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multimodal image exploitation and learning 2023

ISBN: (纸本)9781510661660

The proceedings contain 24 papers. The topics discussed include: the space dual of Einstein’s relativity advances the theoretical backing for this year's astronomical observation of dark energy residing in the universe's black holes;a machine learning approach to image registration verification;new retinex model-based infrared image enhancement;application of computational topology to vision aided navigation in GPS-denied regions;off-angle iris recognition using single and multiple deep learning frameworks;pixel-level transformations in domain adaptive object detection for thermal imagery;real-time pedestrian detection using radar-camera fusion and clustering;and diagnosis of keratoconus using novel elevation mapping technique from smartphone image.

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Multi-Modality Sensing and Data Fusion for Multi-Vehicle Detection

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 2280-2295页

作者： Roy, Debashri Li, Yuanyuan Jian, Tong Tian, Peng Chowdhury, Kaushik Ioannidis, Stratis Northeastern Univ Dept Elect Comp Engn Boston MA 02115 USA

With the recent surge in autonomous driving vehicles, the need for accurate vehicle detection and tracking is critical now more than ever. Detecting vehicles from visual sensors fails in non-line-of-sight (NLOS) settings. This can be compensated by the inclusion of other modalities in a multi-domain sensing environment. We propose several deep learning based frameworks for fusing different modalities (image, radar, acoustic, seismic) through the exploitation of complementary latent embeddings, incorporating multiple state-of-the-art fusion strategies. Our proposed fusion frameworks considerably outperform unimodal detection. Moreover, fusion between image and non-image modalities improves vehicle tracking and detection under NLOS conditions. We validate our models on the real-world multimodal ESCAPE dataset, showing 33.16% improvement in vehicle detection by fusion (over visual inference alone) over test scenarios with 30-42% NLOS conditions. To demonstrate how well our framework generalizes, we also validate our models on the multimodal NuScene dataset, showing similar to 22% improvement over competing methods.

关键词： Vehicle detection tracking multimodal data fusion latent embeddings image seismic acoustic radar

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A Machine learning Approach to image Registration Verification

A Machine Learning Approach to Image Registration Verificati...

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multimodal image exploitation and learning 2023

作者： DelMarco, Stephen Webb, Helen Stolle, Frank Bortolami, Simone Tucker, Frank Hillyer, Travis BAE Systems 600 District Avenue BurlingtonMA01803 United States BAE Systems 600 District Avenue BurlingtonMA01803 United States CCDC Soldier Center STTC 12423 Research Pkwy OrlandoFL32826 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downstream exploitation algorithms depends on reliable image registration. image registration failure can corrupt processing and performance of downstream algorithms by mis-associating image features. For example, feature mis-association leads to generation of incorrect target geo-coordinates in aerial surveillance applications, or erroneous vision-based measurements in vision aided navigation. Accurate and dependable registration failure detection mitigates deleterious effects of erroneous registration. However, in autonomous operation modes, with no human in-the-loop, and in the absence of registration solution groundtruth knowledge, verifying registration solutions is problematic. In this paper we present a machine learning based image registration verification system that operates autonomously, without ground-truth. We train a machine learning algorithm to identify correct registration solutions, even for difficult multi-modal image registration in which sensor phenomenology differences produce different feature manifestation. The verification approach includes techniques for mitigation of falsealarms that may arise due to feature ambiguity. We present examples of feature ambiguity for correlation-related registration techniques. We describe Radon transform processing, covariance estimation, and fusion techniques for feature ambiguity detection. We present numerical verification performance results from a small pilot study designed to investigate the feasibility of using machine learning for reliable registration verification. © 2023 SPIE.

