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A review on multimodal medical image fusion towards future research

MULTIMEDIA TOOLS AND applications 2023年第5期82卷 7361-7382页

作者： Venkatesan, B. Ragupathy, U. S. Natarajan, Indhu Kongu Engn Coll Dept Elect & Instrumentat Engn Erode Tamil Nadu India Sri Shakthi Inst Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

image fusion is a technique used to merge two or more source images into a single image that incorporates more details than the originals and still offering an accurate depiction about the captured information. Resultant fused images are more accurate and provide comprehensive information for both the human and machine vision perception for further processing of the image. image fusion provides better performance in the areas like pattern recognition, image processing, computer vision, machine learning and artificial intelligence. In the recent years image fusion has moved out of the laboratories and used in the real time applications. This paper provides the insight of various techniques for image fusion like primitive fusion (Simple averaging, Maxima and Minima, etc.), Discrete Wavelet Transform (DWT) based fusion, Principal Component Analysis (PCA) based fusion, Curvelet transform based fusion etc. On-going through various literatures, it is found that image fusion in spatial domain provides high resolution images, although the fusion algorithms are dependent on the nature of image and also depends on the application for which the image is to be fused. Hence, spectral domain fusion and hybrid fusion techniques are introduced and it is proven to be better than the spatial domain fusion. Comparison of all the techniques along with recent approaches are done to find the best approach towards future research to provide new direction to the researchers in medical sector.

关键词： image fusion Imaging modalities Medical imaging Spatial domain Transform domain

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MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification 13

MaskChanger: A Transformer-Based Model Tailoring Change Dete...

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13th Iranian/3rd International machine vision and image processing Conference (MVIP)

作者： Ebrahimzadeh, Mohammad Manzuri, Mohammad Taghi Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated change interpretation challenging. We propose MaskChanger - a novel deep learning paradigm tailored for satellite image change detection. Our method adapts the segmentation-specialized Mask2Former architecture by incorporating Siamese networks to extract features separately from bi-temporal images, while retaining the original mask transformer decoder. To our knowledge, this is the first study in which change detection is converted from the existing per-pixel classification approach into a mask classification approach. Evaluated on the LEVIR-CD benchmark of over 600 very high-resolution image pairs exhibiting real-world rural and urban changes, MaskChanger achieves F1-Score of 91.96%, outperforming prior transformer-based change detection approaches.

关键词： Change Detection Remote Sensing image Transformer

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Exploring photosensitive nanomaterials and optoelectronic synapses for neuromorphic artificial vision

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CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE 2025年 35卷

作者： Lee, Hyun-Haeng Ro, Jun-Seok Kim, Kwan-Nyeong Park, Hea-Lim Lee, Tae-Woo Seoul Natl Univ Dept Mat Sci & Engn Seoul 08826 South Korea Seoul Natl Univ Sci & Technol Dept Mat Sci & Engn Seoul 01811 South Korea Seoul Natl Univ Inst Engn Res Res Inst Adv Mat Dept Chem & Biol EngnInterdisciplinary Program Bi 1 Gwanak Ro Seoul 08826 South Korea SN Display Co Ltd Seoul 08826 South Korea

Artificial vision systems will be essential in intelligent machine-vision applications such as autonomous vehicles, bionic eyes, and humanoid robot eyes. However, conventional digital electronics in these systems face limitations in system complexity, processing speed, and energy consumption. These challenges have been addressed by biomimetic approaches utilizing optoelectronic synapses inspired by the biological synapses in the eye. Nano- materials can confine photogenerated charge carriers within nano-sized regions, and thus offer significant potential for optoelectronic synapses to perform in-sensor image-processing tasks, such as classifying static multicolor images and detecting dynamic object movements. We introduce recent developments in optoelectronic synapses, focusing on use of photosensitive nanomaterials. We also explore applications of these synapses in recognizing static and dynamic optical information. Finally, we suggest future directions for research on optoelectronic synapses to implement neuromorphic artificial vision.

