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BiThermalNet: a lightweight network with BNN RPN for thermal object detection

BiThermalNet: a lightweight network with BNN RPN for thermal...

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conference on multimodal image exploitation and learning

作者： Yuan, Chunyu Agaian, Sos S. CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA CUNY Dept Comp Sci Coll Staten Isl New York NY 10021 USA

ISBN: (纸本)9781510650770;9781510650763

As traditional RGB cameras cannot perform well under weak light in the darkness and poor weather conditions, thermal cameras have become an essential component of edge systems. This paper proposes a lightweight, faster binarized R-CNN network (a state-of-the-art instance segmentation model), called BiThermalNet, for thermal object detection with high detection capabilities and lower memory usage. It designs a new Region Proposal Network(RPN) structure with a binary neural network (BNN) to lower model size by 16%, having higher accuracy performance. BiThermalNet adds novel-designed residual gates to maximum information entropy and offers channel-wise weight and bias to reduce errors from binarization. The extensive experiments on different thermal datasets (such as Dogs&People Thermal Dataset, UNIRI-TID) confirm that BiThermalNet can outperform traditional faster R-CNN by sizable margins with smaller models size. Moreover, a comparative analysis of the proposed methods on thermal images will also be presented.

关键词： Object Detection Binary Neural Network Thermal Object Detection Deep learning Computer Vision

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Transmission Map Optimization for Single image Dehazing

Transmission Map Optimization for Single Image Dehazing

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conference on multimodal image exploitation and learning

作者： Trongtirakul, Thaweesak Agaian, Sos Rajamangala Univ Technol Phra Nakhon Fac Ind Educ 399 Samsen Rd Bangkok 10300 Thailand CUNY Coll Staten Isl Dept Comp Sci 2800 Victory Blvd Staten Isl NY 10314 USA

ISBN: (纸本)9781510650770;9781510650763

In hazy weather, images and videos captured outdoor scenes often suffer from inadequate visibility, low contrast, and color shift due to the atmospheric light scattering from the atmosphere particles. In general, the haze is not uniformly distributed. It is a high challenge in computer vision-based applications to visualize matters behind hazy scenes like haze-free images. This paper aims to: i) develop a new optimal-based transmission map for removing haze or fog from a single image and a video;and ii) demonstrate the utility and effectiveness of the developed technique. The proposed method offers a single image de-hazing algorithm based on transmission map optimization and novel enhancement techniques. Intensive computer simulation results of natural Live-Haze dataset and synthetic image datasets such as O-HAZY, dataset show that: 1. The presented approach effectively removes haze and prevents color distortion from undesirable de-hazing. 2. The resulting dehazed images illustrate realistic colors and remarkable details. 3. The proposed method achieves to restore the visibility of hazy scenes and illustrates colorful and natural appearances.

关键词： image Dehazing image Enhancement Scene Restoration Visibility Restoration

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An Automatic multimodal Data Registration Strategy for 2D/3D Information Fusion

An Automatic Multimodal Data Registration Strategy for 2D/3D...

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conference on multimodal image exploitation and learning

作者： Schierl, Jonathan Asari, Vijayan Singer, Nina Aspiras, Theus Stokes, Andrew Keaffaber, Brett Van Rynbach, Andre Decker, Kevin Rabb, David Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA Air Force Res Lab Dayton OH USA Def Engn Corp Dayton OH USA

ISBN: (纸本)9781510650770;9781510650763

With the emergence of advanced 2D and 3D sensors such as high-resolution visible cameras and less expensive lidar sensors, there is a need for a fusion of information extracted from senor modalities for accurate object detection, recognition, and tracking. To train a system with data captured by multiple sensors the regions of interest in the data must be accurately aligned. A necessary step in this process is a fine, pixel-level registration between multiple modalities. We propose a robust multimodal data registration strategy for automatically registering the visible and lidar data captured by sensors embedded in aerial vehicles. The coarse registration of the data is performed by utilizing the metadata, such as timestamps, GPS, and IMU information, provided by the data acquisition systems. The challenge is these modalities contain very different sets of information and are not able to be aligned using classical methods. Our proposed fine registration mechanism employs deep-learning methodologies for feature extraction of data in each modality. For our experiments, we use a 3D geopositioned aerial lidar dataset along with the visible data (coarsely registered) and extracted SIFT-like features from both of the data streams. These SIFT features are generated by appropriately trained deep-learning algorithms.

