Network For Image Recognition Research Articles

One and one make eleven: An interpretable neural network for image recognition

Although non-interpretable (black-box) deep learning models are well known for their accuracy, interpretable deep learning models should be used for high stake decisions, such as: healthcare. In this paper, we present a novel technique of combining the existing state-of-the-art models, and using them as a base model to build an interpretable deep learning model Comb-ProtoPNet. In contrast to usual technique of combining the logits of two (or more) algorithms to form an ensemble algorithm, we combine the algorithms themselves. Our proposed interpretable model applies a prototype layer on top of the convolutional layers of an ensemble base model. We trained and tested our algorithm over the dataset of chest CT-scan images of COVID-19 patients, pneumonia patients and normal people. The use of a certain combination of blocks from two different state-of-the-art models (statistically) significantly improved the accuracy compared to the individual use of the state-of-the-art models as the base models, and this is where the part of the title “One and one make eleven” comes from.

Automatic identification method of bridge structure damage area based on digital image

It is of great scientific and practical value to use effective technical means to monitor and warn the structural damage of bridges in real time and for a long time. Traditional image recognition network models are often limited by the lack of on-site images. In order to solve the problem of automatic recognition and parameter acquisition in digital images of bridge structures in the absence of data information, this paper proposes an automatic identification method for bridge structure damage areas based on digital images, which effectively achieves contour carving and quantitative characterization of bridge structure damage areas. Firstly, the digital image features of the bridge structure damage area are defined. By making full use of the feature that the pixel value of the damaged area is obviously different from that of the surrounding image, an image pre-processing method of the structure damaged area that can effectively improve the quality of the field shot image is proposed. Then, an improved Ostu method is proposed to organically fuse the global and local threshold features of the image to achieve the damaged area contour carving of the bridge structure surface image. The scale of damage area, the proportion of damage area and the calculation rule of damage area orientation are constructed. The key inspection and characteristic parameter diagnosis of bridge structure damage area are realized. Finally, test and analysis are carried out in combination with an actual project case. The results show that the method proposed in this paper is feasible and stable, which can improve the damage area measurement accuracy of the current bridge structure. The method can provide more data support for the detection and maintenance of the bridge structure.

Improvement Airport Security System with Face Recognition Using Neural Network Based on the Arduino Uno Microcontroller

The aim of this study is to implement an algorithm for face recognition, based on the Arduino uno microcontroller. This paper presents it as an airport security system to detect passenger’s face and compare the result with the database of unwanted people. The system combines laser trigger circuit for image capturing and artificial neural network for image recognition. The laser trigger circuit has many benefits such as avoiding camera shakes or taking a picture without a timer. The captured image will be analyzed and processed in MATLAB using an artificial neural network to recognize the passenger’s face from the real captured images after the training phase. Many experiments have been conducted on our face databases with various numbers of iterations. The recognitions’ accuracy and efficiency of the proposed model are 93.33% and 2.67 respectively with 0.696530 seconds as execution time. The result shows the robustness of the developed model in terms of mean squared error, execution time, recognitions’ efficiency and accuracy. The smallest obtained mean squared error is 9.9991e-04 for the training data set and 0.1764 for the testing data set when they are recorded for a modified neural network which makes the developed system more reliable. Finally, the artificial neural network demonstrates the ability to detect the unseen relationship between features belong to the same face.

Elastic Adaptively Parametric Compounded Units for Convolutional Neural Network

The activation function introduces nonlinearity into convolutional neural network, which greatly promotes the development of computer vision tasks. This paper proposes elastic adaptively parametric compounded units to improve the performance of convolutional neural networks for image recognition. The activation function takes the structural advantages of two mainstream functions as the function’s fundamental architecture. The SENet model is embedded in the proposed activation function to adaptively recalibrate the feature mapping weight in each channel, thereby enhancing the fitting capability of the activation function. In addition, the function has an elastic slope in the positive input region by simulating random noise to improve the generalization capability of neural networks. To prevent the generated noise from producing overly large variations during training, a special protection mechanism is adopted. In order to verify the effectiveness of the activation function, this paper uses CIFAR-10 and CIFAR-100 image datasets to conduct comparative experiments of the activation function under the exact same model. Experimental results show that the proposed activation function showed superior performance beyond other functions.

