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Comprehensive Analysis and Benchmarking of De-raining Techniques Through a Rain Model Approach

Comprehensive Analysis and Benchmarking of De-raining Techni...

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conference on multimodal image exploitation and learning

作者： Badrieva, Shokhina Bernabel, Silvano Agaian, Sos GC CUNY Coll Staten Isl CSI Dept Comp Sci New York NY 10314 USA

ISBN: (纸本)9781510650770;9781510650763

Adverse weather conditions, such as rain, impact the visual quality of images and significantly impact the performance of vision systems for drone-based video surveillance and self-driving car applications. It is essential to develop algorithms that can automatically remove these artifacts and not degrade the rest of the image. Several methods have been proposed in literature and practice to address this problem. They mainly focus on specific rain models, such as droplets, streaks, mist, or a combination of these. Real-life rain images are largely randomized with diverse rain sizes, types, densities, and directions. Furthermore, rain impacts various image parts differently and is often randomly distributed. Most existing de-raining algorithms can't remove drops, streaks, and mist from images simultaneously. This paper addresses this issue by reviewing existing algorithms and datasets through a rain model lens. We present surveys and quantitative benchmarking of state-of-the-art intelligence algorithms based on the rain types they aim to remove. While other review papers exist on single image de-raining, our work looks at and outlines the different algorithms and datasets available for each specific rain model. Finally, the paper makes the following contributions: Select the most recent state of the art algorithms and show their performance for each rain type on our combination dataset called the Combination Rain Model Dataset Offers insights on the issues that still exist in the developing field of image de-raining and future steps in the field

关键词： De-rain rain removal deep learning algorithms digital image processing de-raining image applications rain model rain streak raindrop rain mist

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Optimal Vision Aided Navigation in GPS-Denied Regions

Optimal Vision Aided Navigation in GPS-Denied Regions

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conference on multimodal image exploitation and learning

作者： DelMarco, Stephen Bortolami, Simone Tom, Victor BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (纸本)9781510650770;9781510650763

The US Department of Defense has a need to successfully navigate in operational regions where GPS is degraded or denied. When GPS is denied, navigation of aerial platforms, including manned and unmanned aerial vehicles (UAVs), for Intelligence, Surveillance, and Reconnaissance (ISR) missions, targeting missions, or autonomous cargo delivery missions, becomes compromised. In the absence of GPS, navigating from pure inertial solutions leads to rapidly growing position errors due to drift in the inertial measurement unit. Vision Aided Navigation (VAN) approaches can aid the inertial solution to reduce navigation error, but require salient and distinct scene content for image alignment. In this paper, we present an approach to optimal path planning for VAN over operational ground regions that minimizes navigation position error. The approach uses automated pre-mission visual fiducial discovery to identify regions in imagery of the fly-over area that contain unique, salient, discriminative, and stable feature content. The discovered visual fiducial regions are used to form a map of probabilities of successful VAN at each point of the gridded fly-over region. An optimal path planning algorithm uses the probability map to determine the path over the fly-over region that maximizes navigability and minimizes VAN positioning error. Constraints, such as no-fly zones and path length constraints, are incorporated into the formulation to generate a constrained optimization problem. We present the mathematical formulation of the constrained path planning optimization problem and generate numerical results demonstrating performance.

关键词： navigation vision path planning image registration

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Towards Optimal Cropping: Breast and Liver Tumor Classification using Ultrasound images

Towards Optimal Cropping: Breast and Liver Tumor Classificat...