关键词： Registration Data Fusion Feature Extraction Remote Sensing SIFT Deep learning Computer Vision GIS Multi-modal lidar

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Multi-Template Multi-Channel image Matching Using Tensor Products of Low-Dimensional Clifford Algebras

Multi-Template Multi-Channel Image Matching Using Tensor Pro...

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conference on multimodal image exploitation and learning

作者： DelMarco, Stephen BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

The rapid expansion of data generated from various sensors, devices, and applications offers the opportunity to exploit complex data relationships in new ways. Multi-dimensional data often appears as multiple, separate data channels, for which multi-channel data representations and analysis techniques have been developed. Alternately, for image matching, multiple-template image matching techniques have been developed. Multi-template approaches use multiple, often single-channel, templates, exhibiting intra-class variations. Same-class test image exemplars must match all reference templates. In this paper, we combine multiple-template matching techniques with multi-channel data representations to provide multi-template, multi-channel image matching. We represent image data with pixels taking values in tensor products of low-dimensional Clifford Algebras, for which Fourier transforms exist. Fourier domain matching provides a computational processing improvement over spatial correlation-based matchers. The tensor product approach provides a decomposition of higher-dimensional algebras into combinations of lower dimensional algebras for which Fourier transforms apply. The tensor product approach produces a performance advantage, on data with the appropriate inter-channel correlation characteristics, through exploitation of additional data channel correlations. When these add constructively, a matching performance benefit occurs. We define an anti-involution mapping on a tensor product space, which leads to a definition of image correlation over these spaces. We prove that the correlation satisfies an extended inner product definition. We prove a Cauchy-Schwartz inequality, which validates use of the image correlation as a matcher. We present an example, using synthetic image data, where the approach provides superior matching performance over classical score sum fusion.

关键词： image correlation Clifford algebra multi-channel quaternion

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Improving Cybersecurity through Deep learning on Keystroke and Mouse Dynamics

Improving Cybersecurity through Deep Learning on Keystroke a...

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conference on multimodal image exploitation and learning

作者： Zheng, Yufeng Yu, Lixin Haque, Sardar Zhang, Ping Elmaghraby, Adel S. Univ Mississippi Med Ctr Jakson MS 39216 USA Alcorn State Univ Lorman MS USA Univ Louisville Louisville KY 40292 USA

ISBN: (纸本)9781510650770;9781510650763

Username, password, and biometrics are three-factor authentication for cybersecurity enhancement. Adding keystroke and mouse biometrics to authentication will definitely improves the cybersecurity. Keystroke dynamics refers to the process of measuring and assessing human's typing rhythm on digital devices. Keystroke timing information such as digraph, dwell time and flight time are used in our experimental datasets. Mouse dynamics records mouse motion (speed), left-, right-, or double-clicking timing information. Our own dataset includes both types of dynamics from same group of subjects. We develop recurrent neural network (RNN) models and support vector machine (SVM) models to represent user's biometrics. Keystroke and mouse dynamics can be used as features fed to the models separately for user verification or identification. Feature fusion is applied to improve the accuracy. Our results show the RNN method is better than traditional methods like SVM, and fusion can further improve the performance.

关键词： Cybersecurity enhancement Keystroke biometrics Mouse biometrics Recurrent neural network (RNN) Deep learning Information fusion

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Semi-Supervised learning for Improved Post-disaster Damage Assessment from Satellite imagery

Semi-Supervised Learning for Improved Post-disaster Damage A...

