“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As time-series data, the spatial and temporal dependencies of the EEG signals between the time points and the different channels contain important information for accurate classification. So far, Transformer-based approaches have not been widely explored in motor-imagery EEG classification and visualization, especially lacking general models based on cross-individual validation. Taking advantage of the Transformer model and the spatial-temporal characteristics of the EEG signals, we designed Transformer-based models for classifications of motor imagery EEG based on the PhysioNet dataset. With 3s EEG data, our models obtained the best classification accuracy of 83.31%, 74.44%, and 64.22% on two-, three-, and four-class motor-imagery tasks in cross-individual validation, which outperformed other state-of-the-art models by 0.88%, 2.11%, and 1.06%. The inclusion of the positional embedding modules in the Transformer could improve the EEG classification performance. Furthermore, the visualization results of attention weights provided insights into the working mechanism of the Transformer-based networks during motor imagery tasks. The topography of the attention weights revealed a pattern of event-related desynchronization (ERD) which was consistent with the results from the spectral analysis of Mu and beta rhythm over the sensorimotor areas. Together, our deep learning methods not only provide novel and powerful tools for classifying and understanding EEG data but also have broad applications for brain-computer interface (BCI) systems. 

/pdf/a-transformer-based-approach-combining-deep-learning-network-2zsp493c.pdf

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

Automatic image-based brick segmentation and crack detection of masonry walls using machine learning

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

https://downloads.hindawi.com/journals/bn/2022/7725597.pdf

Early Stroke Prediction Methods for Prevention of Strokes

Classification of EEG signals is a cornerstone of building the motor-imagery (MI) based Brain-computer interface (BCI) systems. EEG signals differ from one subject to another and even for the same subject among different trials, and this is why designing a general classification model is still debated. Deep learning is dominant in so many fields like computer vision and natural language processing but it is still under investigation for EEG signals classification. We followed a new trend in EEG signals classification in which these signals are transformed into images, and so classifying such signals become an image classification problem where Deep learning can work well. The Physionet dataset for EEG motor movement/imagery tasks was used which consists of 109 subjects and the motor imagery EEG signals for three frequency bands (Delta [0.5-4 Hz], Mu [8–13 Hz], and Beta [13–30 Hz]) was transformed into 3-channel images (one channel for each band) using the Azimuthal equidistant projection and Clough-Tocher algorithm for interpolation. These 2-D images represent the input data to our model which consists of Deep Convolutional Neural Network (DCNN) to extract the spatial and frequency features followed by Long Short Term Memory (LSTM) to extract temporal features and then finally to be classified into 5 different classes (4 motor imagery tasks and one rest). Our results were promising (70.64% average accuracy) and 5% better than the results of Support Vector Machine (SVM) method over the same dataset. We noticed that taking Delta band into account increases the classification accuracy by 2.51%.

Multi-Class Classification of Motor Imagery EEG Signals Using Image-Based Deep Recurrent Convolutional Neural Network

In this paper we introduce a novel machine learning-based fully automatic approach for the semantic analysis and documentation of masonry wall images, performing in parallel automatic detection and virtual completion of occluded or damaged wall regions, and brick segmentation leading to an accurate model of the wall structure. For this purpose, we propose a four-stage algorithm which comprises three interacting deep neural networks and a watershed transform-based brick outline extraction step. At the beginning, a U-Net-based sub-network performs initial wall segmentation into brick, mortar and occluded regions, which is followed by a two-stage adversarial inpainting model. The first adversarial network predicts the schematic mortar-brick pattern of the occluded areas based on the observed wall structure, providing in itself valuable structural information for archeological and architectural applications. The second adversarial network predicts the pixels’ color values yielding a realistic visual experience for the observer. Finally, using the neural network outputs as markers in a watershed-based segmentation process, we generate the accurate contours of the individual bricks, both in the originally visible and in the artificially inpainted wall regions. Note that while the first three stages implement a sequential pipeline, they interact through dependencies of their loss functions admitting the consideration of hidden feature dependencies between the different network components. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state-of-the-art is given. The experiments confirmed that the proposed method outperforms the reference techniques both in terms of wall structure estimation and regarding the visual quality of the inpainting step, moreover it can be robustly used for various different masonry wall types.

Deep Learning-Based Masonry Wall Image Analysis

Nowadays there is an increasing need for using artificial intelligence techniques in image-based documentation and survey in archeology, architecture or civil engineering applications. Brick segmentation is an important initial step in the documentation and analysis of masonry wall images. However, due to the heterogeneous material, size, shape and arrangement of the bricks, it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches. In this paper, we propose a new technique which combines the strength of deep learning for brick seed localization, and the Watershed algorithm for accurate instance segmentation. More specifically, we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process, which provides as output the accurate contours of the individual bricks, and also separates them from the mortar regions. For training the network and evaluating our results, we created a new test dataset which consist of 162 hand-labeled images of various wall categories. Quantitative evaluation is provided both at instance and at pixel level, and the results are compared to two reference methods proposed for wall delineation, and to a morphology based brick segmentation approach. The experimental results showed the advantages of the proposed U-Net markered Watershed method, providing average F1-scores above 80%.

/pdf/cnn-based-watershed-marker-extraction-for-brick-segmentation-23qhsy70tj.pdf

CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

3D vehicle shape completion is a key task in urban environment reconstruction, since the available sensors and 3D scanning procedures (such as Mobile Laser Scanning) in an outdoor city scene cannot usually extract the entire car shapes, which can be necessary for specifying their geometric properties for further analysis or realistic visualization. In this paper, we propose a novel multi-view based 3D object point cloud completion technique. In contrast to existing approaches, our method operates on 2D images formed by projecting the point cloud from several virtual camera positions around the object of interest. Both color and geometrical information is considered during the process, generating dense textured point clouds, displaying realistic patterns in the missing regions from the partial inputs. We present both quantitative and qualitative tests on various synthetic and real laser scanned vehicle point clouds, which demonstrate that our method surpasses existing state-of-the-art approaches. By applying it to vehicles from the Shapenet dataset, our approach outperforms recent techniques in terms of Earth Mover’s Distance (EMD) and Chamfer Distance (CD) by 43.8% and 12.17%, respectively.

Multi-view Based 3D Point Cloud Completion Algorithm for Vehicles

Iris recognition is a type of biometrics technology that is based on physiological features of the human body. The objective of this research is to recognize and identify iris among many irises that are stored in a visual database. This study employed a left and right iris biometric framework for inclusion decision processing by combining image processing and artificial bee colony. The proposed approach was evaluated on a visual database of 280 colored iris pictures. The database was then divided into 28 clusters. Images were preprocessed and texture features were extracted based Gabor filters to capture both local and global details within an iris. The technique begins by comparing the attributes of the online-obtained iris picture with those of the visual database. This technique either generates a reject or approve message. The consequences of the intended work reflect the output’s accuracy and integrity. This is due to the careful selection of attributes, as well as the deployment of an artificial bee colony and data clustering, which decreased complexity and eventually increased identification rate to 100%. We demonstrate that the proposed method achieves state-of-the-art performance and that our recommended procedures outperform existing iris recognition systems.

/pdf/iris-recognition-based-on-2d-gabor-filter-dvc7ynu2.pdf

Iris recognition based on 2D Gabor filter

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