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conference on multimodal image exploitation and learning

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

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

In tumor diagnostics from Ultrasound scan images, the region of interest is often determined by marking the boundary of the suspect mass by experts, simply by clicking on sufficient number of tumor boundary points. To determine whether the tumor is malignant or benign, clinical experts who are trained for long time on how to interpret image information from the marked tumor region and from the surrounding area. In contrast, in designing automatic computer aided diagnosis system using both traditional and conventional machine learning, the relevant image features are generally obtained by cropping the tumor as region of interest (RoI) without considering the periphery of the tumor that might contain important discriminative information for better classification accuracy. In this work, we investigate the impact on classification accuracy of different types of tumors by the cropping strategy where the tumor area will be augmented by a proportion of the surrounding region of the ROI. The required optimal proportion need to be determined so that the cropped ROIs encapsulate information about posterior echo and shadow of the tumor in addition to internal texture and echo that has mainly been used as classification indicators. Recently proposed cropping techniques use the best fitting ellipse of the tumor and examine the proportion by which the ellipse is expanded to improve accuracy. Unfortunately, the fitting ellipse may not reflect the shape of the tumor. Here, we investigate a number of alternative approaches of cropping the ROIs using the concept of convex hull shape(s) determined from the tumor boundary points selected by radiologists. Initially, we check several expansion ratio scales of the convex hull ranging from 0.6 to 4.0 against the cropped tumor without margin. Several classification methods including handcrafted features and deep learning methods are adopted for breast and liver tumors using ultrasound images. We shall demonstrate the importance of optimal c

关键词： Optimal Cropping Margin Augmentation Ultrasound Tumor scans Convex Hull Deep learning

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Towards phenological development quantification in Brassica plants via multispectral imaging and deep learning

Towards phenological development quantification in Brassica ...

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conference on multimodal image exploitation and learning

作者： Stone, Abigail A. Rajeev, Srijith Rao, Shishir P. Panetta, Karen A. Agaian, Sos S. Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA CUNY Dept Comp Sci New York NY 10016 USA

ISBN: (纸本)9781510673854;9781510673847

As global populations soar and the climate warms, food supply management is an increasingly critical problem. Precision agriculture, driven by on-site data collected from various sensors, plays a pivotal role in optimizing irrigation, fertilization, and enhancing plant health and crop yield. However, the manual process of in-filed chlorophyll measurement, which is a key metric for guiding agricultural decisions, is very cumbersome and poses significant challenges. This paper explores the transformative potential of multispectral imaging data to automate plant measuring and monitoring tasks, thereby reducing labor and time costs while improving the quality of data available for making informed agricultural decisions. We present a deep-learning model for instance segmentation of plants, trained on the Growliflower dataset of RGB and multispectral image cubes of cauliflower plants. The proposed algorithm uses a Convolutional Neural Network (CNN) to leverage both the spectral information and and its spatial context to locate individual plants. We introduce a novel band-selection algorithm for determining the most significant multispectral features for use in the convolutional network: this reduces model complexity while ensuring accurate results. Our model's ability to generalize across varying growth stages, soil conditions, and varieties of crops in the training dataset demonstrates its suitability for real-world agricultural applications. This fusion of cutting-edge sensing technology for robotic systems and state-of-the-art deep learning models holds significant promise for advancements in crop yield, resource efficiency, and sustainability practices.

关键词： precision agriculture agricultural robotics deep learning multispectral imaging

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Enhancing Robustness of Weather Removal: Preprocessing-Based Defense Against Adversarial Attacks

Enhancing Robustness of Weather Removal: Preprocessing-Based...

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conference on multimodal image exploitation and learning

作者： Frants, Vladimir Agaian, Sos City Univ New York Grad Ctr 365 Fifth Ave New York NY 10016 USA City Univ New York Coll Staten Isl 2800 Victory Blvd Staten Isl NY 10314 USA

ISBN: (纸本)9781510673854;9781510673847

Recent progress in deraining and dehazing methods has dramatically enhanced image quality in bad weather. However, these methods are vulnerable to adversarial attacks, severely compromising their effectiveness. Traditional defenses like adversarial training and model distillation necessitate significant retraining, hindering their real-world application due to high computational costs. To address these limitations, we propose the Quaternion-Hadamard Transformer Network (QHTN), a novel defense strategy against white-box adversarial attacks, including the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). The QHTN leverages a transformer architecture with three key modules: preprocessing, local-global feature extraction, and reconstruction. The local-global feature extraction module utilizes innovative Hadamard and quaternion convolution blocks to analyze spatial and inter-channel relationships. This unique approach enables the QHTN to incorporate a denoising mechanism during preprocessing, effectively mitigating adversarial noise before it influences the model's input. Extensive evaluations demonstrate the QHTN's efficacy in safeguarding haze and rain removal models from adversarial attacks. These results validate the QHTN's efficiency and potential for broader adoption in image-processing defense mechanisms.

