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检索条件"任意字段=Conference on Multimodal Image Exploitation and Learning"

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multimodal learning based threat detection in dual view, dual energy X-ray images

Multimodal learning based threat detection in dual view, dua...

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conference on multimodal image exploitation and learning

作者： Rameshan, Renu M. Goel, Somya Singh, Kuldeep Sharma, Krishan Prabhu, Anoop G. Vehant Technol Pvt Ltd B 24Sect 59 Noida India

ISBN: (纸本)9781510673854;9781510673847

Explosive detection in dual energy X-ray systems is a difficult problem owing to the fact that we don't have enough information to estimate the effective atomic number and density of a material. Though there are several approximations available in the literature, building a solution with an acceptable true positive and false positive rate is not trivial. In this work we exploit the learning capability of a multimodal neural network for achieving a high detection rate and an acceptable false positive rate. We also show that, using a guided filter based fusion for fusing the high and low energy images leads to fused images that have a high mutual information w.r.t. the high and low images, than the existing solutions. This fused image is one of the inputs to the neural network, the other being a material dependent image that we create from the high and low energy images. The proposed solution has a high recall.

关键词： Explosive detection multimodal learning image fusion dual energy X-ray

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Derivation, Optimization, and Comparative Analysis of Support Vector Machines Application to Multi-Class image Data

Derivation, Optimization, and Comparative Analysis of Suppor...

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conference on multimodal image exploitation and learning

作者： Shekhar, Avi Saeed, Amir K. Johnson, Benjamin A. Rodriguez, Benjamin M. Johns Hopkins Univ Whiting Sch Engn 3400 N Charles St Baltimore MD 21218 USA

ISBN: (纸本)9781510673854;9781510673847

Support Vector Machines (SVM) have emerged as a powerful and versatile machine learning technique for solving classification and regression problems. This paper presents a thorough review of SVM, encompassing its motivation, derivation of the optimization problem, the utilization of kernels for data transformation, and a comprehensive analysis of solution methods. The review is supported by experiments conducted on a data set derived from the Traffic Sign data set. The motivation for SVM lies in its ability to address complex classification tasks by transforming the data into a higher-dimensional feature space. This is particularly beneficial for data sets derived from multiple sources. The findings presented in this paper contribute to a better understanding of SVM's capabilities.

关键词： support vector machine machine learning optimization multimodal

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Alpha-rooting and Correlation Method of image Enhancement

Alpha-rooting and Correlation Method of Image Enhancement

引用

conference on multimodal image exploitation and learning

作者： Grigoryan, Artyom M. Agaian, Sos S. Univ Texas San Antonio San Antonio TX 78249 USA CUNY CSI New York NY 10021 USA

ISBN: (纸本)9781510650770;9781510650763

This paper analyzes the known method of the Fourier transforms-based alpha-rooting in image enhancement and describes a new method of alpha-rooting, by using the autocorrelation function of the image. The alpha-rooting in the frequency-domain can be described by the Taylor series, as well as in the spatial domain, by using the inverse 2-D DFT. In such a series, the alpha-rooting is the convolution of the image with the series of the autocorrelation functions. The application of the Taylor series in alpha-rooting allows to use the parameterized filters even for parameter alpha in a much larger interval than [0,1]. Examples of application of these two filters for enhancement of the grayscale images are given.

关键词： image enhancement alpha-rooting correlation function Fourier transform

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multimodal Dialog for Browsing Large Visual Catalogs using Exploration-exploitation Paradigm in a Joint Embedding Space 19

Multimodal Dialog for Browsing Large Visual Catalogs using E...

引用

ACM International conference on Multimedia Retrieval (ICMR)

作者： Bhattacharya, Indrani Chowdhury, Arkabandhu Raykar, Vikas C. Rensselaer Polytech Inst Troy NY 12180 USA Rice Univ Houston TX USA IBM Res Bangalore Karnataka India

ISBN: (纸本)9781450367653

We present a multimodal dialog (MMD) system to assist online customers in visually browsing through large catalogs. Visual browsing allows customers to explore products beyond exact search results. We focus on a slightly asymmetric version of a complete MMD system, in that our agent can understand both text and image queries, but responds only in images. We formulate our problem of lshowing the k best images to a userz, based on the dialog context so far, as sampling from a Gaussian Mixture Model (GMM) in a high dimensional joint multimodal embedding space. The joint embedding space is learned by Common Representation learning and embeds both the text and the image queries. Our system remembers the context of the dialog, and uses an exploration-exploitation paradigm to assist in visual browsing. We train and evaluate the system on an MMD dataset that we synthesize from large catalog data. Our experiments and preliminary human evaluation show that the system is capable of learning and displaying relevant products with an average cosine similarity of 0.85 to the ground truth results, and is capable of engaging human users.

关键词： multimodal dialog visual browsing common representation learning interactive AI agent multimodal content analysis and retrieval online human-computer interaction

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Brightening the shadows: low-light image feature enhancement

Brightening the shadows: low-light image feature enhancement

引用

conference on multimodal image exploitation and learning

作者： Halford, Gavin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

Low-light image enhancement plays a crucial role for applications in security, photography, medical imaging, and scientific research. Traditional enhancement methods, including multi-spectral hardware and contrast adjustments via computer vision, often fall short due to current hardware limitations or the sparse data available in low-light conditions. This paper introduces an innovative approach that significantly improves the brightness and overall quality of low-light images, focusing on enhanced feature extraction. Our method efficiently and accurately compensates for missing data in real-time, making it highly suitable for scenarios that demand immediate processing. This is particularly beneficial for surveillance applications, where the clarity of images is essential for swift decision-making.

