Deep Image Features Research Articles

Scene Classification Using Deep Networks Combined with Visual Attention

In view of the scene’s complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability.

Prediction Model for Tea Polyphenol Content with Deep Features Extracted Using 1D and 2D Convolutional Neural Network

The content of tea polyphenols (TP) is one of the important indicators for judging the quality of tea. Accurate and non-destructive estimation technology for tea polyphenol content has attracted more and more attention, which has become a key technology for tea production, quality identification, grading and so on. Hyperspectral imaging technology is a fusion of spectral analysis and image processing technology, which has been proven to be an efficient technology for predicting tea polyphenol content. To make full use of spectral and spatial features, a prediction model of tea polyphenols based on spectral-spatial deep features extracted using convolutional neural network (CNN) was proposed, which not only broke the limitations of traditional shallow features, but also innovated the technical path of integrated deep learning in non-destructive detection for tea. Firstly, one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN) models were constructed to extract the spectral deep features and spatial deep features of tea hyperspectral images, respectively. Secondly, spectral deep features, spatial deep features, and spectral-spatial deep features are used as input variables of machine learning models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF). Finally, the training, testing and evaluation were realized using the self-built hyperspectral dataset of green tea from different grades and different manufacturers. The results showed that the model based on spectral-spatial deep features had the best prediction performance among the three machine learning models (R2 = 0.949, MAE = 0.533 for training sets, R2 = 0.938, MAE = 0.799 for test sets). Moreover, the visualization of estimation results of tea polyphenol content further demonstrated that the model proposed in this study had strong estimation ability. Therefore, the deep features extracted using CNN can provide new ideas for estimation of the main components of tea, which will provide technical support for the estimation tea quality estimation.

Educational Data Mining by Optimally Fusing Shallow and Deep Features.

In order to meet the requirement of designing the educational curriculum and extract the relevant data toward students, we in this paper utilize the deep learning architecture to study the personalized understanding and educational feature extraction algorithm of the high school and college students. Aiming at the insufficiency of deep collaborative filtering recommendation algorithm that can only discover the paths that have attracted students' attention, the requirements of intelligent course are proposed by us. First, the word vector permutation discourse poke model is extracted to produce audible vectors of message data, a well-designed learning algorithm is proposed subsequently to extract and fuse multiple heterogeneous educational data. Then, the SVD algorithm program is used to reduce the range of feature vectors. And thus, the model is completely functional and we identify unified channels and users. The method of combining hand-extracted low-level features with high-level human gesture features (image visual gate features and image deep features) automatically extracted by our adopted deep network is used to identify students' various emotions. The conclusive product is a flat semantic description that effectively detects copying and objection. Experiments are performed on a generic dataset, and we use the old-fashioned manual birth method and two well-known data sets VGG16 and fine-tune AlexNet.

Deep Learning Based Cancer Survival Analysis Algorithm

Histopathological image examination is the gold-standard tool of cancer diagnosis. However, it is subjective to only rely on doctor qualitative analysis of pathological image. What’s more, it is easy to miss pathological information. The texture of pathological images is complex and highly heterogeneous, so it is difficult to extract the deep features of histopathological images. In this paper, a deep learning-based cancer survival analysis algorithm is proposed to process the histopathological image data, which can effectively extract the histopathological image features and improve the accuracy of the model. The average C-Index of the proposed method is 0.7233, which is improved by 28.0% and 19.9% compared with two state-of-the-art methods.

