Recognition Rate Of Images Research Articles

Integration of synaptic phototransistors and quantum dot light-emitting diodes for visualization and recognition of UV patterns.

Synaptic photodetectors exhibit photon-triggered synaptic plasticity, which thus can improve the image recognition rate by enhancing the image contrast. However, still, the visualization and recognition of invisible ultraviolet (UV) patterns are challenging, owing to intense background noise. Here, inspired by all-or-none potentiation of synapse, we develop an integrated device of synaptic phototransistors (SPTrs) and quantum dot light-emitting diodes (QLEDs), facilitating noise reduction and visualization of UV patterns through on-device preprocessing. The SPTrs convert noisy UV inputs into a weighted photocurrent, which is applied to the QLEDs as a voltage input through an external current-voltage–converting circuit. The threshold switching characteristics of the QLEDs result in amplified current and visible illumination by the suprathreshold input voltage or nearly zero current and no visible illumination by the input voltage below the threshold. The preprocessing of image data with the SPTr-QLED can amplify the image contrast, which is helpful for high-accuracy image recognition.

(Retracted) Feature extraction method CNDFA for target contour of coal and gangue based on multifractal

Feature extraction is an important factor to improve the recognition rate of coal and gangue. The existing feature extraction methods have some shortcomings in coal and gangue recognition, such as unsatisfactory recognition rate for actual images. Therefore, aiming at the shortcomings of existing coal and gangue feature extraction methods in coal and gangue recognition, the contour feature extraction method is researched based on images analysis to coal and gangue recognition. The center normalization detrended fluctuation analysis (CNDFA) feature extraction algorithm of contour is proposed based on the process of contour feature extraction for coal and gangue. Based on the analysis of the representative features of coal and gangue, the extraction process of target contour features is established based on hardness difference. Combined with the overall features of contour curve after detrending, a center normalized CNDFA feature extraction algorithm is proposed. First, the detrended analysis of contour curve is realized by least square optimal fitting, and then the detrended data are normalized. Finally, the contour features are described quantitatively by multifractal (MF) spectrum to form the geometric features of the target contour curve, which is used to train the support vector machine classifier. The experiment is carried out on the basis of image preprocessing, and the CNDFA method and other feature extraction methods, such as wavelet, gray level co-occurrence matrix, gray level difference statistics, auto-correlation function, and MF, are applied to the contour feature extraction of coal and gangue. Through the comprehensive comparison of the results after different methods recognition in confusion matrix, accuracy, and coal cleanliness, it is concluded that the overall effect of the CNDFA method is better than other methods, and the accuracy is improved by 5% to 25%. The results show that the CNDFA method has better performance. Compared with the other methods, it can better extract the contour features to improve the recognition rate of coal and gangue.

A proposed model for text and image encryption using different ‎techniques

Data security and protection is one of the most common technologies used in the field of computer ‎ science. This is because of its great importance in all fields related to daily life, whether political, ‎ economic, and other aspects change. In this research paper, a new intelligent system ‎ that ‎ concentrates on image ‎ and text encryption and decryption is proposed. ‎ Furthermore, ‎ the image recognition rate is increased. In this paper, ‎ the Rivest Shamir Adleman 3 key (RSA3k) algorithm is used but ‎ in a new ‎ technique to encrypt and decrypt ‎ text. The proposed technique relies on using square root ‎ of public ‎ key as ‎ private key. XOR ‎ operation for colored images ‎ encryption and decryption is ‎ performed. By calculating signal to noise ratio (PSNR), mean square error (MSE), and bit error ‎ rate (BER), the most important things that we address how to provide data security ‎ for large data based on encryption techniques and increase the security of multimedia data and texture date and ‎ address the problem of time and accuracy in image matching. ‎ the results show that the quality of images after decryption is good.

Two-Dimensional Perovskite-Gated AlGaN/GaN High-Electron-Mobility-Transistor for Neuromorphic Vision Sensor.

The extraordinary optoelectronic properties and continued commercialization of GaN enable it a promising component for neuromorphic visual system (NVS). However, typical GaN‐based optoelectronic devices demonstrated to data only show temporary and unidirectional photoresponse in ultraviolet region, which is an insurmountable obstacle for construction of NVS in practical applications. Herein, an ultrasensitive visual sensor with phototransistor architecture consisting of AlGaN/GaN high‐electron‐mobility‐transistor (HEMT) and two‐dimensional Ruddlesden–Popper organic–inorganic halide perovskite (2D OIHP) is reported. Utilizing the significant variation in activation energy for ion transport in 2D OIHP (from 1.3 eV under dark to 0.4 eV under illumination), the sensor can efficiently perceive and storage optical information in ultraviolet–visible region. Meanwhile, the photo‐enhanced field‐effect mechanism in the depletion‐mode HEMT enables gate‐tunable negative and positive photoresponse, where some typical optoelectronic synaptic functions including inhibitory and excitatory postsynaptic current as well as paired‐pulse facilitation are demonstrated. More importantly, a NVS based on the proposed visual sensor array is constructed for achieving neuromorphic visual preprocessing with an improved color image recognition rate of 100%.

