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image Reconstruction and Recognition of Optical Flow Based on Local Feature Extraction Mechanism of visual Cortex 1

引用

Proceedings of the 4th International conference on Neural Computing for Advanced Applications, NCAA 2023

作者： Lin, Wanyan Yi, Hao Li, Xiumin College of Automation Chongqing University Chongqing400030 China

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

Neurons in the medial superior temporal (MSTd) region of the visual cortex of the brain can efficiently recognize the firing patterns from the neurons in the MT region. The process is similar to sparse coding in non-negative matrix decomposition (NMF), and the modular recognition of images can be achieved through synaptic plasticity learning rules. In this paper, a spiking neural network model based on spike-timing-dependent plasticity (STDP) is built to simulate the processing process of the visual cortex region MT-MSTD. Our results show that STDP can perform similar functions as NMF, i.e. generating sparse and linear superimposed output based on local features, so as to accurately reconstruct input stimuli for image reconstruction. Finally, support vector machine is used to achieve image recognition of optical flow inputs in eight directions. Compared with NMF, local feature extraction using STDP does not need to retrain the decision layer during the testing procedure of new optical flow samples, contributing to more efficient recognition of optical flow images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neurons

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A Deep Learning-based Audio-in-image Watermarking Scheme

A Deep Learning-based Audio-in-Image Watermarking Scheme

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Das, Arjon Zhong, Xin Univ Nebraska Omaha Dept Comp Sci Omaha NE 68182 USA

ISBN: (纸本)9781728185514

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.

关键词： Audio-in-image watermarking deep learning neural networks robustness

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Prompt-Based Segmentation and Inpainting : A New Approach to Disaster image Creation 14

Prompt-Based Segmentation and Inpainting : A New Approach to...

引用

14th International conference on Information and Communication Technology Convergence, ICTC 2023

作者： Choi, Minji Choi, Ji Hoon Won, Ru-Bin Bae, Byungjun University of Science and Technology Korea Information and Communication Engineering Daejeon Korea Republic of Media Intellectualization Research Lab Daejeon Korea Republic of

ISBN: (纸本)9798350313277

This paper proposes a novel approach to disaster image generation using prompt-based segmentation techniques. By segmenting terrains based on the provided prompt and inputting disaster-related prompts into the segmented area, we explore a method to generate images that reflect disaster scenarios. Moreover, we propose a method to adjust the inpainting mask area according to the severity of the disaster, providing a visual representation that varies with the situation. We acknowledge limitations in areas such as the inpainting mask region and the overall disaster image generation, and suggest directions for further research to overcome these challenges. We emphasize that the methodology of our study contributes significantly to disaster management and information dissemination. © 2023 IEEE.

关键词： disaster alert disaster image image inpainting image processing image segmentation text to image model

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MED-GPVS: A Deep Learning-Based Joint Biomedical image Classification and visual Question Answering System for Precision e-Health

MED-GPVS: A Deep Learning-Based Joint Biomedical Image Class...

引用

IEEE International conference on communications (ICC)

作者： Haridas, Harishma T. Fouda, Mostafa M. Fadlullah, Zubair Md Mahmoud, Mohamed ElHalawany, Basem M. Guizani, Mohsen Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Thunder Bay Reg Hlth Res Inst TBRHRI Thunder Bay ON Canada Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN USA Benha Univ Fac Engn Shoubra Banha Egypt Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

General Purpose Vision System (GPVS) is a task-agnostic vision-language system that inputs an image and a question from which the system recognizes the tasks to be performed and outputs bounding boxes, confidence scores, and text outputs to answer the question. While much attention to GPVS has been recently given in the computer vision field, its medical field applications are still in their infancy. This paper presents MED-GPVS, a customized deep learning-based GPVS on biomedical images to perform various vision tasks, such as object detection and visual question answering, on medical images to facilitate precision medicine/e-health services. Our envisioned MED-GPVS takes an image and a natural language text as inputs, and then outputs bounding boxes, confidence scores, and generates a caption (i.e., the answer to the posed query). For example, if a medical image of a patient's abdomen is presented to MED-GPVS followed by the question: "does the picture contain stomach?", MED-GPVS should ideally provide the answer "yes" along with a prediction box and prediction score on the image. We utilize the multilingual SLAKE dataset, which was annotated by expert physicians with a full semantic label, to validate the performance of MED-GPVS under various scenarios involving different biomedical image-based diagnoses. For the visual question answering (VQA) task, MED-GPVS demonstrates encouraging performance with significantly high accuracy of 82.41%.

关键词： e-health precision medicine General Purpose Vision System (GPVS) DEtection TRansformer (DETR) Vision-and-Language Bidirectional Encoder Representations from Transformers (ViLBERT) object detection Natural Language processing (NLP)

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AnomalyHop: An SSL-based image Anomaly Localization Method

AnomalyHop: An SSL-based Image Anomaly Localization Method

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Kaitai Wang, Bin Wang, Wei Sohrab, Fahad Gabbouj, Moncef Kuo, C-C Jay Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90007 USA Tampere Univ Dept Comp Sci Tampere Finland

ISBN: (纸本)9781728185514

An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.