An approach to enhance performance of age invariant face recognition

Age invariant face recognition (AIFR) is currently a study topic with several potential uses. It offers a variety of real-world applications, including passport renewal, driver’s license renewal, locating missing children, locating criminals, providing security to VIPs etc. In the field of AIFR, scientific efforts have increased. Matching faces of big age differences is thus a challenge, owing to the significant variation in appearance between young and elderly age. The appearance and form of the face deteriorate with age, making facial recognition the most difficult task. AIFR has become a highly common and difficult chore in recent years. In this discipline, the set of feature extraction and classification algorithms is crucial. This paper addresses the issues raised above by proposing an enhanced ASM approach for extracting features from 2D search regions using handcrafted and deep image features in conjunction with a 7-layer CNN architecture and a smaller image size of 32x32 pixels to reduce delay time and space complexity. Using the standard dataset LAG, the study approach entails running many tests to evaluate the proposed system’s performance. The results show that the suggested method beats state-of-the-art algorithms in face recognition and achieves good accuracy throughout the age spectrum. The presented methodology achieves a maximum accuracy of 91.76 percent for the LAG database, outperforming all existing state-of-the-art methodologies.

A Deep Convolutional Neural Network Stacked Ensemble for Malware Threat Classification in Internet of Things

Malicious attacks to software applications are on the rise as more people use Internet of things (IoT) devices and high-speed internet. When a software system crash happens caused by malicious action, a malware imaging method can examine the application. In this study, we present a novel malware classification method that captures suspected operations in a variety of discrete size image features, allowing us to identify such IoT device malware families. To decrease deep neural network training time, essential local and global image features are selected using a combined local and global feature descriptor (LBP-GLCM). The classification performance of the proposed deep learning model is improved by combining the predictions of weak learners (CNNs) and using them as knowledge input to a multi-layer perceptron meta learner. This is a neural network ensemble with stacked generalization that is used to improve network generalization ability. The public dataset used for performance evaluation contains 5472 samples from 11 different malware families. In order to compare the proposed methodology to current malware detection systems, we developed a baseline experiment. The proposed approach improved malware classification results to 98.5% accuracy and 98.4% accuracy when using [Formula: see text] and [Formula: see text] image sizes, respectively. Overall, the results showed that the stacked generalization ensemble with multi-step extracting features is a more effective method for classification performance and response time.

A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification

The large intra-class difference and inter-class similarity of scene images bring great challenges to the research of remote-sensing scene image classification. In recent years, many remote-sensing scene classification methods based on convolutional neural networks have been proposed. In order to improve the classification performance, many studies increase the width and depth of convolutional neural network to extract richer features, which increases the complexity of the model and reduces the running speed of the model. In order to solve this problem, a lightweight convolutional neural network based on hierarchical-wise convolution fusion (LCNN-HWCF) is proposed for remote-sensing scene image classification. Firstly, in the shallow layer of the neural network (groups 1–3), the proposed lightweight dimension-wise convolution (DWC) is utilized to extract the shallow features of remote-sensing images. Dimension-wise convolution is carried out in the three dimensions of width, depth and channel, and then, the convoluted features of the three dimensions are fused. Compared with traditional convolution, dimension-wise convolution has a lower number of parameters and computations. In the deep layer of the neural network (groups 4–7), the running speed of the network usually decreases due to the increase in the number of filters. Therefore, the hierarchical-wise convolution fusion module is designed to extract the deep features of remote-sensing images. Finally, the global average pooling layer, the fully connected layer and the Softmax function are used for classification. Using global average pooling before the fully connected layer can better preserve the spatial information of features. The proposed method achieves good classification results on UCM, RSSCN7, AID and NWPU datasets. The classification accuracy of the proposed LCNN-HWCF on the AID dataset (training:test = 2:8) and the NWPU dataset (training:test = 1:9), with great classification difficulty, reaches 95.76% and 94.53%, respectively. A series of experimental results show that compared with some state-of-the-art classification methods, the proposed method not only greatly reduces the number of network parameters but also ensures the classification accuracy and achieves a good trade-off between the model classification accuracy and running speed.

