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检索条件"任意字段=Neural and Stochastic Methods in Image and Signal Processing II"

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MAP Tomographic Reconstruction with a Spatially Adaptive Hierarchical image Model 25

MAP Tomographic Reconstruction with a Spatially Adaptive Hie...

引用

25th European signal processing Conference (EUSIPCO)

作者： Nikou, Christophoros Univ Ioannina Dept Comp Sci & Engn Ioannina Greece

ISBN: (纸本)9780992862671

A method for penalized likelihood tomographic reconstruction is presented which is based on a spatially adaptive stochastic image model. The model imposes onto the image a smoothing Gaussian prior whose parameters follow a Gamma distribution. Three variations of the model are examined: (i) a stationary model, where the Gamma distribution has the same constant parameter for the entire image, (ii) a non stationary model, where this parameter varies with respect to location and (iii) a non stationary directional model where the parameter varies also with respect to orientation (horizontal or vertical direction). In all cases, the MAP criterion provides a closed form solution for both the unknown image and the parameters of the model. Numerical experiments showed that the reconstructions obtained using the proposed image priors outperform the state of the art EM based methods.

关键词： stochastic systems

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Saccade gaze prediction using a recurrent neural network 24

Saccade gaze prediction using a recurrent neural network

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24th IEEE International Conference on image processing, ICIP 2017

作者： Ngo, Thuyen Manjunath, B.S. Department of Electrical and Computer Engineering University of California Santa Barbara United States

ISBN: (纸本)9781509021758

We present a model that generates close-to-human gaze sequences for a given image in the free viewing task. The proposed approach leverages recent advances in image recognition using convolutional neural networks and sequence modeling with recurrent neural networks. Feature maps from convolutional neural networks are used as inputs to a recurrent neural network. The recurrent neural network acts like a visual working memory that integrates the scene information and outputs a sequence of saccades. The model is trained end-to-end with real-world human eye-tracking data using back propagation and adaptive stochastic gradient descent. Overall, the proposed model is simple compared to the state-of-the-art methods while offering favorable performance on a standard eye-tracking data set. © 2017 IEEE.

关键词： Eye tracking

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CLUSTER CONVOLUTIONAL neural NETWORKS FOR FACIAL AGE ESTIMATION 24

CLUSTER CONVOLUTIONAL NEURAL NETWORKS FOR FACIAL AGE ESTIMAT...

引用

24th IEEE International Conference on image processing (ICIP)

作者： Shang, Chong Ai, Haizhou Tsinghua Univ Dept Comp Sci & Tech Tsinghua Natl Lab Info Sci & Tech Beijing Peoples R China

ISBN: (纸本)9781509021758

In computer vision tasks, such as age estimation problem, a typical deep learning model is insufficient to represent all the transformations between data and their labels. In this paper, we present a novel deep neural network called cluster convolutional neural network (Cluster-CNN) to estimate ages from facial images, which is based on the concept that clustering rich CNN features is able to assist the network to efficiently tackle the nonlinearity of this task. In particular, given a facial image, we first coarsely normalize a face to a standard size according to the distance between two eyes. Then the normalized face is fed into a Cluster-CNN to get the prediction. The cluster module we propose in this paper is able to capture multimodal transformations, and is differentiable, thus making it capable to be optimized in a unified back-propagation fashion. We evaluate the proposed method on the popular MORPH ii dataset. To the best of our knowledge, our model outperforms all the other previous methods by a large margin.

关键词： Age estimation cluster CNN face age

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A Feature Learning Approach for image Retrieval 24th

A Feature Learning Approach for Image Retrieval

引用

24th International Conference on neural Information processing (ICONIP)

作者： Yao, Junfeng Yu, Yao Deng, Yukai Sun, Changyin Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Southeast Univ Sch Automat Naijing 210096 Peoples R China

ISBN: (纸本)9783319700960;9783319700953

Extraction of effective image features is the key to the content-based image retrieval task. Recently, deep convolutional neural networks have been widely used in learning image features and have achieved top results. Based on CNNs, metric learning methods like contrastive loss and triplet loss have been proved effective in learning discriminative image features. In this paper, we propose a new supervised signal to train convolutional neural networks. This step could ensure that the features obtained are well differentiated in space, which is very suitable for image retrieval task. We give an example on MNIST to illustrate the intent of this loss function. Also, we evaluate our method on two datasets including CUB-200-2011, CARS196. The experimental results show that the retrieval effect is fairly good on this two datasets. Besides, our loss function is much easier to implement and train.

