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检索条件"主题词=convolutional autoencoder"

共 405 条记录，以下是81-90 订阅

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Deep convolutional autoencoder-based Lossy Image Compression 33

Deep Convolutional AutoEncoder-based Lossy Image Compression

引用

33rd Picture Coding Symposium (PCS)

作者： Cheng, Zhengxue Sun, Heming Takeuchi, Masaru Katto, Jiro Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo Japan

ISBN: (纸本)9781538641606

Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

关键词： convolutional autoencoder Image compression Deep learning Principal component analysis

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Ultrasonic Guided Wave Dispersion Compensation Based on Fourier Basis convolutional autoencoder 43

Ultrasonic Guided Wave Dispersion Compensation Based on Four...

引用

43rd Chinese Control Conference (CCC)

作者： Zeng, Jianxin Mei, Lin Li, Shuaiyong Li, MaoYang Yang, Zhang Chongqing Univ Posts & Telecommun Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Chongqing Special Equipment Inspect & Res Inst Chongqing 401121 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Ultrasonic guided waves are widely used signals in industry, but their inherent dispersion and multimodal characteristics significantly impact practical applications. Considering the frequency-dependent nature of guided wave signals, this paper proposes a Fourier-based convolutional autoencoder (FCAE) algorithm for dispersion compensation in ultrasonic guided wave signals, thoroughly investigating its practical application in handling ultrasonic guided wave signals. The neural network model combines the time-frequency analysis characteristics of Fourier bases and the nonlinear mapping capability of neural networks to learn the time of flight (TOF) of guided wave modes. Through this approach, the model successfully eliminates phase distortion and energy leakage caused by Lamb wave dispersion in guided wave signals, thereby significantly improving the quality and interpretability of the signals. The proposed Fourier-based neural network model's effectiveness in dispersion compensation is validated through simulation experiments, providing robust support for further applications of ultrasonic guided wave technology.

关键词： Fourier Basis convolutional autoencoder Ultrasonic Guided Wave Dispersion Compensation

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A Feature Extraction Method Based on convolutional autoencoder for Plant Leaves Classification

A Feature Extraction Method Based on Convolutional Autoencod...

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2nd IEEE Colombian Conference on Computational Intelligence (ColCACI)

作者： Paco Ramos, Mery Paco Ramos, Vanessa Loaiza Fabian, Arnold Osco Mamani, Erbert Univ Nacl Jorge Basadre Grohmann Tacna Peru

ISBN: (纸本)9781728116143

In this research, we present an approach based on convolutional autoencoder (CAE) and Support Vector Machine (SVM) for leaves classification of different trees. While previous approaches relied on image processing and manual feature extraction, the proposed approach operates directly on the image pixels, without any preprocessing. Firstly, we use multiple layers of CAE to learn the features of leaf image dataset. Secondly, the extracted features were used to train a linear classifier based on SVM. Experimental results show that the classifiers using these features can improve their predictive value, reaching an accuracy rate of 94.74 %.

关键词： Feature Extraction Image Processing Plant Classification convolutional autoencoder Deep Learning Computer Vision

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Latent Feature Separation and Extraction with Multiple Parallel Encoders for convolutional autoencoder

Latent Feature Separation and Extraction with Multiple Paral...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Kim, Jaehyun Kim, Myungjun Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon South Korea Ajou Univ Dept Artificial Intelligence Dept Ind Engn Suwon South Korea

ISBN: (纸本)9781665421973

Much of the real-world image data is unlabeled or mislabeled. Therefore, even if there is no label, if similar images can be grouped together with image data itself and used the group as a label, more image data can be effectively used in various tasks. This will be especially effective when dividing images belonging to the same domain into sub-groups. Therefore, in this study, we propose an image feature extraction method to be used for image clustering. The proposed feature extraction model is the Multi-head convolutional autoencoder (MCAE), which is a model composed of multiple encoders in parallel based on the convolutional autoencoder (CAE). The proposed model showed about 14% lower test reconstruction loss compared to CAE, and the correlation coefficient between extracted features was about 56% lower. In addition, as the results of clustering based on the extracted features, MCAEbased clustering showed about 3.5 times higher silhouette score than that CAE-based clustering.

关键词： feature extraction convolutional autoencoder Multi-head convolutional autoencoder multiple parallel encoders independence correlation coefficient image clustering

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Unsupervised convolutional autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases 6

Unsupervised Convolutional Autoencoder-Based Feature Learnin...

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6th Annual International Conference on Computer, Control, Informatics and its Applications (IC3INA)

作者： Pardede, Hilman F. Suryawati, Endang Sustika, Rika Zilvan, Vicky Indonesian Inst Sci Res Ctr Informat Bandung Indonesia

ISBN: (纸本)9781538657416

Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers.

关键词： Plant diseases detection convolutional autoencoder feature learning SVM

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Polycrystalline Silicon Wafer Scratch Segmentation based on Deep convolutional autoencoder

Polycrystalline Silicon Wafer Scratch Segmentation based on ...