Dynamic Transfer Exemplar based Facial Emotion Recognition Model Toward Online Video

In this article, we focus on the dynamic facial emotion recognition from online video. We combine deep neural networks with transfer learning theory and propose a novel model named DT-EFER. In detail, DT-EFER uses GoogLeNet to extract the deep features of key images from video clips. Then to solve the dynamic facial emotion recognition scenario, the framework introduces transfer learning theory. Thus, to improve the recognition performance, model DT-EFER focuses on the differences between key images instead of those images themselves. Moreover, the time complexity of this model is not high, even if previous exemplars are introduced here. In contrast to other exemplar-based models, experiments based on two datasets, namely, BAUM-1s and Extended Cohn–Kanade, have shown the efficiency of the proposed DT-EFER model.

Abstract 1218: HOPE2Net: Integrating histological features and position embeddings in spatially resolved transcriptomics to predict gene expression and pathway activities from histology images in tumors

Abstract Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled the profiling of transcriptome-wide gene expression while retaining spatial location information of each measured spot within a tissue. Meanwhile, the corresponding histopathology images of tissue sections are readily available and can be aligned to the measured spots. Given that the histology images are practically more convenient and affordable to obtain, we designed HOPE2Net, a multi-layer perceptron architecture, that leverages information provided by SRT data to predict gene expression and pathway activities from histology images. Through systematic evaluations of different approaches for extracting deep image features and cellular morphology features, HOPE2Net performs feature selections from a pre-trained Vision Transformer, which is the state-of-art deep learning model for image recognition. After extracting histological image features, HOPE2Net further integrates with position embeddings, to optimize the gene expression and pathway activity prediction tasks. Through analyzing breast cancer and prostate cancer SRT datasets obtained from numerous tissue sections in multiple patients, we demonstrate that HOPE2Net can accurately predict the gene expression patterns for highly variable genes and the activities for significantly enriched domain-specific pathways. We further show that the predicted gene expression and pathway activities can help detect cancer subtypes and aid in treatment decision-makings. Given the growing interest in applying SRT in cancer genomics, we believe HOPE2Net holds the potential in identifying biomarkers from direct screenings of tissue histology images, which may be implemented in clinical studies for cancer diagnoses and decision-making processes. Citation Format: Kenong Su, Minxing Pang, Mingyao Li. HOPE2Net: Integrating histological features and position embeddings in spatially resolved transcriptomics to predict gene expression and pathway activities from histology images in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1218.

Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application

As artificial intelligence in medical imaging is used to diagnose many diseases, it can also be employed to diagnose whether a person has periodontal bone loss or not. Accurate and early diagnosis performs a vital task in the treatment of the patient’s dental disorder. Therefore, such medical images are known to be an important clinical adjunct. In this manuscript, whether the patient has periodontal bone loss or non-periodontal bone loss is diagnosed employing hybrid artificial intelligence-based systems. Herein, after tagging a total of 1432 images by an expert, we extract 1000 deep image features for each image using AlexNet and SqueezeNet deep learning architectures. On the other hand, we classify these images directly without extracting the image features using the EfficientNetB5 deep learning architecture. First, we categorize AlexNet-based deep image features using the Coarse Tree, Weighted K-Nearest Neighbor (KNN), Gaussian Naïve Bayes, RUSBoosted Trees Ensemble, and Linear Support Vector Machine (SVM) classifiers. Afterward, we classify SqueezeNet-based deep image features using Medium Tree, Gaussian Naïve Bayes, Boosted Trees Ensemble, Coarse KNN, and Medium Gaussian SVM classifiers. With the help of the ten classifiers employed in this study, we also design a user-friendly Graphical User Interface (GUI) application. Thanks to this application, we aim to reduce the workload of experts, save time and help to diagnose dental disorders early. The results show that the best classifiers for AlexNet-based, SqueezeNet-based, and Direct-Convolutional Neural Network (CNN) are Linear SVM, Medium Gaussian SVM, and EfficientNetB5, respectively. Among these classifiers, the best classifier is Linear SVM, and its accuracy, error, sensitivity, specificity, precision, and F1 score values are 81.49%, 18.51%, 84.57%, 79.14%, 75.68%, and 79.88%, respectively.