关键词： image registration

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Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical image Segmentation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2025年第5期36卷 8372-8386页

作者： Wu, Yonghuang Wu, Guoqing Lin, Jixian Wang, Yuanyuan Yu, Jinhua Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Fudan Univ Inst Funct & Mol Med Imaging Shanghai 200438 Peoples R China Cent Hosp Minhang Dist Stroke Ctr Shanghai 201101 Peoples R China

Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images based on deep learning has made great progress, fully supervised models require a great amount of annotations, making such complex medical image segmentation a difficult problem. In this article, we propose a semi-supervised model for complex medical image segmentation, which innovatively proposes a bidirectional self-training paradigm, through dynamically exchanging the roles of teacher and student by estimating the reliability at the model level. The direction of information and knowledge transfer between the two networks can be controlled, and the probability distribution of the roles of teacher and student in the next stage will be jointly determined by the model's uncertainty and instability in the training process. We also resolve the problem that loosely coupled networks are prone to collapse when training on small-scale annotated data by proposing asymmetric supervision (AS) strategy and hierarchical dual student (HDS) structure. In particular, a bidirectional distillation loss combined with the role exchange (RE) strategy and a global-local-aware consistency loss are introduced to obtain stable mutual promotion and achieve matching of global and local features, respectively. We conduct detailed experiments on two public datasets and one private dataset and lead existing semi-supervised methods by a large margin, while achieving fully supervised performance at a labeling cost of 5%.

关键词： Training image segmentation Task analysis Medical diagnostic imaging Reliability Predictive models Perturbation methods Complex medical image segmentation self-training semi-supervised

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Improved Hybrid Real-Binary PSO Using Modified learning and Restarting Methods for Planar Microwave Absorber Designs

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IEEE ACCESS 2023年 11卷 15505-15513页

作者： Jia, Xingning Qin, Guoche Li, Chunshu Ningxia Univ Sch Phys & Elect Elect Engn Yinchuan 750021 Peoples R China

This paper proposes an improved hybrid real-binary particle swarm optimization (IHPSO) using modified learning and restarting methods to address electromagnetic optimization problems involving both continuous and discrete decision variables. The modified learning approach aims to improve the exploration and exploitation capabilities of IHPSO. A restarting mechanism is employed to randomly reposition the swarm of particles to prevent them from being stuck in the local optimum, while still directing the search process with the global best. To demonstrate the performance of the IHPSO, 23 benchmark functions are tested, and the results are compared with those of other traditional algorithms. In both unimodal and multimodal functions, IHPSO outperforms the original hybrid real-binary PSO. Finally, the proposed algorithm is used to design planar microwave absorbers, a classic hybrid real-binary electromagnetic optimization problem.

关键词： Optimization Convergence image quality Particle swarm optimization Electromagnetics Benchmark testing Microwave theory and techniques Microwave technology Hybrid optimization particle swarm optimization microwave absorber design

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Diagnosis of Keratoconus using Novel Elevation Mapping Technique from Smartphone image

Diagnosis of Keratoconus using Novel Elevation Mapping Techn...

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multimodal image exploitation and learning 2023

作者： Foysal, Kamrul H. Ankur, Tanjemoon Dept. of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

Keratoconus is a progressive eye disease prevalent worldwide. Keratoconus is caused by the change in curvature of the eyeball. An unevenly shaped lens causes blurry and inaccurate vision in the patient, which eventually may cause blindness. The existing keratoconus detection techniques use a less accurate keratoscope and bulky, expensive topography imaging (OCT) device, which causes inconvenience in diagnosing this disease in a remote and scarce setup. In this paper, I propose a novel smartphone-based keratoconus diagnosis technique that uses a smartphone camera to acquire a 2D image of the cornea with Placido disc reflection, and process different image processing techniques including entropy-based edge detection, multiple circles detections, finally calculating 3D topography of the eye in an app. The existing inconvenient methods of keratoconus detection can be replaced by our proposed method, which is accurate, quick, reliable, and simple for usage by clinicians and patients in remote settings. © 2023 SPIE.

关键词： Smartphones

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image Haze Removal Using Block-Based Multi-Scale Enhancement Method

Image Haze Removal Using Block-Based Multi-Scale Enhancement...

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multimodal image exploitation and learning 2023

作者： Voronin, V. Tokareva, O. Gapon, N. Naumov, I. Zelensky, A. Semenishchev, E. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

An image haze removal algorithm based on multi-scale block-rooting processing has been developed for removing haze/fog from an image based on frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is proposed in obtaining a local-global estimate of high-contrast images, which are also used in the general fusion model. The proposed block-based multi-scale enhancement method base on 3-D blockrooting multi-scale transform domain technique, comprising: finding similar blocks in the image by block-matching;block-grouping for different block sizes;applying 3-D block-matching parametric image enhancement;calculating the quality measure of enhancement;optimizing parameters of image enhancement method through the quality measure of enhancement;fusing different enhanced images. To test the performance of the proposed algorithm, the public database O-HAZE is used. © 2023 SPIE.

