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Optimized Bio-inspired Spiking Neural Models based Anatomical and Functional Neurological image Fusion in NSST Domain 27

Optimized Bio-inspired Spiking Neural Models based Anatomica...

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27th National conference on communications (NCC)

作者： Das, Manisha Gupta, Deep Radeva, Petia Bakde, Ashwini Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur Maharashtra India Univ Barcelona Dept Math & Comp Sci Barcelona Spain Comp Vis Ctr Barcelona Spain All India Inst Med Sci Dept Radiodiag Nagpur Maharashtra India

ISBN: (纸本)9781665441773

Fusion of complementary anatomical and functional information present in multi-modal medical images provides improved visualization of various bodily structures and assists radiologist to infer more factual diagnostic interpretations. Inspired by the neuronal assemblies of mammal's visual cortex, spiking neural models such as dual-channel pulse coupled neural network (DCPCNN) and coupled neural P (CNP) system efficiently extract and integrate complementary information present in the source images. But, these models have various free parameters which are set using hit and trial approach in most of the conventional fusion methods. This paper presents an optimized multi-modal medical image fusion method in non-subsampled sheartlet transform (NSST) domain wherein the free parameters of both DCPCNN and CNP system are optimized using multi-objective grey wolf optimization (MOGWO). Extensive experiments are performed on various anatomical-functional images. Subjective and objective result analysis indicate that the proposed method effectively fuse important diagnostic information of the source images and also outperforms other state of the art fusion methods.

关键词： Medical image fusion non-subsampled sheartlet transform dual-channel pulse coupled neural network coupled neural P system

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A Secure Wavelet based Watermarking Approach using Psycho-visual Redundancy in image 7

A Secure Wavelet based Watermarking Approach using Psycho-Vi...

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7th International conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Sinha, Ashish Panda, Jeebananda Delhi Technological University Technology Student Delhi India

ISBN: (纸本)9781665405201

The principal requirements of any digital watermarking algorithm are imperceptibility, robustness, security and capacity of the watermark [2], [9]. Imperceptibility and robustness are inherently conflicting in nature. Over numerous trials of digital watermarking of multimedia, it has been found that embedding a watermark with high strength also decreases its obscurity and makes the watermark more prone to detection. On the other hand, a watermark embedded with a low strength improves the imperceptibility but causes degradation of robustness against signal processing, geometric and feature distortive attacks. This paper proposes a secure approach for watermarking using the image Partitioning approach, which exploits the psycho-visual redundancy in the image to embed the watermark. Using the chaotic scrambling based Arnold Transform, watermark is first scrambled and then embedded in the sub-band of the image obtained using the Discrete Wavelet Transform and characterized by the lowest frequency. This improves the security as well as enhances the imperceptibility of the watermark, without significant deviation in visual appearance of the watermarked image from the cover image. Further, frequency domain embedding leads to better robustness and stability. The method is used to perform watermarking of grayscale images and performs well when subjected to common distortions, geometric, order-statistic filtering and signal processing attacks. This was determined by calculating the Peak Signal to Noise Ratio of the watermarked cover image and the retrieved watermarks and the Normalized Cross-Correlation of the embedded and extracted watermarks. © 2021 ieee.

关键词： Discrete wavelet transforms

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Are Large Language Models Good Neural Architecture Generators for Edge?

Are Large Language Models Good Neural Architecture Generator...

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ieee International conference on Edge Computing (EDGE)

作者： Hadjer Benmeziane Kaoutar El Maghraoui IBM Research Europe Rüschlikon Switzerland IBM T. J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

Neural Architecture Search (NAS) is an incredibly indispensable technique in edge computing scenarios where computational resources are limited. However, the vastness of search spaces presents a formidable obstacle, hindering efficient exploration and identification of optimal architectures. In response, our study introduces an innovative paradigm that characterizes hardware and deep learning architectures, addressing the urgent demand for streamlined and swift architectural design *** approach pioneers the utilization of a fine-tuned Large Language Model (LLM) to navigate the intricate terrain of architectural space. By harnessing the innate capabilities of LLMs, traditionally associated with natural language processing tasks, we extend their application domain to the realm of neural architecture design. Empirical assessments conducted on benchmark datasets such as CIFAR-10 and imageNet highlight the effectiveness of our fine-tuned LLM-based methodology. Notably, our findings reveal performance levels on par with stateof-the-art (SOTA) NAS techniques, achieving unparalleled speed and completing architectural searches within seconds.

关键词： Navigation Large language models image edge detection Deep architecture Computer architecture Natural language processing Hardware

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Compressed Video Quality Metric Based on Just-Noticeable-Difference and Saliency-aware Blocking Detection 7

Compressed Video Quality Metric Based on Just-Noticeable-Dif...