Application of Deep Convolutional Neural Networks in Image Recognition and Classification in Library Management

Application of Deep Convolutional Neural Networks in Image Recognition and Classification in Library Management

Researches advanced in image recognization based on deep learning

Image recognition is a fundamental problem in the field of computer vision community, which aims to understand the content of an image to predict the class of the image. Traditional image recognition methods rely heavily on the quality of handcrafted features, and the recognition accuracy and generalization ability cannot meet practical application requirements. Thanks to the speedy progress of deep learning theory and technology, convolutional neural networks can adaptively learn image semantic features through high-dimensional nonlinear changes, which sensibly rises the exactness of image recognition. At present, image recognition has been generally applied to many fields such as commodity circulation, smart retail, pest identification, medical image analysis and so on. In this paper, we introduce the latest research progress in deep learning-based image recognition. Specifically, following the temporal clues of technological development, we first introduce representative image recognition networks, including their design ideas, basic network structures, advantages and disadvantages, etc. Second, we quantitatively compare the performance of different image recognition networks. Finally, we summarize the existing problems in the sphere of image recognition research and discuss its possible future directions.

Abstract 139: Semi-automated Classification Of Peripheral Artery Disease Lesion Composition From Multi-Contrast Magnetic Resonance Histology At 9.4 Tesla With A 2 Dimensional Convolutional Neural Network Variational AutoEncoder Algorithm

Introduction: Using artificial intelligence (AI)-assisted methods on MRI histology may help endovascular peripheral artery disease (PAD) treatment planning. Objectives: To evaluate soft vs. hard PAD plaque components and crossability using multi-contrast high-resolution MRI, AI algorithm, and histologic samples. Methods: PAD lesions were harvested from 6 patients (one of each) after amputation and imaged using a 9.4T ultra-high resolution MRI scanner with 3 contrasts: T1-weighted (T1w), T2-weighted (T2w) and Ultrashort Echo Time (UTE). 3D imaging volumes were spatially registered and pseudo-color composites (T1w: red, T2w: green, UTE:blue) were created. Custom 2D variational autoencoder (VAE) classification AI algorithm using convolutional neural networks for image recognition classified and scored cross-sections as lumen occluded with soft tissue (tissue score (TS)=10), with soft and hard tissue (TS=30) and with hard tissue (TS= 50). 2 readers performed visual assessment of crossability (crossable/non-crossable) on preselected MRI slices compared to histologic samples (Fig1a), percent agreement was calculated for inter-rater reliability. Results: MRI multi-contrast registration succeeded for all section (pseudo-colors: smooth muscle cells- pink/red, fatty lesions-green, collagen-dark blue, calcification-black). VAE separated axial sections based on plaque composition (Fig1b), average TS per tissue segments ranged from 10 to 48.1 and corresponded well to the composition of tissues inspected visually.20 slices showed appropriate alignment, for the evaluation of crossability 11 were excluded due to lack of sufficient histological sample, inter-rater reliability was 100% on 9 slices (7 crossable, 2 non-crossable). Conclusion: AI algorithm for image recognition successfully distinguished lesion composition. AI-aided analysis may help rapid evaluation of lesion crossability in planning endovascular interventions.

A novel urban mobility classification approach based on convolutional neural networks and mobility-to-image encoding

Over the last few decades, the classification and prediction of mobility trajectories in dynamic networks have become major research topics. Switching of mobility areas (hand-over) in modern cellular networks is frequent due to restricted coverage area and node speeds (urban, highway, etc.). Accurate management of hand-over events is highly desirable to improve the system’s quality of service. We have exploited the high accuracy of machine learning to classify user mobility from mobility traces which we encoded into images. The method delivers high performance in mobility classification/prediction (exceeding 95%) and avoids the need to study and implement a dedicated neural network structure. The technique requires the conversion of mobility traces into image structures and the subsequent application of a convolutional neural network. We propose a novel approach to classifying mobility that involves data-to-image encoding and machine learning for image classification. Numerous simulations were performed to demonstrate the benefits of the proposed technique and to illustrate the variance in the accuracy of the functions of many encoding/classification parameters. The work represents a first preliminary step towards a new mobility prediction approach. We demonstrate that it is possible to achieve a very high level of prediction accuracy with low computational complexity, exploiting the strength of neural networks in image recognition.

