Skin visualization for beauty industries using deep learning is discussed. UV skin images were taken by a medical dermoscopy digital camera, and we created datasets for training. Neural networks called U-net and convo...
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ISBN:
(纸本)9784885523281
Skin visualization for beauty industries using deep learning is discussed. UV skin images were taken by a medical dermoscopy digital camera, and we created datasets for training. Neural networks called U-net and convolutional autoencoder were constructed and trained with our datasets. Once our neural network was trained, skin images that approximated the UV image could be generated without the medical camera. The performance of our U-net and convolutional autoencoder is discussed.
Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers ha...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169262
Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers have proposed creative learning representations to utilize the unparalleled generalization capabilities of such structures for unsupervised applications commonly called deep clustering. This paper presents a comprehensive analysis of popular deep clustering architectures including deep autoencoders and convolutional autoencoders to study how network topology, hyperparameters and clustering coefficients impact accuracy. Three popular benchmark datasets are used including MNIST, CIFAR10 and SVHN to ensure data independent results. In total, 20 different pairings of topologies and clustering coefficients are used for both the standard and convolutional autoencoder architectures across three different datasets for a joint analysis of 120 unique combinations with sufficient repetitive testing for statistical significance. The results suggest that there is a general optimum when it comes to choosing the coding layer (latent dimension) size which is correlated to an extent with the complexity of the dataset. Moreover, for image datasets, when color makes a meaningful contribution to the identity of the observation, it also helps improve the subsequent deep clustering performance.
Anomaly detection is the task of learning patterns of normal data and identifying data with other characteristics. As various types of sensors are attached to vehicle, healthcare equipment, production facilities, etc....
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ISBN:
(纸本)9783030295134;9783030295127
Anomaly detection is the task of learning patterns of normal data and identifying data with other characteristics. As various types of sensors are attached to vehicle, healthcare equipment, production facilities, etc., detecting anomalies in multi-channel sensor data has become very important. In sensor data, abnormal signals occur temporally during certain intervals of a few channels. It is very important to capture the characteristics of individual channel and cross-channel relationship in order to detect abnormal signals that occur locally for a short time interval. We propose a channel-wise reconstructionbased anomaly detection framework which consists of two parts: channel-wise reconstruction part with convolutional autoencoder (CAE) and anomaly scoring part with machine learning algorithms, isolation forest (iForest) and local outlier factor (LOF). CAE learns the features of normal signal data and measures channel-wise reconstruction error. We applied the symmetric skip-connections technique to build a CAE model for higher reconstruction performance. Given the channel-wise reconstruction error as an input, iForest and LOF summarize it to anomaly score. We present our results on data collected from real sensors attached to vehicle and show that the proposed framework outperforms traditional reconstruction-based anomaly detection methods and one-class classification methods.
The inherent smaller radar cross sections of vulnerable road users resulting in smaller signal-to-noise-ratios make an accurate detection of them somewhat challenging. Mutual radar interference in typical automotive s...
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ISBN:
(纸本)9781728189420
The inherent smaller radar cross sections of vulnerable road users resulting in smaller signal-to-noise-ratios make an accurate detection of them somewhat challenging. Mutual radar interference in typical automotive scenarios further imposes the difficulty of a target detection by additionally raising the noise floor. The traditional signal processing pipeline consists of multiple but separate stages for interference detection, mitigation and target detection. In this paper, a convolutional neural network based autoencoder architecture is used to perform a combined single-stage target detection while generalizing over different interference noise. The proposed approach achieves significant improvement over state-of-the-art methods while preserving the instance of each target and is able to identify them uniquely in case of a partial occlusion or overlapping of multiple targets.
Tools for automatic image analysis are gaining importance in the clinical workflow, ranging from time-saving tools in diagnostics to real-time methods in image-guided interventions. Over the last years, ultrasound (US...
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ISBN:
(纸本)9781728195742
Tools for automatic image analysis are gaining importance in the clinical workflow, ranging from time-saving tools in diagnostics to real-time methods in image-guided interventions. Over the last years, ultrasound (US) imaging has become a promising modality for image guidance due to its ability to provide volumetric images of soft tissue in real-time without using ionizing radiation. One key challenge in automatic US image analysis is the identification of suitable features to describe the image or regions within, e.g. for recognition, alignment or tracking tasks. In recent years, features that were learned data-drivenly provided promising results. Even though these approaches outperformed hand-crafted feature extractors in many applications, there is still a lack of feature learning for local description of three-dimensional US (3DUS) images. In this work, we present a completely data-driven feature learning approach for 3DUS images for usage in target tracking. To this end, we use a 3D convolutional autoencoder (AE) with a custom loss function to encode 3DUS image patches into a compact latent space that serves as a general feature description. For evaluation, we trained and tested the proposed architecture on 3DUS images of the liver and prostate of five different subjects and assessed the similarity between the decoded patches and the original ones. Subject- and organ-specific as well as general AEs are trained and evaluated. Specific AEs could reconstruct patches with a mean Normalized Cross Correlation of 0.85 and 0.81 at maximum in liver and prostate, respectively. It can also be shown that the AEs are transferable across subjects and organs, with a small accuracy decrease to 0.83 and 0.81 (liver, prostate) for general AEs. In addition, a first tracking study was performed to show feasibility of tracking in latent space. In this work, we could show that it is possible to train an AE that is transferable across two target regions and several subjects. Hence,
Pressures for survival make sensory circuits adapted to a species' natural habitat and its behavioral challenges. Thus, to advance our understanding of the visual system, it is essential to consider an animal'...
