiments confirmed that the method proposed in this study achieves higher denoising and edge preservation performance than the ones used in previous research. The results also showed that it has the highest denoising performance against salt-and-pepper noise as compared to other filters: non-local mean filter, Gaussian filter, median filter, bilateral filter, adaptive bilateral filter, and FMGFI. The experimental results for the Gaussian noise sowed that the proposed method has the same denoising and edge preservation performance as the other filters in visual evaluation. From the above, the proposed method is expected to contribute to efficient denoising and improvement of image quality by using it as a preprocessing.

SAR Image Target Recognition Method by Global and Local Dictionary Sparse Representation

ABSTRACT Target variant detection has been challenging for Synthetic Aperture Radar (SAR), and the performance of the target variant recognition needs to be further enhanced. SAR images are widely used in the field of target recognition and are important to support reconnaissance operations. This paper proposes a novel research approach to improve the efficiency of target recognition. First, the theory surrounding sparse representation and dictionary learning is presented. Secondly, a SAR image target recognition model is constructed according to the theory. Meanwhile, a dynamic joint sparse representation model is proposed based on multi-information and applied to SAR image target recognition when sparse representation is under consideration. Finally, experiments are set up to validate the proposed model. The results are presented as follows: 1. the recognition rate range of the sample SAR image target is 0.811–0.995 and 0.867–0.990, respectively, when the two cases, without registration processing and with registration processing, utilize the recognition method of dictionary learning and sparse representation. 2. with the increase in dictionary size, the average recognition rate of SAR images based on multi-information dynamic joint sparse representation also increases under the conditions of no logarithm transformation and median filtering and after logarithm transformation and median filtering are run. Then, the average recognition rate range is 0.63–0.9 and 0.65–0.96, respectively. Thus, the recognition rate is improved by 5%-10%. 3. the recognition approach based on the sparse representation of multi-information dynamic joint has distinct sparse degrees of SAR image recognition in the case of logarithmic transformation and median filtering when the sample image has been registered and not been registered. The relative average recognition rates were 0.950–0.970 and 0.955–0.977, respectively. The key contribution of the research is to offer a workable solution to the issues plaguing SAR target variant recognition and to improve the significant limitations in the state-of-the-art literature thoroughly.

Liver CT Image Recognition Method Based on Capsule Network

The automatic recognition of CT (Computed Tomography) images of liver cancer is important for the diagnosis and treatment of early liver cancer. However, there are problems such as single model structure and loss of pooling layer information when using a traditional convolutional neural network to recognize CT images of liver cancer. Therefore, this paper proposes an efficient method for liver CT image recognition based on the capsule network (CapsNet). Firstly, the liver CT images are preprocessed, and in the process of image denoising, the traditional non-local mean (NLM) denoising algorithm is optimized with a superpixel segmentation algorithm to better protect the information of image edges. After that, CapsNet was used for image recognition for liver CT images. The experimental results show that the average recognition rate of liver CT images reaches 92.9% when CapsNet is used, which is 5.3% higher than the traditional CNN model, indicating that CapsNet has better recognition accuracy for liver CT images.

Ultra-High-Speed Accelerator Architecture for Convolutional Neural Network Based on Processing-in-Memory Using Resistive Random Access Memory

Processing-in-Memory (PIM) based on Resistive Random Access Memory (RRAM) is an emerging acceleration architecture for artificial neural networks. This paper proposes an RRAM PIM accelerator architecture that does not use Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). Additionally, no additional memory usage is required to avoid the need for a large amount of data transportation in convolution computation. Partial quantization is introduced to reduce the accuracy loss. The proposed architecture can substantially reduce the overall power consumption and accelerate computation. The simulation results show that the image recognition rate for the Convolutional Neural Network (CNN) algorithm can reach 284 frames per second at 50 MHz using this architecture. The accuracy of the partial quantization remains almost unchanged compared to the algorithm without quantization.

A2FWPO: Anti-aliasing filter based on whale parameter optimization method for feature extraction and recognition of dance motor imagery EEG

The classification accuracy of EEG signals based on traditional machine learning methods is low. Therefore, this paper proposes a new model for the feature extraction and recognition of dance motor imagery EEG, which makes full use of the advantage of anti-aliasing filter based on whale parameter optimization method. The anti-aliasing filter is used for preprocessing, and the filtered signal is extracted by two-dimensional empirical wavelet transform. The extracted feature is input to the robust support matrix machine to complete pattern recognition. In pattern recognition process, an improved whale algorithm is used to dynamically adjust the optimal parameters of individual subjects. Experiments are carried out on two public data sets to verify that anti-aliasing filter-based preprocessing can improve signal feature discrimination. The improved whale algorithm can find the optimal parameters of robust support matrix machine classification for individuals. This presented method can improve the recognition rate of dance motion image. Compared with other advanced methods, the proposed method requires less samples and computing resources, and it is suitable for the practical application of brain-computer interface.

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.