Intelligent Face Recognition System Based on Universal Design Concept.

The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people's life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users' physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.

Comparing Autobiographical Brand Images and Neutral Images Regarding False Memory Formation.

Consumers' prior experiences form an episodic memory that largely influences their decision-making process. This episodic memory is mainly linked to cognitive and emotional perception and we know that brand image influences our cognitive and emotional perception. Nevertheless, it has not been well described how autobiographical memories of brand images differ from other types of images. In this study, we hypothesized that brand pictures have a higher chance to create false memories as compared to neutral ones. We investigated this hypothesis using the Deese-Roediger-McDermott paradigm with lists of brand pictures from the local market and associated neutral images from the international affective picture system. Thirty graduate students were exposed to image stimuli, followed by a distractor task and a recognition task. After the normality test, reaction times (RT), and false recognition rate of brands and neutral images were statistically compared using a pairwise t-test. The results showed a significant decrease in reaction time (RT) and an increase in the false recognition rate of brand pictures compared to neutral images. Interestingly, the effect of gender on the creation of false memory by autobiographical brand images was not significant. We hope these findings can pave the way for a better understanding of the false memory mechanism. Autobiographical brand images give a higher chance of false memory as compared to neutral imagesMen and women do not differ in the formation of false memoryReaction time in false memory is longer than in true memory.False positives create more cognitive load. We see many images around us every day, such as the image of different brands in our daily shopping, which puts us in front of different types of images, many of which are old, these brands become part of our life memories, and their images are aspects of autobiography. Therefore, these brands can form false memories for people. The image stimuli of this study are to investigate whether these images can form false memories or not. The results showed that the images that we see every day in the street of the supermarket and can change our memories.

Sports Biology Seminar of Three-dimensional Movement Characteristics of Yoga Standing Based on Image Recognition

With the exponential improvement of the integration capabilities of electronic hardware devices, digital images have become an indispensable information carrier, thus promoting the development of image recognition, detection, or tracking technologies. At present, there is a blank in the field of image recognition measured by the action standard of yoga standing three-dimensional movement, which will bring huge room for researchers to play. Based on the traditional image recognition technology, there are cases where missed detection or false detection cannot be correctly identified and made a correct judgment. In this paper, the ability to capture the action is improved by optimizing the network for the positioning of the stereoscopic action target of the yoga station. The linear discriminant analysis (LDA) after adding optimization is used to reduce the dimensionality of the captured image data, which is beneficial to improving image recognition rate and reducing image loss rate. Through the analysis of the three-dimensional movement of the yoga station, the model based on the improved algorithm is compared with the traditional model, and the final image recognition accuracy is improved compared with the network model before the improvement. The image recognition error rate steadily tends to 5%, and the loss rate is also below 2%. Through the optimized convolutional neural network, this paper can accurately capture the image position, and the recognition rate has also been greatly improved, which can provide a reference for future research in other fields.

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections.

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add “Salt and pepper” noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

An image restoration and detection method for picking robot based on convolutional auto-encoder

• An image restoration method based on convolutional autoencoder is proposed. • Use the area growth method to divide and code the obstruction according to the contour. • Predict the overall outline of citrus based on the unobstructed citrus outline. • Fill the area to be repaired with the selection method of similar pixels. • The repair rate and image recognition rate have been effectively improved. At present, machine vision and deep learning theories have been widely used in fruit recognition and picking. However, in the process of identification and picking, there are often situations where the target is blocked, existing methods cannot accurately identify, or the identification accuracy rate is low. For improving the recognition rate of fruits under occlusion, this paper proposes an image restoration method based on a convolutional auto-encoder. This method first encodes the obstruction and then determines the general shape of the occluded fruit and compares the general shape with the coding part is combined to determine the area to be repaired. Finally, the area to be repaired is filled with pixels to realize the image repair and recognition of the occlusion map. The average repair rate of the method proposed in this paper is 95.96%, the restoration rate is 3.52% higher than the traditional convolution method, the L2 loss value is 0.63% lower than the traditional convolution method, the average detection accuracy of the restored fruits is 94.77%.

Towards a super-resolution based approach for improved face recognition in low resolution environment.