Inverted N-Type Lightweight Network Based on Back Projection for ImageSuper-Resolution Reconstruction

Aiming at the problem that the super-resolution reconstruction algorithms based on deep learning increase the complexity of the network model and cause a large number of parameters by increasing the depth of neural network to improve the super-resolution reconstruction performance, an inverted N-type lightweight network based on back projection for image super-resolution reconstruction is proposed. First, the initial convolutional block is used to extract the shallow features of low-resolution images. Secondly, the deep features of low-resolution images are extracted through two rounds of gradual model compression based on the inverted N-type network of back projection and two rounds of gradual model restoration of the inverted N-type network. Then, the extracted shallow features and deep features are combined by global residual learning and scaled up to the desired output reconstructed image size by using the upsampling module. Finally, the reconstruction module is used to reconstruct the super-resolution image. The results of experiments on the Set5, Set14, BSDS100 and Urban100 testing sets show that the proposed algorithm has a lighter network structure than other algorithms, and the reconstructed super-resolution images not only have higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), but also have better visual effects.

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes.

Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76 ± 0.08 and 0.91 ± 0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine.

Educational Psychology Analysis Method for Extracting Students' Facial Information Based on Image Big Data.

At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students' facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student's academic mood, they can intervene on the students' positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students' positive academic emotions, in order to improve students' positive academic emotions, which will improve students' academic performance and teaching.

A multimodal transformer to fuse images and metadata for skin disease classification.

Skin disease cases are rising in prevalence, and the diagnosis of skin diseases is always a challenging task in the clinic. Utilizing deep learning to diagnose skin diseases could help to meet these challenges. In this study, a novel neural network is proposed for the classification of skin diseases. Since the datasets for the research consist of skin disease images and clinical metadata, we propose a novel multimodal Transformer, which consists of two encoders for both images and metadata and one decoder to fuse the multimodal information. In the proposed network, a suitable Vision Transformer (ViT) model is utilized as the backbone to extract image deep features. As for metadata, they are regarded as labels and a new Soft Label Encoder (SLE) is designed to embed them. Furthermore, in the decoder part, a novel Mutual Attention (MA) block is proposed to better fuse image features and metadata features. To evaluate the model’s effectiveness, extensive experiments have been conducted on the private skin disease dataset and the benchmark dataset ISIC 2018. Compared with state-of-the-art methods, the proposed model shows better performance and represents an advancement in skin disease diagnosis.

MSAt-GAN: a generative adversarial network based on multi-scale and deep attention mechanism for infrared and visible light image fusion

For the past few years, image fusion technology has made great progress, especially in infrared and visible light image infusion. However, the fusion methods, based on traditional or deep learning technology, have some disadvantages such as unobvious structure or texture detail loss. In this regard, a novel generative adversarial network named MSAt-GAN is proposed in this paper. It is based on multi-scale feature transfer and deep attention mechanism feature fusion, and used for infrared and visible image fusion. First, this paper employs three different receptive fields to extract the multi-scale and multi-level deep features of multi-modality images in three channels rather than artificially setting a single receptive field. In this way, the important features of the source image can be better obtained from different receptive fields and angles, and the extracted feature representation is also more flexible and diverse. Second, a multi-scale deep attention fusion mechanism is designed in this essay. It describes the important representation of multi-level receptive field extraction features through both spatial and channel attention and merges them according to the level of attention. Doing so can lay more emphasis on the attention feature map and extract significant features of multi-modality images, which eliminates noise to some extent. Third, the concatenate operation of the multi-level deep features in the encoder and the deep features in the decoder are cascaded to enhance the feature transmission while making better use of the previous features. Finally, this paper adopts a dual-discriminator generative adversarial network on the network structure, which can force the generated image to retain the intensity of the infrared image and the texture detail information of the visible image at the same time. Substantial qualitative and quantitative experimental analysis of infrared and visible image pairs on three public datasets show that compared with state-of-the-art fusion methods, the proposed MSAt-GAN network has comparable outstanding fusion performance in subjective perception and objective quantitative measurement.

Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering

With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making process of tourists. Among UGCs, photos represent tourists’ visual preferences for a specific area. Paying attention to the value of photos, several studies have attempted to analyze them using deep learning technology. However, the research methods that analyze tourism photos using recent deep learning technology have a limitation in that they cannot properly classify unique photos appearing in specific tourist attractions with predetermined photo categories such as Places365 or ImageNet dataset or it takes a lot of time and effort to build a separate training dataset to train the model and to generate a tourism photo classification category according to a specific tourist destination. The purpose of this study is to propose a method of automatically classifying tourist photos by tourist attractions by applying the methods of the image feature vector clustering and the deep learning model. To this end, first, we collected photos attached to reviews posted by foreign tourists on TripAdvisor. Second, we embedded individual images as 512-dimensional feature vectors using the VGG16 network pre-trained with Places365 and reduced them to two dimensions with t-SNE(t-Distributed Stochastic Neighbor Embedding). Then, clusters were extracted through HDBSCAN(Hierarchical Clustering and Density-Based Spatial Clustering of Applications with Noise) analysis and set as a regional image category. Finally, the Siamese Network was applied to remove noise photos within the cluster and classify photos according to the category. In addition, this study attempts to confirm the validity of the proposed method by applying it to two representative tourist attractions such as ‘Gyeongbokgung Palace’ and ‘Insadong’ in Seoul. As a result, it was possible to identify which visual elements of tourist attractions are attractive to tourists. This method has the advantages in that it is not necessary to create a classification category in advance, it is possible to flexibly extract categories for each tourist destination, and it is able to improve classification performance even with a rather small volume of a dataset.

Combination of Radiological and Clinical Baseline Data for Outcome Prediction of Patients With an Acute Ischemic Stroke.

BackgroundAccurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline.MethodsWe included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process.ResultsFrom 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach.ConclusionsThe combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values.

A real-time defect detection in printed circuit boards applying deep learning

Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustness

Personalized Smart Clothing Design Based on Multimodal Visual Data Detection.

In the traditional clothing customization system, only the designer participates in the clothing design, and the style is single. In the face of numerous styles, the user just repeatedly arranges and combines the styles, but does not realize the user's innovative design. In this paper, we propose a novel multitask deep convolutional neural network training method for task-by-task transfer learning, and learn deep image features for image retrieval tasks on noisy user click data. Image retrieval model based on image-text multimodal correlation features: this paper uses image-text multimodal correlation features to calculate the correlation between query keywords and images, and calculates the correlation between images and images. In this paper, the method of automatic generation of clothing style is researched, and parameterized coding is designed for it. Taking the typical style of suits as the initial population, through the human-computer interaction interface, the scoring value is assigned to the fitness value to carry out the evolution process. The binary string of the suit style generated by the genetic algorithm is decoded through the decoding algorithm and rules, decoded into a visual style diagram of the style of the suit style, and the style diagram of the suit style is automatically drawn. From the perspective of clothing design, this paper summarizes the general design methods of outdoor sports smart clothing, follows the integration of people-clothing-environment, and proposes a wearer-centered design concept to deeply explore the rationality of outdoor sports smart clothing design methods. This paper further solves the key problems in the design process, and takes the fashion of clothing as the core principle to design the structure of clothing modeling. The fabric selection of suits is based on the principle of clothing comfort. The key is to realize the outdoor sports monitoring function of suits through sensing technology. The design uses Arduino as an electronic prototype platform, so as to detect the heart rate of the human body during exercise and the microclimate temperature under the clothes. This kind of suit with monitoring function is ultimately a combination of sensing device and clothing. It not only has monitoring function, but also has the aesthetic concept of clothing design and conforms to the performance of human body structure, which will provide reference and reference for the design of outdoor sports smart clothing.