Flagging cartel participants with deep learning based on convolutional neural networks

Adding to the literature on the data-driven detection of bid-rigging cartels, we propose a novel approach based on deep learning (a subfield of artificial intelligence) that flags cartel participants based on their pairwise bidding interactions with other firms. More concisely, we combine a so-called convolutional neural network for image recognition with graphs that in a pairwise manner plot the normalized bids of some reference firm against the normalized bids of any other firms participating in the same tenders as the reference firm. Based on Japanese and Swiss procurement data, we construct such graphs for both collusive and competitive episodes (i.e when a bid-rigging cartel is or is not active) and we use a subset of graphs to train the neural network such that it learns distinguishing collusive from competitive bidding patterns. With the remaining graphs, we test the neural network’s out-of-sample performance in correctly classifying collusive and competitive bidding interactions. We obtain a very decent average accuracy of around 95% or slightly higher when either applying the method within Japanese, Swiss, or mixed data (in which Swiss and Japanese graphs are pooled). When using data from one country for training to test the trained model’s performance in the other country (i.e. transnationally), predictive performance decreases (likely due to institutional differences in procurement procedures across countries), but often remains satisfactorily high. All in all, the generally quite high accuracy of the convolutional neural network despite being trained in a rather small sample of a few 100 graphs points to a large potential of deep learning approaches for flagging and fighting bid-rigging cartels.

An Image Detection–Memory–Recognition Artificial Visual Unit Based on Dual‐Gate Phototransistors

Optoelectronic synapses integrating sensing–memory–processing functions have great advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy‐efficient manner. Herein, a light‐induced bidirectional response is demonstrated in the proposed WSe2/MoS2 junction field‐effect transistor (JFET) with an extra Ge back gate. The WSe2/MoS2 JFET exhibits high responsivity and detectivity owing to effective modulation by the top junction and back dielectric gates. The unique bidirectional photoresponse and interfacial state storage properties of the device result in significant synaptic excitatory effects under visible light stimulation and remarkable synaptic inhibitory effects under infrared illumination. Optical storage with visible light and optical erasure with infrared light can be achieved in the device based on its synaptic behavior. The conductance changes under different types of illumination can be used to mimic the weight update in a neural network for image recognition. A high accuracy exceeding 98% was achieved in handwritten digit recognition. This visible and infrared dual‐band phototransistor, which integrates image perception, memory, and recognition functionalities, provides a promising solution for self‐driving, surveillance, computer vision, and biomedical imaging applications.

A Conic Radon-based Convolutional Neural Network for Image Recognition

This article presents a new approach for image recognition that proposes to combine Conical Radon Transform (CRT) and Convolutional Neural Networks (CNN). In order to evaluate the performance of this approach for pattern recognition task, we have built a Radon descriptor enhancing features extracted by linear, circular and parabolic RT. The main idea consists in exploring the use of Conic Radon transform to define a robust image descriptor. Specifically, the Radon transformation is initially applied on the image. Afterwards, the extracted features are combined with image and then entered as an input into the convolutional layers. Experimental evaluation demonstrates that our descriptor which joins together extraction of features of different shapes and the convolutional neural networks achieves satisfactory results for describing images on public available datasets such as, ETH80, and FLAVIA. Our proposed approach recognizes objects with an accuracy of 96 % when tested on the ETH80 dataset. It also has yielded competitive accuracy than state-of-the-art methods when tested on the FLAVIA dataset with accuracy of 98 %. We also carried out experiments on traffic signs dataset GTSBR. We investigate in this work the use of simple CNN models to focus on the utility of our descriptor. We propose a new lightweight network for traffic signs that does not require a large number of parameters. The objective of this work is to achieve optimal results in terms of accuracy and to reduce network parameters. This approach could be adopted in real time applications. It classified traffic signs with high accuracy of 99%.

A Multi-prototype Capsule Network for Image Recognition with High Intra-class Variations

A Multi-prototype Capsule Network for Image Recognition with High Intra-class Variations

Class attention network for image recognition

Class attention network for image recognition

ПРИМЕНЕНИЕ НЕЙРОННЫХ СЕТЕЙ В РАСПОЗНАВАНИИ ЛИЦ

This article discusses the relevance of the use of neural networks in image recognition, various structures of neural networks, such as convolutional, multi–layered and multitasking neural networks, their areas of application and provides examples of the use of different structures in solving various tasks.

Developing tongue coating status assessment using image recognition with deep learning.

To build an image recognition network to evaluate tongue coating status. Two image recognition networks were built: one for tongue detection and another for tongue coating classification. Digital tongue photographs were used to develop both networks; images from 251 (178 women, 74.7±6.6 years) and 144 older adults (83 women, 73.8±7.3 years) who volunteered to participate were used for the tongue detection network and coating classification network, respectively. The learning objective of the tongue detection network is to extract a rectangular region that includes the tongue. You-Only-Look-Once (YOLO) v2 was used as the detection network, and transfer learning was performed using ResNet-50. The accuracy was evaluated by calculating the intersection over the union. For tongue coating classification, the rectangular area including the tongue was divided into a grid of 7×7. Five experienced panelists scored the tongue coating in each area using one of five grades, and the tongue coating index (TCI) was calculated. Transfer learning for tongue coating grades was performed using ResNet-18, and the TCI was calculated. Agreement between the panelists and network for the tongue coating grades in each area and TCI was evaluated using the kappa coefficient and intraclass correlation, respectively. The tongue detection network recognized the tongue with a high intersection over union (0.885±0.081). The tongue coating classification network showed high agreement with tongue coating grades and TCI, with a kappa coefficient of 0.826 and an intraclass correlation coefficient of 0.807, respectively. Image recognition enables simple and detailed assessment of tongue coating status.