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one. This paper develops a robust real-time face recognition approach that uses super-resolution to improve images and detect faces in the video. Many previously developed face detection systems are constrained by the severe distortion in the captured images. Further, many systems failed to handle the effect of motion, blur, and noise on the images registered on a camera. The presented approach improves descriptor count of the image based on the super-resolved faces and mitigates the effect of noise. Furthermore, it uses a parallel architecture to implement a super-resolution algorithm and overcomes the efficiency drawback increasing face recognition performance. Experimental analysis on the ORL, Caltech, and Chokepoint datasets has been carried out to evaluate the performance of the presented approach. The PSNR (Peak Signal-to-Noise-Ratio) and face recognition rate are used as the performance measures. The results showed significant improvement in the recognition rates for images where the face didn’t contain pose expressions and scale variations. Further, for the complicated cases involving scale, pose, and lighting variations, the presented approach resulted in an improvement of 5%-6% in each case.

Edge detail enhancement algorithm for high-dynamic range images

Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.

Application Research of Trademark Recognition Technology based on SIFT Feature Recognition Algorithm in Advertising Design

Now-a-days, due to the sharp increase in the number of advertising designs, creative duplication is easy to occur in advertising design. If this situation is not discovered in time, it may cause legal disputes and cause damage to the reputation and property of the enterprise. In view of the above situation, this paper proposes a trademark recognition technology based on SIFT feature recognition algorithm to avoid duplication of advertisement design and cause copyright disputes. Aiming at the defect that the dimension of the image feature vector extracted by SIFT algorithm is too high, the principal component analysis method is used to reduce its dimension. For the problem of unsatisfactory image recognition rate accuracy of SIFT algorithm, a support vector machine is used to classify the extracted feature vector, so as to improve the image recognition rate. Based on the above content, build a trademark recognition model. The research results show that the recognition accuracy of the model reaches 98.82%, 0.66% and 0.58% higher than that of Model 1 and Model 2; AUC value of model 3 is 0.962, 0.039 higher than model 2 and 0.107 higher than model 1. The above results show that the proposed trademark recognition model can better identify similar advertising designs, thereby avoiding design duplication and legal disputes.

A Systematic Literature Review of Law Enforcement Image Recognition Methods based on Generative Adversarial Networks Framework

Image recognition research based on the Generative Adversarial Networks (GAN) has been widely used in various specialized technical fields because of its universality, adaptability, and scalability of the adversarial framework. The framework achieves the tasks of image generation, synthesis, and transformation to improve the image resolution and recognition rate, which is consistent with the purpose of the police in the image recognition work. However, the task of applying the GAN method to police image recognition is not widely explored. In this paper, we systematically review related research to understand the current applications of GAN in the field of image recognition and summarize their contributions. We analyze 35 academic papers after 2018 to provide a state-of-the-art research stream. According to the GAN applications, we divided the dataset into three domains, which are image-to-image translation, image augmentation, and mixed model. The results show that there is little difference in the number of articles in the three fields. In addition, most of the papers use image conversion methods from more than two domains, which indicates that GAN is flexible in designing the framework according to the research tasks. Based on the methods and challenges in the literature, we further propose future research directions.

A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network

Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.

Medical Image Recognition Technology in the Effect of Substituting Soybean Meal for Fish Meal on the Diversity of Intestinal Microflora in Channa argus.

Purpose To study the application of medical image recognition technology based on backpropagation neural network (BPNN) in the effect of soybean meal replacing fish meal on intestinal microbial diversity of Channa argus and to evaluate the application value of this intelligent algorithm, Channa argus was fed with different contents of soybean meal instead of fish meal. Methods After intestinal samples were collected and bacteria were isolated, microscopic imaging was performed, and the images were classified and identified. BPNN was constructed to perform denoising, smoothing, and segmentation. Results After BPNN processing, the bacteria were completely separated from the original image background, and the bacteria was in the closed state, which was beneficial to feature extraction and species recognition. If there were 2 hidden layer nodes, the segmentation accuracy of bacterial microscopic images was the highest, up to 97.3%. With the replacement ratio of fish meal increased, the species of intestinal microbiome gradually enriched, and the relative abundance of intestinal microbiome was higher after fish meal was completely replaced by soybean meal (replacement). The intestinal microbial enzyme activities were affected by different fish meal and soybean meal contents in the diet. The glutamate transaminase and adenosine deaminase activities were increased after the replacement and were higher than those before the replacement, with statistically significant differences (P < 0.05). Conclusion Replacement of fish meal with soybean meal has a significant effect on the intestinal flora diversity of Channa argus, and there is a close relationship between them. The image recognition technology based on BPNN has high recognition rate and segmentation accuracy for microbiological microscopic images.