关键词： image anomaly localization successive subspace learning

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Alpha-trimmed Mean Filter and XOR based image Enhancement for Embedding Data in image

Alpha-trimmed Mean Filter and XOR based Image Enhancement fo...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Alam, S. M. Ammar Ul Huda, Moid Farhan, Muhammad Habib Univ Dhanani Sch Sci & Engr Karachi Pakistan

ISBN: (纸本)9781728185514

In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.

关键词： steganography alpha-trimmed mean LSB manipulation image enhancement data embedding

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Binocular visual Mechanism Guided No-Reference Stereoscopic image Quality Assessment Considering Spatial Saliency

Binocular Visual Mechanism Guided No-Reference Stereoscopic ...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Feng, Jinhui Li, Sumei Chang, Yongli Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In recent years, with the popularization of 3D technology, stereoscopic image quality assessment (SIQA) has attracted extensive attention. In this paper, we propose a two-stage binocular fusion network for SIQA, which takes binocular fusion, binocular rivalry and binocular suppression into account to imitate the complex binocular visual mechanism in the human brain. Besides, to extract spatial saliency features of the left view, the right view, and the fusion view, saliency generating layers (SGLs) are applied in the network. The SGL apply multi-scale dilated convolution to emphasize essential spatial information of the input features. Experimental results on four public stereoscopic image databases demonstrate that the proposed method outperforms the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.

关键词： stereoscopic image quality assessment binocular visual mechanism spatial saliency convolutional neural network

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No-Reference Stereoscopic image Quality Assessment Based on The visual Pathway of Human visual System

No-Reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Meng, Fan Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full of challenges due to the complex relationship between binocular views. In this article, firstly, convolutional neural network (CNN) based on the visual pathway of human visual system (HVS) is built, which simulates different parts of visual pathway such as the optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. Secondly, the two pathways of our method simulate the 'what' and 'where' visual pathway respectively, which are endowed with different feature extraction capabilities. Finally, we find a different application way for 3D-convolution, employing it fuse the information from left and right view, rather than just extracting temporal features in video. The experimental results show that our proposed method is more in line with subjective score and has good generalization.

关键词： Stereoscopic image quality assessment (SIQA) Convolutional neural network (CNN) the human visual system (HVS) 3D-convolution (C3D)

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Single image Dehazing Using Multipath Networks Based on Chain of U-Nets 1

引用

7th International conference on Computer Vision and image processing, CVIP 2022

作者： Deivalakshmi, S. Sandana, J. Sudaroli Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India

ISBN: (数字)9783031314070

ISBN: (纸本)9783031314063

Single-image haze removal is a necessary pre-processing step for several computer vision applications and may be a challenging task. The images captured outdoors are seriously degraded in color and contrast due to extreme weather conditions like fog, haze, snow and rain. This poor visual quality can inhibit the performance, which is intended to operate on clear conditions like object detection. In recent years, single-image dehazing, which recovers clean images from haze-affected images, has gained popularity. This paper proposes a unique and efficient multipath supported U-Net called Ladder-Net to directly restore a hazy free image from a hazy image. Utilizing only a single set of encoder and decoder branches within the U-Net structure, the proposed Ladder-Net has numerous encoder and decoder branches and dense skip connections between each pair of adjoining encoder and decoder branches at every level. The encoder and decoder are designed to acquire the contextual information in the input images and to evaluate the role of each input to the resulting hazy-free images, respectively, without relying on the atmospheric scattering model. The proposed model is trained and tested using RESIDE benchmark dataset. Experiments conducted on hazy images and a synthetic dataset obtained naturally show that the proposed method proves effective in achieving superior performance over previous methods. The experimental results convey that the proposed architecture achieves exceptional performance over the prevailing methods concerning the qualitative and the quantitative performance, PSNR and SSIM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Object detection

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Low-Light image Enhancement Algorithm Based on the Fusion of Multi-scale Features and Attention Mechanism 1

引用

11th International conference on communications, Signal processing, and Systems, CSPS 2022

作者： Sun, Youchen Zhang, Baoju Zhang, Bo Zhang, Cuiping Zhang, Jin Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China

ISBN: (数字)9789819923625

ISBN: (纸本)9789819923618

Aiming at the problems of Retinex-Net such as large noise of reflection component, low brightness of illumination component and insufficient feature extraction, a low-light image enhancement algorithm based on fusion of multi-scale features and attention mechanism is proposed. First, the Retinex-Net network is used as the basic model to decompose the input image, and the atrous convolution and ordinary convolution are fused to achieve multi-scale feature extraction to obtain more detailed information;multi-layer attention is introduced into the enhanced network. The force mechanism module enhances the brightness of the details and illumination components;finally, the denoised reflection components and the enhanced illumination components are fused into a normal illumination image output. Experiments show that the algorithm can effectively improve the details of the image and improve the visual effect of the image. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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