关键词： Optoelectronic synapses Nanomaterials Artificial vision systems Artificial synapses Neuromorphic bioelectronics

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An image-Based Transfer Learning Approach for Using In Situ processing Data to Predict Laser Powder Bed Fusion Additively Manufactured Ti-6Al-4V Mechanical Properties

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3D PRINTING AND ADDITIVE MANUFACTURING 2025年第1期12卷 48-60页

作者： Luo, Qixiang Shimanek, John D. Simpson, Timothy W. Beese, Allison M. Penn State Univ Dept Mat Sci & Engn University Pk PA USA Penn State Univ Dept Ind & Mfg Engn University Pk PA USA Penn State Univ Dept Mech Engn University Pk PA USA Penn State Univ Dept Mat Sci & Engn University Pk PA 16802 USA

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4V fabricated through laser powder bed fusion (PBF-LB) AM using in situ photodiode processing signals. Samples were fabricated under different processing parameters, varying laser powers and scan speeds for the purpose of probing a wide range of microstructure and property variations. Photodiode data were collected during fabrication, later to be arranged in image format and extracted to information-dense vectors by the transferal of deep convolutional neural network (DCNN) structures and weights pretrained on a large computer vision benchmark image database. The extracted features were then used to train and test a newly designed regression model for mechanical properties. Average cross-validation accuracies were found to be 98.7% (r(2) value of 0.89) for the prediction of ultimate tensile strength, which ranged from 900 to 1150 MPa in the samples studied, and 93.1% (r(2 )value of 0.96) for the prediction of elongation to fracture, which ranged from 0 to 17%. Thus, with high accuracy and hardware-accelerated inference speeds, we demonstrate that a transfer learning framework can be used to predict strength and ductility of metal AM components based on processing signals in PBF-LB, illustrating a potential route toward real-time closed-loop control and process optimization of PBF-LB in industrial applications.

关键词： machine learning computer vision deep convolution neural network laser powder bed fusion in situ processing monitoring

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Task-Switchable Pre-Processor for image Compression for Multiple machine vision Tasks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 6416-6429页

作者： Yang, Mingyi Yang, Fei Murn, Luka Blanch, Marc Gorriz Sock, Juil Wan, Shuai Yang, Fuzheng Herranz, Luis Xidian Univ Sch Telecommun Engn Xian Peoples R China Nankai Univ Coll Comp Sci Tianjin 300350 Peoples R China BBC Res & Dev London EC4Y 0DS England Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia Univ Autonoma Barcelona Comp Vis Ctr Barcelona 08193 Spain

Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications.

关键词： Task analysis Codecs machine vision image coding Semantics Bit rate Feature extraction image compression for machine vision Pre-processor Multiple tasks

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Interactive Enhancement of Tourism Product Design Using machine vision and CAD Technology

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Computer-Aided Design and applications 2024年第S15期21卷 210-226页

作者： Li, Xiaojing Zhai, Juan School of Tourism Xinyang Vocational and Technical College Hennan Xinyang464000 China

The interactivity of tourism product design improves the user experience and promotes tourism development. However, it faces challenges in technology realization, user experience, data processing, differentiated design, and innovation. Therefore, it is necessary to study how to use machine vision and CAD (computer-aided design) technology to enhance the interactivity of tourism product design and the attraction and competitiveness of tourism products. This article explores the application of machine vision and CAD technology in enhancing the interactivity of tourism product design and designs a comparative experiment to verify the effect of machine vision and CAD technology in enhancing interactivity. The experiment shows that the interactive enhancement method based on machine vision and CAD technology not only improves the design efficiency and stability at the technical level but, more importantly, optimizes the user experience from the user's point of view so that users can participate in the design process more intuitively and truly, thus significantly improving user satisfaction. Moreover, the interactive enhancement method based on machine vision and CAD technology ensures more accurate and smooth interaction between designers and users by improving the accuracy of image recognition and CAD modeling. This advantage enables designers to realize users' intentions more accurately and improves the overall quality and efficiency of design. © 2024 U-turn Press LLC.

关键词： Computer aided design

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Illumination Consistency processing Based on Illumination Domain Signal-Guided Unsupervised Generative Adversarial Network for Flotation Froth images

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Xiaoli Zhang, Yinan Kong, Lingshuang Zhou, Jiayi Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China Changsha Univ Sch Elect Informat & Elect Engn Changsha 410083 Peoples R China

In the machine vision-based online monitoring of the flotation process, froth images acquired in real-time are subject to color distortion and excessive bright spots caused by inconsistent illumination, which hinders the effectiveness of image analysis and further online measurement for operating performance indicators. Current image processing methods struggle to correct color distortion and remove excess bright spots in froth images simultaneously. Therefore, in this article, an illumination domain signal-guided unsupervised generative adversarial network (IDS-GUGAN) is proposed for illumination consistency processing of flotation froth images. First, considering the varying effects of inconsistent illumination on froth images, the illumination domain signal-guided image generation (IDS-GIG) mechanism based on the theory of unsupervised disentangled representation learning is designed to achieve adaptive correction of froth images with varying degrees of distortion. Moreover, a novel lightweight double-closed-loop network architecture is introduced to support unsupervised learning utilizing unpaired froth images and improve computational efficiency, which makes the proposed approach highly suitable for industrial applications. Comprehensive experiments on a real tungsten cleaner flotation process dataset and two public benchmark datasets related to image illumination processing tasks consistently endorse the superiority of IDS-GUGAN.