Ship Target Recognition Under Different Sunlight Intensity

<p indent=0mm>In the case of surface target monitoring, the clarity of the ship target often varies with the reflection intensity of the sea surface under different sunlight intensity, which will lead to the unstable recognition rate of the ship target and increase the false alarm rate. For this reason, the ship target recognition algorithm based on ResNet-50 is proposed. Firstly, it uses ResNet-50 network to extract image feature information and applies sunlight robust loss constraint to the features before and after sunlight intensity change to reduce the feature difference. Then, it uses gray-scale histogram to calculate the statistical matrices of features to obtain six features: light contrast, brightness, smoothness, information, third-order matrices and entropy, and generates new feature vector to apply sunlight robust loss constraint to the features before and after sunlight intensity change again. Finally, the two constraints are combined to form a loss function and trained to optimize the optimal weights using Bayesian adaptive hyperparameters. The experimental results show that the average recognition rate of the database for ship sunlight variation reaches 90.47%, which is about 4.00% higher than that before the improvement, and the recognition rate of ship images with sunlight variation of 25, 65 and 80 increases by 3.14%, 6.07% and 16.41%, which shows that the algorithm has a good constraint effect on sunlight variation and the recognition rate is significantly improved.

A robust neuromorphic vision sensor with optical control of ferroelectric switching

The rapid development of the artificial intelligence field has increased the demand for retina-inspired neuromorphic vision sensors with integrated sensing, memory, and processing functions. Here, we present a neuromorphic vision sensor with an optoelectronic transistor structure consisting of monolayer molybdenum disulfide and barium titanate ferroelectric film. Beyond conventional electrical tuning of ferroelectric polarization, the optoelectronic transistor can exhibit a light-dosage tunable synaptic behavior with a high switching ratio and good non-volatility, enabled by photo-induced ferroelectric polarization reversal. The wavelength-dependent optical sensing and multi-level optical memory properties are utilized to achieve the in-sensor neuromorphic visual pre-processing. A simulated artificial neural network built from the proposed vision sensors with neuromorphic pre-processing function demonstrated that the image recognition rate for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset could be significantly improved by reducing redundant data. The obtained results suggest that 2D semiconductor/ferroelectric optoelectronic transistors can provide a promising hardware implementation towards constructing high-performance neuromorphic visual systems

Ultrafast machine vision with artificial neural network devices based on a GaN-based micro-LED array.

GaN-based micro-LED is an emerging display and communication device, which can work as well as a photodetector, enabling possible applications in machine vision. In this work, we measured the characteristics of micro-LED based photodetector experimentally and proposed a feasible simulation of a novel artificial neural network (ANN) device for the first time based on a micro-LED based photodetector array, providing ultrafast imaging (∼133 million bins per second) and a high image recognition rate. The array itself constitutes a neural network, in which the synaptic weights are tunable by the bias voltage. It has the potentials to be integrated with novel machine vision and reconfigurable computing applications, acting as a role of acceleration and similar functionality expansion. Also, the multi-functionality of micro-LED broadens its application potentials of combining ANN with display and communication.

Research on Position Recognition System of Gantry Hoisting Based on Machine Vision

During the process of steel casting, the task of worker is to identify the relative position of gantry hook and lifting lug of hot metal ladle so as to judge the appropriate time when gantry crane is to be lifted. Worker needs to stand near hot metal ladle, which does great damage to human’s body if exposed to such a bad environment. For this reason, the author has designed a machine vision system to displace artificial recognition. The machine vision system consists of the vision recognition algorithm based on AlexNet Convolution Neural Network (CNN) and the improved Canny edge detection algorithm. AlexNet CNN completes the preliminary recognition of the image and provides the samples that meet the recognition conditions to the improved Canny algorithm. The improved Canny algorithm completes the extraction of parameters such as the relative distance between the gantry hook and lifting lug and the tilt Angle of gantry hook. The sample images are extracted from two environments: model simulation and field simulation. Experimental data shows that the recognition algorithm based on AlexNet CNN can achieve 100% image recognition rate, which is higher than other traditional machine learning algorithms; the improved Canny edge detection algorithm can improve edge quality of extracting images in harsh environments.

Optimization Research on Server Detection Based on RGB Maximum Mean Features and Deep Belief Network Model

The intelligent detection about the running state of server equipment in computer room is a very important research field. In general, the state recognition research of signal lamp mainly includes the color and shape characteristics of signal lamp, and machine learning models. At present, the research results in this respect are not ideal for the recognition of signal lamps, Due to the small size and high density of the detected server lamp source, its shape characteristics cannot be accurately obtained, and state information is difficult to be perceived. This paper proposes a RGBMM features algorithm and a DBN network model algorithm, which is different from the traditional signal state recognition algorithm. According to the characteristics of server signal lamp, the color state image data of signal lamp is extracted effectively by RGBMM features algorithm, then the DBN network model algorithm is used to evaluate and identify the sampled signals, so as to judge whether the server is working normally. The experimental results show that the state recognition accuracy of the signal lamp is up to 98%, while the recognition rate of the signal lamp color state image using HSV( ( Hue, Saturation, Value) ) transformation is 90.7%. The difference between the three is different the algorithms used. So the RGBMM features algorithm and the DBN network model algorithm have a wider use for the signal lamp color status recognition.