关键词： Hadamard Transform Quaternion Neural Network Computer Vision image Processing

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Determination of tool cutting surface quality from analysis on a group of images and the combined data

Determination of tool cutting surface quality from analysis ...

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conference on multimodal image exploitation and learning

作者： Semenishchev, Evgenii Voronin, Viacheslav Zelensky, Aleksandr Alepko, Andrey Zhdanova, Marina Agaian, Sos Moscow State Tech Univ STANKIN Moscow Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

The article presents a description of a system that allows you to analyze the surface of a tool used for processing metal products. The information parameters are data on the current position of the object and the angles of its rotation, as well as a set of images obtained in the visible and/or infrared range. The captured images are subject to interference caused by: small distances between the camera and the investigated instrument;sensor noise;blurring the boundaries (defocusing) of objects at the edges of images;uneven lighting;the complexity of the shape of the analyzed instrument. The introduction of the possibility of analyzing a series of images obtained in different ranges will improve the accuracy of the formation of the structure of the means (instrument). The article describes methods and algorithms for improving the quality of a series of images. A description of the choice of their parameters is given, and an analysis of their influence on the result is presented. Methods have been developed for analyzing objects and identifying their boundaries by analyzing a series of images obtained in different ranges (IR and visible). The use of combined ranges makes it possible to increase the accuracy of determining the boundary in conditions of interference and low illumination and also allows the analysis of objects of complex shapes (the presence of holes, internal structures, chips). A series of test images, fixing the edge of the cutter, the shape of the drill, and cutting inserts, show the effectiveness of the developed approach and the possibility of automating the analysis process.

关键词： IR images local features denoising combining image instrument tool cutting surface

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Sat U-Net: A Fusion Based Method for Forensic Splicing Localization in Satellite images

Sat U-Net: A Fusion Based Method for Forensic Splicing Local...

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conference on multimodal image exploitation and learning

作者： Horvath, Janos Xiang, Ziyue Cannas, Edoardo Daniele Bestagini, Paolo Tubaro, Stefano Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab Viper W Lafayette IN 47907 USA Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo da Vinci 32 Milan Italy

ISBN: (纸本)9781510650770;9781510650763

Satellite images are widely available to the public. These satellite images are used in various fields including natural disaster analysis, meteorology and agriculture. As with any type of images, satellite images can be altered using image manipulation tools. A common manipulation is splicing, i.e., pasting on top of an image a region coming from a different source image. Most manipulation detection methods designed for images captured by "consumer cameras" tend to fail when used with satellite images. In this paper we propose a machine learning approach, Sat U-Net, to fuse the results of two exiting forensic splicing localization methods to increase their overall accuracy and robustness. Sat U-Net is a U-Net based architecture exploiting several Transformers to enhance the performance. Sat U-Net fuses the outputs of two unsupervised splicing detection methods, Gated PixelCNN Ensemble and Vision Transformer, to produce a heatmap highlighting the manipulated image region. We show that our fusion approach trained on images from one satellite can be lightly retrained on few images from another satellite to detect spliced regions. We compare our approach to well-known splicing detection methods (i.e., Noiseprint) and segmentation techniques (i.e., U-Net and Nested Attention U-Net). We conducted our experiments on two large datasets: one dataset contains images from Sentinel 2 satellites and the other one contains images from Worldview 3 satellite. Our experiments show that our proposed fusion method performs well when compared to other techniques in localizing spliced areas using Jaccard Index and Dice Score as metrics on both datasets.