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Pressures for survival make sensory circuits adapted to a species' natural habitat and its behavioral challenges. Thus, to advance our understanding of the visual system, it is essential to consider an animal's specific visual environment by capturing natural scenes, characterizing their statistical regularities, and using them to probe visual computations. Mice, a prominent visual system model, have salient visual specializations, being dichromatic with enhanced sensitivity to green and UV in the dorsal and ventral retina, respectively. However, the characteristics of their visual environment that likely have driven these adaptations are rarely considered. Here, we built a UV-green-sensitive camera to record footage from mouse habitats. This footage is publicly available as a resource for mouse vision research. We found chromatic contrast to greatly diverge in the upper, but not the lower, visual field. Moreover, training a convolutional autoencoder on upper, but not lower, visual field scenes was sufficient for the emergence of color-opponent filters, suggesting that this environmental difference might have driven superior chromatic opponency in the ventral mouse retina, supporting color discrimination in the upper visual field. Furthermore, the upper visual field was biased toward dark UV contrasts, paralleled by more light-offset-sensitive ganglion cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution.
Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorit...
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Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking.
Visualizations help decipher latent patterns in music and garner a deep understanding of a song's characteristics. This paper offers a critical analysis of the effectiveness of various state-of-the-art Deep Neural...
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Visualizations help decipher latent patterns in music and garner a deep understanding of a song's characteristics. This paper offers a critical analysis of the effectiveness of various state-of-the-art Deep Neural Networks in visualizing music. Several implementations of auto encoders and genre classifiers have been explored for extracting meaningful features from audio tracks. Novel techniques have been devised to map these audio features to parameters that drive visualizations. These methodologies have been designed in a manner that enables the visualizations to be responsive to the music as well as provide unique visual experiences across different songs.
Defect inspection is extremely crucial to ensure the quality of steel surface. It affects not only the subsequent production, but also the quality of the end-products. However, due to the rare occurrence and appearanc...
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Defect inspection is extremely crucial to ensure the quality of steel surface. It affects not only the subsequent production, but also the quality of the end-products. However, due to the rare occurrence and appearance variations of defects, surface defect identification of steels has always been a challenging task. Recently, deep learning methods have shown outstanding performance in image classification, especially when there are enough training samples. Since most sample images of steel surface are unlabeled, a new semi-supervised learning method is proposed to classify surface defects of steels. The new method is named CAE-SGAN, as it is based on convolutional autoencoder (CAE) and semi-supervised Generative Adversarial Networks (SGAN). CAE-SGAN first trains a stacked CAE through massive unlabeled data. Considering the appearance variations of defects, the passthrough layer is used to help CAE extract fine-grained features. After CAE is trained, the encoder network of CAE is reserved as the feature extractor and fed into a softmax layer to form a new classifier. SGAN is introduced for semi-supervised learning to further improve the generalization ability of the new method. The classifier is trained with images collected from real production lines and images randomly generated by SGAN. Extensive experiments are carried out with samples captured from different steel production lines, and the results Indicate that CAE-SGAN had yielded best performances compared with traditional methods. Especially for hot rolled plates, the classification rate is improved by around 16%.
Industry 4.0 encapsulates methods, technologies, and procedures that transform data into informed decisions and added value in an industrial context. In this regard, technologies such as Virtual Metrology or Soft Sens...
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Industry 4.0 encapsulates methods, technologies, and procedures that transform data into informed decisions and added value in an industrial context. In this regard, technologies such as Virtual Metrology or Soft Sensing have gained much interest in the last two decades due to their ability to provide valuable knowledge for production purposes at limited added expense. However, these technologies have struggled to achieve wide-scale industrial adoption, largely due to the challenges associated with handling complex data structures and the feature extraction phase of model building. This phase is generally hand-engineered and based on specific domain knowledge, making it time consuming, difficult to automate, and prone to loss of information, thus ultimately limiting portability. Moreover, in the presence of complex data structures, such as 2-dimensional input data, there are no established procedures for feature extraction. In this paper, we present a Deep Learning approach for Virtual Metrology, called DeepVM, that exploits semi-supervised feature extraction based on convolutional autoencoders. The proposed approach is demonstrated using a real world Semiconductor Manufacturing dataset where the Virtual Metrology input data is 2-dimensional Optical Emission Spectrometry data. The feature extraction method is tested with different types of state-of-the-art autoencoder. (C) 2019 Elsevier Ltd. All rights reserved.
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