关键词： image retrieval Convolutional neural networks Metric learning

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A DEEP LEARNING BASED ALTERNATIVE TO BEAMFORMING ULTRASOUND imageS

A DEEP LEARNING BASED ALTERNATIVE TO BEAMFORMING ULTRASOUND ...

引用

IEEE International Conference on Acoustics, Speech and signal processing

作者： Arun Asokan Nair Trac D. Tran Austin Reiter Muyinatu A. Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins University Baltimore USA Department of Computer Science Johns Hopkins University Baltimore USA

ISBN: (纸本)9781538646595

Deep learning methods are capable of performing sophisticated tasks when applied to a myriad of artificial intelligent (AI) research fields. In this paper, we introduce a novel approach to replace the inherently flawed beamforming step during ultrasound image formation by applying deep learning directly to RF channel data. Specifically, we pose the ultrasound beamforming process as a segmentation problem and apply a fully convolutional neural network architecture to segment anechoic cysts from surrounding tissue. We train our network on a dataset created using the Field ii ultrasound simulation software to simulate plane wave imaging with a single insonification angle. We demonstrate the success of our architecture in extracting tissue information directly from the raw channel data, which completely bypasses the beam-forming step that would otherwise require multiple insonification angles for plane wave imaging. Our simulated results produce mean Dice coefficient of 0.98 ± 0.02, when measuring the overlap between ground truth cyst locations and cyst locations determined by the network. The proposed approach is promising for developing dedicated deep-learning networks to improve the real-time ultrasound image formation process.

关键词： Deep Learning Beamforming Ultrasound Imaging Machine Learning image Segmentation Beamforming Ultrasonography image segmentation Ultrasonic Waves Artificial Intelligence Machine Learning Insonification image formation Plane waves images

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FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS 24

FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS

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24th IEEE International Conference on image processing (ICIP)

作者： Li, Lei Feng, Xiaoyi Jiang, Xiaoyue Xia, Zhaoqiang Hadid, Abdenour Northwestern Polytech Univ Xian Shaanxi Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland

ISBN: (纸本)9781509021758

Convolutional neural networks (CNNs) have achieved excellent performance in the field of pattern recognition when huge amount of training data is available. However, training a CNN model is less obvious when only a limited amount of data is given such as in the case of face anti-spoofing problem. It is indeed not easy to collect very large sets of fake faces. Especially for the fully-connected layers, tens of thousands of parameters need to be learned. To tackle this problem of lack of training data in face anti-spoofing, we propose to explore the incorporation of hand-crafted features in the CNN framework. In our proposed approach, the color local binary patterns (LBP) features are extracted from the convolutional feature maps, which are fine tuned based on the VGG-face model. These features are then fed into support vector machine (SVM) classifier. Extensive experiments are conducted on two benchmark and publicly available databases showing very interesting performance compared to state-of-the-art methods.

关键词： Face Feature extraction image color analysis Databases Machine learning Training Support vector machines

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Deep Learning for SAR image Formation 24

Deep Learning for SAR Image Formation

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Conference on Algorithms for Synthetic Aperture Radar imagery XXIV

作者： Mason, Eric Yonel, Bariscan Yazici, Birsen Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

ISBN: (数字)9781510609044

ISBN: (纸本)9781510609037;9781510609044

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

关键词： synthetic aperture radar SAR deep learning inverse problems recurrent neural networks RNN radar imaging autofocus neural network

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CAN MICRO-EXPRESSION BE RECOGNIZED BASED ON SINGLE APEX FRAME?

CAN MICRO-EXPRESSION BE RECOGNIZED BASED ON SINGLE APEX FRAM...