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conference on multimodal image exploitation and learning

作者： Oludare, Victor Kezebou, Landry Panetta, Karen Agaian, Sos Tufts Univ Medford MA 02155 USA CUNY New York NY 10021 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

The devastating aftermath of a natural disaster is often challenging to assess, and inaccuracies are bound to occur when an assessment is done manually due to the inevitable human-in-the-loop errors. Timely and accurate evaluation of the extent of damages is often needed to effectively deploy resources to hard-hit areas, save lives, and facilitate adequate planning towards disaster recovery. The commonly used supervised learning approaches have made a considerable improvement in assessing natural disasters. However, quickly implementing supervised classification is still challenging due to the complexity of acquiring many labeled samples in the aftermath of disasters. In this paper, we propose a: i) two-stream high-resolution network (HRNet) that takes a pair of pre-and post-disaster images and ii) semi-supervised framework for improving the generalizability of current methods to other housing styles. The proposed method comprises of two parts: a multi-class deep learning model, and a pseudo-label generator and refinement module. By harnessing information from a large amount of unlabeled data and aerial imagery, our approach can outperform its base model. Experimental results on the xView2 dataset demonstrate that the proposed framework improves the performance of our two-stream model for unseen satellite images depicting a scene before and after a disaster.

关键词： disaster analysis semi-supervised learning satellite imagery damage assessment remote sensing

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COVID-19 Detection in CT images using Customized Weighted Filter-based CNN

COVID-19 Detection in CT Images using Customized Weighted Fi...

引用

conference on multimodal image exploitation and learning

作者： Sanghavi, Foram Panetta, Karen Agaian, Sos Tufts Univ Dept Elect Engn Medford MA 02155 USA CUNY Grad Ctr Dept Comp Sci New York NY 10010 USA

ISBN: (纸本)9781510643062

The Coronavirus (Covid-19) pandemic has been affecting the health of people around the globe. With the number of confirmed cases and deaths still rising daily, it is now crucial to quickly detect the positive cases and provide them with the necessary treatment. Presently, several research investigations are being conducted to help control the spread of this epidemic. One research topic is to create faster and more accurate detection. Recent studies have demonstrated that chest CT images encompass the distinctive COVID-19 features, which can be utilized for achieving an efficient COVID-19 diagnosis. However, manually reading these images on a large scale can be laborious and is intractable. Thus, using an artificial intelligence- based system that can help capture the precise information and give an accurate diagnosis would be beneficial. In this paper, a customized weighted filter-based CNN ( CCNN) is proposed. Computer simulations show that the proposed CCNN system (1) increases the effectiveness of detect COVID- 19 CT scans from the non-COVID-19 CT scans and ( 2) has faster training time compared to the traditional deep learning models.

关键词： COVID-19 detection CT images CNN deep learning SARS-CoV-2 CT scan dataset

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Ultrasound image Augmentation by Tumor Margin Appending for Robust Deep learning based Breast Lesion Classification

Ultrasound Image Augmentation by Tumor Margin Appending for ...

引用

conference on multimodal image exploitation and learning

作者： Hassan, Tahir AlZoubi, Alaa Du, Hongbo Jassim, Sabah Univ Buckingham Sch Comp Buckingham MK18 1EG England