关键词： LOL Dataset Low-Light Enhancement Efficient AI

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Alpha Mean Trim Texture descriptors for Optical Coherence Tomography Eye Classification

Alpha Mean Trim Texture descriptors for Optical Coherence To...

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conference on multimodal image exploitation and learning

作者： Liew, Alex Ryan, Larry Agaian, Sos CUNY Grad Ctr Dept Comp Sci 365 5th Ave New York NY 10016 USA

ISBN: (纸本)9781510650770;9781510650763

Accurate ocular disorder classification and estimation of cornea depth and morphological changes depends on clear imaging of the affected structures. Ophthalmologists typically employ Optical Coherence Tomography (OCT) to help diagnose these conditions. This paper presents a new method called Alpha Mean Trim Local Binary Pattern (AMT-LPB) for automated texture classification of specific macular disease detected on OCT images of the retinal membrane. The performance of the proposed method achieved an overall accuracy of 99% using 10-fold cross-validation on the Duke University dataset [9].

关键词： Alpha Mean Trim Local Binary Patterns Speckle noises image flattening R-Squared Fitness Function

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Quaternion Fourier Transform-Based Alpha-Rooting Color image Enhancement in 2 Algebras: Commutative and Non-Commutative

Quaternion Fourier Transform-Based Alpha-Rooting Color Image...

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conference on multimodal image exploitation and learning

作者： Grigoryan, Artyom M. Gomez, Alexis A. Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9781510673854;9781510673847

In this paper, we describe new methods of color image enhancement alpha-rooting by the 2D quaternion discrete Fourier transform (QDFT) in the commutative algebra, or the (2,2)-model. In this model, the concept of the convolution is unique, which is very important when transforming tasks with color images into the frequency domain. Also, there are only two types of the exponential function and therefore only two QDFTs. Both these transforms can be used to reduce the convolution to operation of multiplication. Illustrative examples on color image enhancement are given. Measures of the image enhancement and selection of the best parameters of alpha-rooting are described. A comparison with the traditional non-commutative quaternion algebra is discussed and shown that the (2,2)-model is more effective in image enhancement.

关键词： Color image enhancement quaternion Fourier transform alpha-rooting

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ROBUST COUPLING IN SPACE OF SPARSE CODES FOR MULTI-VIEW RECOGNITION 23

ROBUST COUPLING IN SPACE OF SPARSE CODES FOR MULTI-VIEW RECO...

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23rd IEEE International conference on image Processing (ICIP)

作者： Taalimi, Ali Rahimpour, Alireza Capdevila, Cristian Zhang, Zhifei Qi, Hairong Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781467399616

Classical dictionary learning algorithms that rely on a single source of information have been successfully used for classification tasks. Additionally, the exploitation of multiple sources has shown to be advantageous in challenging real world situations. We propose a new framework to exploit robust modality fusion in classification in order to achieve better classification performance than single source methods. multimodal learning is able to leverage any correlations between sensor modalities found in the data. We propose a new bilevel optimization, referred to as (MCJWDL). We perform supervised dictionary learning while forcing a coupling between the resulting sparse codes from different sources of information. Extensive experiments demonstrate that MCJWDL outperforms state-of-the-art sparse representation and dictionary learning approaches for the multi-view object and multi-view action recognition.

关键词： multimodal classification Robust feature fusion Dictionary learning Coupling

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No-reference Transform-Based Quality Assessment for Imaging Applications

No-reference Transform-Based Quality Assessment for Imaging ...

引用

conference on multimodal image exploitation and learning

作者： Voronin, V. Zhdanova, M. Zelensky, A. Agaian, S. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia CUNY Dept Comp Sci New York NY 10021 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

This paper aims to introduce a new transform domain no-reference (NR) image quality assessment that predicts the perceptual quality for improving imaging systems performance. The main idea is that enhancing the contrast of diverse real-world digital images by creating more high-frequency content in the improved image than the original image. The proposed measure uses different fast orthogonal transforms, such as Fourier and Fibonacci. A generalized transform-based model of local transform-based coefficients is derived and transformed the model parameters into features used for perceptual image quality score prediction. To test the performance of the proposed algorithm, we use the well-known and publicly available database TID2008. The Pearson correlation coefficient is utilized to measure and compare the proposed quality measures performance with state-of-the-art approaches.

关键词： quality measure frequency-domain transform no-reference (NR) image quality image enhancement

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Quality Assessment with Deep learning for Imaging Applications

Quality Assessment with Deep Learning for Imaging Applicatio...

引用

conference on multimodal image exploitation and learning

作者： Voronin, V. Zelensky, A. Zhdanova, M. Semenishchev, E. Frantc, V Siryakov, A. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia CUNY Dept Comp Sci New York NY 10021 USA

ISBN: (纸本)9781510650770;9781510650763

To establish stable video operations and services while maintaining high quality of experience, perceptual video quality assessment becomes an essential research topic in video technology. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. The paper presents a novel visual quality metric for video quality assessment. To address this problem, we study the of neural networks through the robust optimization. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the MCL-V dataset with comparison existing approaches.

关键词： quality measure deep learning no-reference (NR) image quality quality assessment

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