关键词： image enhancement

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New Retinex Model-Based Infrared image Enhancement

New Retinex Model-Based Infrared Image Enhancement

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multimodal image exploitation and learning 2023

作者： Trongtirakul, Thaweesak Agaian, Sos Dept. of Electrical Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon 399 Samsen Rd. Dusit Bangkok101300 Thailand Graduate Center City University of New York 365 Fifth Ave. New YorkNY10016 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

The thermal imaging system often suffers from low contrast, low-spatial resolution, and blur under heat radiation conditions. Currently, available image enhancement methods are most suitable for visible images. However, existing methods often enhance objects and noise for noisy infrared images, simultaneously producing a very poor result. This paper presents two single infrared image enhancement methods, including (i) a Bi-Logarithmic Histogram Equalization with Quasi-symmetric correction and (ii) a combined luminance and reflection decomposition and image fusion-based method. Computer simulations on benchmarking infrared Kuangxd database show that the proposed algorithm performance outperforms conventional image enhancement methods, including a cutting-edge learningbased method, in terms of subjective and objective evaluations. © 2023 SPIE.

关键词： image enhancement

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Entropy bottleneck network for IR image compression

Entropy bottleneck network for IR image compression

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multimodal image exploitation and learning 2023

作者： Depoian, Arthur C. Adams, Ethan R. Chiapputo, Nicholas Bailey, Colleen P. Guturu, Parthasarathy Department of Electrical Engineering University of North Texas DentonTX76207 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

The objective of image compression is to reduce irrelevance and redundancy of the image data to be able to store or transmit data in an efficient manner by minimizing the number of bits required to represent an image accurately. JPEG is capable of achieving an image compression ratio of 10:1 with little perceptible loss in image quality using standard metrics, and has become the most widely used standard image compression in the world since its release. Traditionally, compression techniques have relied on linear transforms to approximate 2-D signals (images), and the omission of specific constituent vectors has been mostly arbitrary. These techniques can save incredible amounts of memory while retaining image integrity. Recently techniques have been developed that use neural networks to approximate these signals. These networks offer the advantage of decorrelating image data to find a series of vectors to represent an image that is smaller than traditional techniques by estimating gradient descent, thus finding the minimum number of bits required to represent an image. Expansion to the development of these architectures is happening rapidly through informed design drawing upon other fields that have recently seen increased focus such as computer vision and image analysis applications. A novel efficient neural network is proposed in this work to compress infrared images at state of the art levels while preserving overall image quality to handle the demands spanning from the daily commute to combat environments. © 2023 SPIE.

关键词： Entropy

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Alpha-rooting Color image Enhancement for Neural Light Field Rendering

Alpha-rooting Color Image Enhancement for Neural Light Field...

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multimodal image exploitation and learning 2023

作者： Oluga, Emmanuel Young, Ben Hood, R. Lyle Grigoryan, Artyom M. College of Engineering and Integrated Design University of Texas at San Antonio San AntonioTX78249 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

In this paper, we explore the use of low-light image enhancement as a preprocessing step to improve the quality of novel view synthesis by Neural Light Fields (NeLF). NeLF is a 3D scene representation method that employs a light field representation, differing from prior methods based on volumetric rendering schemes. One of the main advantages of NeLF is its faster rendering speed, as it requires only one network forward pass without Ray marching. However, NeLF struggles to model low-illumination scenes due to its viewer-centered framework, which does not consider the interaction between illumination and scenes. To address this issue, we propose the use of 2D low-light image enhancement as a preprocessing solution. Our approach utilizes the Alpha-rooting by 2-D DFT as a preprocessing step to enhance low-light images prior to their use by the NeLF model. We demonstrate that this approach leads to significant improvements in the quality of novel view synthesis by NeLF on low-light images. We also consider how this can have practical applications in various domains such as applied Human biomechanics. © 2023 SPIE.

关键词： image enhancement

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