关键词： Flotation froth image generative adversarial network (GAN) illumination consistency processing unsupervised disentangled representation learning Flotation froth image generative adversarial network (GAN) illumination consistency processing unsupervised disentangled representation learning

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Combining Creative Adversarial Networks with Art Design Models and machine vision Feedback Optimization

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Computer-Aided Design and applications 2024年第S15期21卷 103-116页

作者： Tian, Qiang Li, Qisong Art School Anhui Jianzhu University Anhui Hefei230601 China Department of Architecture National Taiwan University of Science and Technology Taipei106335 Taiwan

In the field of art and design, different artistic styles endow works with unique charm and expressive power. Computer-aided design (CAD) model processing in art and design refers to the stage of using computer technology to process CAD models in art and design works. The traditional CAD model processing methods mainly include rule-based optimization, parametric design, etc. However, these methods often struggle to achieve ideal results when faced with complex and diverse art and design works. This article proposes an art and design CAD model processing and machine vision feedback optimization method based on Generative Adversarial Networks (GAN). This method combines an image sparse encoding algorithm to repair and optimize art images, guiding the optimization direction of the model. Through experimental verification and subjective assessment by observers, the results show that this method is significantly superior to traditional design methods in terms of image restoration accuracy, quantity of erroneous pixels, and user satisfaction. This method improves the quality of restoration and creation and provides new impetus for the progress of art and design. © 2024 U-turn Press LLC.

关键词： Generative adversarial networks

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Dual-Adaptive Heterojunction Synaptic Transistors for Efficient machine vision in Harsh Lighting Conditions

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ADVANCED MATERIALS 2024年第32期36卷 2404160-2404160页

作者： Wang, Yiru Nie, Shimiao Liu, Shanshuo Hu, Yunfei Fu, Jingwei Ming, Jianyu Liu, Jing Li, Yueqing He, Xiang Wang, Le Li, Wen Yi, Mingdong Ling, Haifeng Xie, Linghai Huang, Wei Nanjing Univ Posts & Telecommun NJUPT State Key Lab Organ Elect & Informat Displays Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun NJUPT Inst Adv Mat IAM Nanjing 210023 Peoples R China Northwestern Polytech Univ Frontiers Sci Ctr Flexible Elect FSCFE MIIT Key Lab Flexible Elect KLoFE Xian 710072 Peoples R China

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes. Organic heterojunction transistors are designed to integrate light intensity-adaptive threshold sliding and second-order adaptive metaplasticity. The unique dual adaptability enables the highlighting of 0.4% low-contrast images, and the efficient recognition can be achieved benefiting from the learning rate changes in the backpropagation process. image

关键词： adaptation machine vision metaplasticity organic heterojunction visuomorphic computing

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Smartphone based app development with machine learning using Hibiscus sabdariffa L. extract for pH estimation

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 257卷

作者： Aydin, Omer Faruk Aydin, Merve Demir, Melisa Caliskan Kahraman, Sibel Istanbul Aydin Univ Dept Comp Programming Istanbul Turkiye Marmara Univ Dept Control & Automat Technol Istanbul Turkiye Istanbul Aydin Univ Dept Ind Engn Istanbul Turkiye Istanbul Aydin Univ Dept Food Engn Istanbul Turkiye

This study presents a novel approach for pH estimation in buffer solutions using images of solutions prepared with Hibiscus sabdariffa L. as a natural pH indicator. The images of the solutions, each displaying distinctive colours indicative of their pH levels, were transformed into standardized 200x200-pixel images through the application of image processing techniques. Following this, a pH prediction model was constructed using the Adaptive Boosting regressor algorithm. The pH values of the training data used when training the model were distributed irregularly between 0-14. The models were trained with 94 pictures and 1880 experimental values. In addition, a reliable pre-processing part has been placed into the model using image processing techniques, allowing test data to be obtained in any desired environment. The obtained training and test data were separated from noise parameters, affecting the prediction results negatively. A smartphone application based on the model has been developed and made available to everyone. This innovative methodology bridges the gap between traditional pH measurement techniques and computer vision, offering amore accessible and eco-friendly means of pH assessment. The practical applications of this research extend to various fields, including environmental monitoring, agriculture, and educational settings.

关键词： machine learning image processing pH estimation Hibiscus sabdariffa L. Smartphone

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