引用

IEEE International Conference on image processing

作者： Yante Li Xiaohua Huang Guoying Zhao Center for Machine Vision and Signal Analysis University of Oulu

Micro-expressions are rapid and subtle facial movements such that they are difficult to detect and recognize. Most of recent works have attempted to recognize micro-expression by using the spatial and dynamic information from the video clip. Physiological studies have demonstrated that the apex frame can convey the most emotion expressed in facial expression. It may be reasonable to use apex frame for improving micro-expression recognition. However, it is wonder how much apex frames contribute to micro-expression recognition. In this paper, we primarily focus on resolving the contribution-level by using apex frame for micro-expression recognition. Firstly, we propose a new method to detect the apex frame in frequency domain, as it is found that apex frame has very correlated relationship with the amplitude change in frequency domain. Secondly, we propose to use deep convolutional neural network (DCNN) on apex frame to recognize micro-expression. Intensive experimental results on CASME ii database shows that our method has achieved considerably improvement compared with the state-of-the-art methods in micro-expression recognition. These results also demonstrate that apex frame can express the major emotion in micro-expression.

关键词： Frequency-domain analysis Face recognition Machine learning Three-dimensional displays Optical imaging Integrated optics

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A Fully Convolutional neural Network for Beamforming Ultrasound images

A Fully Convolutional Neural Network for Beamforming Ultraso...

引用

IEEE International Ultrasonics Symposium

作者： Arun Asokan Nair Mardava Rajugopal Gubbi Trac Duy Tran Austin Reiter Muyinatu A. Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD Department of Computer Science Johns Hopkins University Baltimore MD Department of Biomedical Engineering Johns Hopkins University Baltimore MD

Plane wave ultrasound imaging is one of the fastest ultrasound methods available to reduce latency for ultrasound-based robotic tracking tasks. However, the presence of acoustic clutter and speckle in the images can confuse robotic tracking algorithms. In addition, multiple plane wave insonification angles are often necessary to generate good-quality images, which further reduces the speed of the tracking process. To overcome these challenges, we are exploring deep learning as a method to extract pertinent information directly from raw radiofrequency channel data to locate targets of interest from a single plane wave insonification. Particularly, in this work, we trained a deep convolutional neural network (CNN) with 50,000 Field-ii simulations corresponding to a single cyst in tissue insonified by one plane wave transmitted at 0 degrees. The simulated cyst radius, axial and lateral positions were varied, along with the simulated tissue sound speed. The output of the training process is an interpretable segmentation mask that is free of clutter and speckle, which we call a CNN-Based image. An additional dataset of 100 simulations was created and two cyst targets in an experimental phantom were imaged to test our approach. The Dice Similarity Coefficient (DSC), representing the overlap between the true cyst location and the cyst location in the CNN-Based image, was 0.91 for simulated data and 0.74 for experimental data. The network was generally sensitive to cyst radius, with mean DSCs increasing from 0.91 to 0.97 when the cyst radius was ≥ 5 mm. A robot controlled ultrasound probe enabled volumetric reconstruction of CNN-Based images, revealing the three-dimensional structure of the two cysts in the phantom. These results demonstrate that a deep neural network trained exclusively with simulated data can generalize to experimental data, which is promising for the development of deep learning methods as an alternative to traditional ultrasound beamforming for robotic

关键词： Ultrasonic imaging image segmentation Robots Array signal processing Phantoms Task analysis

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Use of OWA Operators for Feature Aggregation in image Classification

Use of OWA Operators for Feature Aggregation in Image Classi...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Pagola, Miguel Forcen, Juan I. Barrenechea, Edurne Lopez-Molina, Carlos Bustince, Humberto Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain

ISBN: (纸本)9781509060344

Feature aggregation is a crucial step in many methods of image classification, like the Bag-of-Words (BoW) model or the Convolutional neural Networks (CNN). In this aggregation step, usually known as spatial pooling, the descriptors of neighbouring elements within a region of the image are combined into a local or a global feature vector. The combined vector must contain relevant information, while removing irrelevant and confusing details. Maximum and average are the most common aggregation functions used in the pooling step. To improve the aggregation of relevant information without degrading their discriminative power for classification in this work we propose the use of Ordered Weighted operators. We provide an extensive evaluation that shows that the final result of the classification using OWA aggregation is always better than average pooling and better than maximum pooling when dealing with small dictionary sizes.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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