ISBN: (纸本)9781510650770;9781510650763

The difficulty of obtaining sufficient number of appropriately labelled samples is a major obstacle to learning class discriminating features by Machine learning (ML) algorithms for tumor diagnostics from Ultrasound (US) images. This is often mitigated by sample augmentation, whereby new samples are generated from existing samples by rotation and flipping operations, Singular Value Decomposition (SVD) or generating synthetic image by Generative Adversarial Networks (GANs). The first approach does not generate new genuine samples, SVD generates images may not be easy to recognize as US tumor scans, and while GANs generate images are visually convincing their use for diagnostics may lead to overfitting and subject to adversarial attacks. We propose an innovative sample augmentation approach that utilizes our recently developed Tumor Margin Appending (TMA) scheme. The TMA scheme constructs the Convex Hull (CH) of the tumor region using a small set of radiologist marked tumor boundary points and crops the image at different radial expansion ratios of the CH onto surrounding tissue. Various ML algorithms, handcrafted features and Convolutional Neural Network (CNN), trained with TMA images at different ratios achieved acceptable diagnostic accuracies. In this paper, our sample augmentation scheme expands the ML training datasets by including TMA samples at several expansion ratios. Results of experiments on training CNN tumor diagnostic schemes for breast tumors yield improved classification performance with additional benefits, including robustness against different inadvertently practiced cropping at different hospitals, serves as a regularizer to reduce model overfitting when tested on unseen datasets obtained using unknown tumor segmentation and cropping procedure.

关键词： Ultrasound Augmentation Margin Appending Optimal Cropping Convex Hull Breast Tumors Deep learning

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COVID-19 Face Mask Detection in a Crowd Using Multi-model Based on YOLOv3 and Hand-crafted Features

COVID-19 Face Mask Detection in a Crowd Using Multi-model Ba...

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conference on multimodal image exploitation and learning

作者： Liu, Shao Agaian, Sos S. CUNY Grad Ctr Dept Comp Sci New York NY 10021 USA CUNY Coll Staten Isl Dept Comp Sci New York NY USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

Face recognition is one of the most challenging biometric modalities when deployed in unconstrained environments due to the high variability that faces images present in the real world in a crowd, which are affected by complex factors including head poses, aging, illumination conditions, occlusions, and facial expressions. The face recognition system aims to identify and track visual data subjects, such as images and videos. More people are currently carrying masks in public, bringing new challenges to the face detection and identification system. This article focuses on the detection and recognition of masked faces. The presented framework is based on new artificial intelligence tools that use hand-crafted and deep learning (YOLOv3 and CNNs) features and SVM classifiers. Computer simulation on five different face mask datasets (Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), Medical Mask Dataset(MMD), Labeled Faces in the Wild (LFW)) and our proposed Artificially simulated masked face dataset (ASMFD), of which the testing results illustrate that the proposed method is comparable or better in most cases than traditional face mask recognition techniques. The presented system may produce anonymous statistical data that can help the agencies predict potential epidemics of COVID-19.

关键词： COVID-19 Masked Face Deep learning Machine learning Masked Face Detection

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Fully Convolutional Network for Breast Lesion Segmentation in Ultrasound image: Towards False Positive Reduction

Fully Convolutional Network for Breast Lesion Segmentation i...

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conference on multimodal image exploitation and learning

作者： Zhang, Sicong Alzoubi, Alaa Du, Hongbo Univ Buckingham Sch Comp Buckingham MK18 1EG England

ISBN: (纸本)9781510650770;9781510650763

Ultrasound scan (US) imagery is an important tool for radiologists to make a fast and reliable diagnosis decision about breast lesion status (benign or malignant). Accurate and automatic segmentation of breast lesion is critical for annotating the lesion characteristics such as margin smoothness and regularity in support of the diagnosis decision. Fully convolutional network (FCN) is one of the commonly used deep learning neural network methods for semantic segmentation. This paper is concerned with effective adaptation of the FCN solutions for segmenting breast lesions from 2D ultrasound images. The paper aims to first evaluate the existing FCN solution for solving the problem at hand and compare its performance with another popular method using U-Net. The paper then highlights one key issue with the FCN, i.e. false positive pixels near the boundary of a lesion and false positive pixels forming false lesions. The paper then investigates several methods in reducing such false positive pixels, including the use of data augmentation in training the classification model and use of loss functions in training the models. Experimental results using several data sets collected from various sources show that our adapted FCN method outperforms U-Net-based solutions in general and the false positive reduction methods we attempted have reduced the false positive pixels in both regions close to lesion boundary and separate from true lesion regions.

关键词： Breast Lesion Ultrasound image Semantic Segmentation FCN False Positive Reduction Data Augmentation Loss Function

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