Designing a Convolutional Neural Network for Image Recognition: A Comparative Study of Different Architectures and Training Techniques

Designing a Convolutional Neural Network for Image Recognition: A Comparative Study of Different Architectures and Training Techniques

A Robust Residual Shrinkage Balanced Network for Image Recognition from Japanese Historical Documents

Kuzushiji, as characters written in a cursive style, had been continuously used for both publishing and handwriting in Japan before the end of the 19th century. However, well into the twentieth century, most modern Japanese lost the ability to read Kuzushiji due to changes in writing systems. Therefore, how to develop advanced machine learning algorithms to identify Japanese historical characters has great significance in preserving cultural knowledge and promoting sustainable development in urban living. Unlike traditional digit image recognition, Kuzushiji image recognition is a more challenging task due to the complex script for cursive handwriting, multiple variations between characters, and imbalanced data. To address such issues, a novel deep learning‐based character recognition method called residual shrinkage balanced network (RSBN) is developed for classifying Japanese historical characters. A novel residual shrinkage structure with a soft threshold function is constructed to reduce redundant features using specially designed skip‐connected subnetworks. Then, multiple residual shrinkage modules are stacked together to learn hierarchical features from raw input. Furthermore, a novel class rebalancing strategy is further designed to improve learning performance from imbalanced data. The experiments are conducted on Japanese classical literature datasets. The results demonstrate that the proposed method obtains more than 89.01% testing accuracy on Kuzushiji‐MNIST dataset, which outperforms the existing deep learning‐based character image recognition methods. Moreover, without additional data augmentation techniques, the proposed approach also achieves 97.16% balanced accuracy on Kuzushiji‐49 dataset, which is the best one compared with existing published results. When considering the computational performance, the number of trainable parameters of the proposed method is competitive with that of the typical ResNet‐18.

Generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition

The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results.

Recent progress in ferroelectric synapses and their applications

Brain-like computing is an important direction for the development of integrated circuits in the post-Moore era, and the development of artificial synaptic devices that can simulate ideal synaptic behavior is the key to building brainlike computing chips with neuron-synapse-neuron connectivity. Ferroelectric thin film materials have unique nonvolatile polarization, and the plasticity of polarization is very similar to that of biological synapses, so ferroelectric synaptic devices have been widely concerned in recent years. In this paper, the research progress of ferroelectric synaptic devices is reviewed from two aspects, simulation of synaptic function in devices and brain-like computing applications. The results show that ferroelectric synaptic devices have two typical structures of two-terminal and three-terminal. In addition to effectively simulating biological synapse functions, ferroelectric synaptic devices also have the advantages of simple structure, low power consumption, high stability, large switching ratio, and fast programming speed. In terms of applications, a series of advances have been made in the study of ferroelectric synapse-based neural networks for image recognition; meanwhile, ferroelectric synapses have also been applied to tactile and visual bionics. Although abundant research progress has been made, ferroelectric synapses are still at the proof-of-principle stage. There are considerable challenges in the synaptic performance regulation mechanism, reliability evaluation criteria, array structure optimization design, high-density integration process, neuromorphic computing architecture design, and novel application scenario expansion, which are also the directions that need to be focused for future ferroelectric synapse research.

Robust ISAR Target Recognition Based on ADRISAR-Net

Due to the inherent unknown image deformation among the training and test samples, performance of the deep convolutional neural network (CNN) will be degraded for Inverse Synthetic Aperture Radar (ISAR) automatic target recognition. Meanwhile, traditional CNN only captures the local spatial information due to small receptive fields, thus, neglects the global information useful for recognition. To tackle these issues, this article proposes the attention-augmented deformation robust ISAR image recognition network, dubbed as ADRISAR-Net. The model adopts the inverse compositional spatial transformer for automatic image deformation adjustment, and performs joint local and global feature extractions by the attention-augmented CNN. Finally, the softmax classifier outputs the recognition results. The proposed ADRISAR-Net is end-to-end trainable, and achieves higher recognition accuracy for the four-satellite and three-airplane ISAR image data sets generated by electromagnetic computing.