引用

International Conference on Electronics, Information, and Communication (ICEIC)

作者： Ranjan, Navin Bhandari, Sovit Kim, Yeong-Chan Kim, Hoon Incheon Natl Univ IoT & Big Data Res Ctr Dept Elect Engn Incheon South Korea

ISBN: (纸本)9781665409346

Silicon chips are the backbone of the current digital era, and it is crucial to find or detect defects like a scratch on the surface of the silicon wafer during production to improve the yield. In the past, traditional machine learning and image processing methods based on handcrafted models or features have heavily relied on expertise, which is inefficient and expensive. This paper presents a machine learning- based method to segment scratch from Integrated Circuit images without any handcrafted features. The proposed method is based on a deep convolutional autoencoder, which learns the spatial relations between the pixels at the encoder and generates the scratch segmented image at the decoder. Our experiments show that our proposed model can efficiently and effectively learn to produce a scratch segmented image.

关键词： convolutional autoencoder segmentation silicon wafer scratch

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VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based convolutional autoencoder 4th

VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-B...

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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Xu, Zhenyu Zhang, Qieshi Hao, Fusheng Ren, Ziliang Kang, Yuhang Cheng, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783030880071;9783030880064

Visual Place Recognition (VPR) is a challenging task in Visual Simultaneous Localization and Mapping (VSLAM), which expects to find out paired images corresponding to the same place in different conditions. Although most methods based on convolutional Neural Network (CNN) perform well, they require a large number of annotated images for supervised training, which is time and energy consuming. Thus, to train the CNN in an unsupervised way and achieve better performance, we propose a new place recognition method in this paper. We design a VGG16-based convolutional autoencoder (VGG-CAE), which uses the features outputted by VGG16 as the label of images. In this case, VGG-CAE learns the latent representation from the label of images and improves the robustness against appearance and viewpoint variation. When deploying VGG-CAE, features are extracted from query images and reference images with post-processing, the Cosine similarities of features are calculated respectively and a matrix for feature matching is formed accordingly. To verify the performance of our method, we conducted experiments with several public datasets, showing our method achieves competitive results comparing to existing approaches.

关键词： Visual place recognition convolutional autoencoder VGG-16

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A Sparse convolutional autoencoder for Joint Feature Extraction and Clustering of Metastatic Prostate Cancer Images 22nd

A Sparse Convolutional Autoencoder for Joint Feature Extract...

引用

22nd International Conference on Artificial Intelligence in Medicine (AIME)

作者： Chen, Zhijun Sayar, Erolcan Zhang, Haoyue Richards, Helen Liu, Lucas Turkbey, Baris Haffner, Michael Harmon, Stephanie NCI Mol Imaging Branch NIH Bethesda MD 20892 USA Fred Hutchinson Canc Ctr Data Sci Integrated Res Ctr Seattle WA 98109 USA

ISBN: (纸本)9783031665349;9783031665356

Metastatic prostate cancer images contain rich and complex information about cellular features. However, due to high level pathogenomic diversity and lack of clinically-validated morphological characteristics, these images are often unlabeled or weakly labeled. Extracting meaningful features from these images using deep learning method is a challenging task. In this paper, we present an unsupervised sparse convolutional autoencoder (SCAE) model to tackle this problem. The imposed sparsity constraints enable the model to perform clustering during feature extraction. The experiments with 856 cores images show that the identified image feature clusters have distinctive cellular feature distributions. By fine-tuning the pretrained encoder module, our proposed model achieves comparable or superior performance to other benchmark models on molecular tumor subtype detection and mutation prediction, suggesting its potential for other downstream supervised tasks.

关键词： convolutional autoencoder sparsity metastatic prostate cancer

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DISEASE GRADING OF HETEROGENEOUS TISSUE USING convolutional autoencoder 14

DISEASE GRADING OF HETEROGENEOUS TISSUE USING CONVOLUTIONAL ...

引用

IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Zerhouni, Erwan Prisacari, Bogdan Zhong, Qing Wild, Peter Gabrani, Maria IBM Res Zurich Saeumerstr 4 CH-8803 Ruschlikon Switzerland Univ Hosp Zurich Inst Surg Pathol Schmelzbergstr 12 CH-8091 Zurich Switzerland

ISBN: (纸本)9781509011728

One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine the most relevant features automatically. In this paper, we propose a method of generating representative image signatures that are robust to tissue heterogeneity. At the core of our approach lies a novel deep-learning based mechanism to simultaneously produce representative image features as well as perform dictionary learning to further reduce dimensionality. By integrating this mechanism in a broader framework for disease grading, we show significant improvement in terms of grading accuracy compared to alternative local feature extraction methods.

关键词： Image Description convolutional autoencoder Tissue Heterogeneity Dimensionality Reduction

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DENOISING convolutional autoencoder BASED B-MODE ULTRASOUND TONGUE IMAGE FEATURE EXTRACTION 44

DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND ...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing Univ Posts & Telecommun Automat Dept Beijing 100876 Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China Univ Michigan Dept Linguist Ann Arbor MI 48109 USA

ISBN: (纸本)9781479981311

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1.

关键词： B-mode ultrasound tongue imaging unsupervised learning feature extraction convolutional autoencoder denoising convolutional autoencoder

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