关键词： Forensic Satellite images Fusion Deep learning

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Chaos-Based Encryption System of DICOM Medical images

Chaos-Based Encryption System of DICOM Medical Images

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conference on multimodal image exploitation and learning

作者： Al Nassan, Wafaa Bonny, Talal Al-Shabi, Mohammad Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates Univ Sharjah Coll Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510673854;9781510673847

The proliferation of digital medical imaging has ushered in unprecedented advancements in healthcare diagnostics and treatment planning. However, this digital era has also raised significant concerns regarding the security and privacy of sensitive medical image data, particularly in the context of the Digital Imaging and Communications in Medicine (DICOM) standard. In response to these concerns, this paper presents a novel Chaos-Driven Encryption (CDE) system designed to fortify the confidentiality and integrity of DICOM medical images. The CDE system harnesses chaotic systems' inherent unpredictability and complexity to create a robust encryption framework. Chaos-based encryption offers a formidable defence against conventional cryptographic attacks due to its nonlinearity and sensitivity to initial conditions. We propose a specific chaotic map and key management scheme tailored for DICOM images, ensuring that the encryption process remains secure and efficient. This paper comprehensively analyzes the CDE system, including its encryption process, key generation, and decryption procedure. We assess the security of CDE through extensive cryptographic analysis, demonstrating its resistance to known attacks and vulnerabilities. Moreover, we evaluate the computational performance of CDE in terms of encryption/decryption speed and resource utilization. Our experimental results highlight the feasibility and effectiveness of Chaos-Driven Encryption in safeguarding DICOM medical images against unauthorized access and tampering. By presenting this innovative encryption system, we contribute to the ongoing dialogue on healthcare data security and privacy, offering a promising solution for the protection of sensitive medical image information in the digital age.

关键词： DICOM image image encryption chaotic system security

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Discovering Connotations as Labels for Weakly Supervised image-Sentence Data 18

Discovering Connotations as Labels for Weakly Supervised Ima...

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27th World Wide Web (WWW) conference

作者： Mogadala, Aditya Kanuparthi, Bhargav Rettinger, Achim Sure-Vetter, York Karlsruhe Inst Technol Karlsruhe Germany BITS Hyderabad India AIFB Karlsruhe Germany

ISBN: (纸本)9781450356404

Growth of multimodal content on the web and social media has generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic "intension''. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic "intension". We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. It's unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval.

关键词： image-Sentence Connotation Labels Weakly Supervised Deep learning Multi-label Prediction

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QSRNet - Towards Quaternion-based Single image Super-Resolution

QSRNet - Towards Quaternion-based Single Image Super-Resolut...

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conference on multimodal image exploitation and learning

作者： Kamath, Shreyas K. M. Rao, Shishir Paramathma Panetta, Karen Agaian, Sos S. Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA CUNY Grad Ctr Dept Comp Sci New York NY 10017 USA

ISBN: (纸本)9781510650770;9781510650763

Single-image super-resolution (SISR), which maps a low-resolution observation to a high-resolution image, has been extensively utilized in various computer vision applications. With the advent of convolutional neural networks (CNNs), numerous algorithms have emerged that achieve state-of-the-art results. However, the main drawback of CNN is the negligence in the interrelationship between the RGB color channel. This negligence further reduces crucial structural information of color and provides a non-optimal representation of color images. Furthermore, most of these CNN-based methods contain millions of parameters and layers, limiting the practical applications. To overcome these drawbacks, an end-to-end trainable single image super-resolution method - Quaternion-based image Super-Resolution network (QSRNet) that takes advantage of the quaternion theory is proposed in this paper. QSRNet aims at maintaining the local and global interrelationship between the channels and produces high-resolution images with approximately 4x fewer parameters when compared to standard CNNs. Extensive computer experimentations were conducted on publicly available benchmarking thermal datasets, including DIV2K, Flickr2K, Set5, Set14, BSD100, Urban100, and UEC100, to demonstrate the effectiveness of the proposed QSRNet compared to traditional CNNs.

关键词： super-resolution quaternion quaternion neural networks quaternion convolutional neural networks hypercomplex neural networks soft-edge

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