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7th International conference on Computer and communications, ICCC 2021

作者： Wang, Zheng Liao, Guodong Wu, Yuxuan Lin, Liqun Yang, Jing Zheng, Haifeng Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Huawei Technologies Co.Ltd. Guangdong Shenzhen China

ISBN: (纸本)9781665409506

Block-based prediction-quantization hybrid coding framework is widely used in video compression, which results in visually annoying artifacts, i.e., blocking artifacts, on encoded videos. They significantly degrade the Quality-of-Experience (QoE). To improve the QoE, it is crucial to develop Video Quality Assessment (VQA) methods that can identify and quantify various artifacts, among which the negative effect introduced by blocking artifacts is of guiding significance. To evaluate and monitor video quality effectively, a novel Blocking-based Compressed Video Quality Assessment (BCVQA) is proposed in this paper. The key idea of the BCVQA method is a blocking detection model based on Just-Noticeable-Difference (JND) mechanism, which is aimed to improve detection performance, since it only focuses on the observable blocking artifacts. Furthermore, the saliency model is utilized in quality prediction procedure after blocking detection, considering the visual attention mechanism. With the fusion of the above two visual mechanisms, quality prediction of compressed videos becomes more effective, since the useful information is refined in our framework. Experimental results show that the proposed BCVQA is consistent with perceived quality and can effectively evaluate video quality. © 2021 ieee.

关键词： image compression

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Indoor Scene Change Understanding (SCU): Segment, Describe, and Revert Any Change

Indoor Scene Change Understanding (SCU): Segment, Describe, ...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Mariia Khan Yue Qiu Yuren Cong Bodo Rosenhahn David Suter Jumana Abu-Khalaf School of Science Centre of AI and Machine Learning Edith Cowan University Australia National Institute of Advanced Industrial Science and Technology Artificial Intelligence Research Center Japan Institute for Information Processing Leibniz University of Hannover

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Understanding of scene changes is crucial for embodied AI applications, such as visual room rearrangement, where the agent must revert changes by restoring the objects to their original locations or states. visual changes between two scenes, pre- and post-rearrangement, encompass two tasks: scene change detection (locating changes) and image difference captioning (describing changes). While previous methods, focused on sequential 2D images, have addressed these tasks separately, it is essential to emphasize the significance of their combination. Therefore, we propose a new Scene Change Understanding (SCU) task for simultaneous change detection and description. Moreover, we go beyond change language description generation and aim to generate rearrangement instructions for the robotic agent to revert changes. To solve this task, we propose a novel method - EmbSCU, which allows to compare instance-level change object masks (for 53 frequently-seen indoor object classes) before and after changes and generate rearrangement language instructions for the agent. EmbSCU is built on our Segment Any Object Model (SAOMv2) - a fine-tuned version of Segment Anything Model (SAM), adapted to obtain instance-level object masks for both foreground and background objects in indoor embodied environments. EmbSCU is evaluated on our own dataset of sequential 2D image pairs before and after changes, collected from the Ai2Thor simulator. The proposed framework achieves promising results in both change detection and change description. Moreover, EmbSCU demonstrates positive generalization results on real-world scenes without using any real-life data during training. The dataset and the code are available here.

关键词： Training visualization image segmentation Adaptation models image color analysis Large language models Stars Indoor environment Problem-solving Intelligent robots

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Decoding with Purpose: Improving image Reconstruction from fMRI with Multitask Learning 4

Decoding with Purpose: Improving Image Reconstruction from f...

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4th ieee International conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Lad, Abhi Patel, Reema Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat Gandhinagar382007 India Indian Institute of Information Technology Department of Computer Science and Engineering Gujarat Surat395007 India

ISBN: (纸本)9781728199511

Advancements in machine learning and computer vision have enabled medical researchers to find a breakthrough in computational neuroscience at a pace never seen before. However, such methods and tools remain inaccessible due to the semantic gap between the domains. One such neural information processing task, decoding fMRI to generate perceived images and analyze visual cortex function, has emerged as an exciting research topic. Existing works use multiple linear decoders or complete end-to-end deep networks for this task. Here we present a novel approach to neural network regression training for decoding tasks and compare the image reconstruction results with existing methods. First, we train an image generator model, which takes image features as input for image reconstruction, on random images which are not part of (fMRI→image) training pairs. The main highlight of the proposed system is our decoder with custom loss function and purpose-based training, which uses the discriminator of our image generator model to improve the image generation quality from decoded image features. Upon testing, our proposed approach with a correlation winning percentage of ∼81% manages to surpass the existing methods (∼78%) in reconstructing test images with better colour and shape reproduction. The approach proposed in this study provides better generalizability over existing decoder-based approaches and more control over training than end-to-end approaches, which can be extended to other such neural information processing tasks. © 2021 ieee.

关键词： image reconstruction

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2021 ieee Colombian conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

2021 IEEE Colombian Conference on Applications of Computatio...

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2021 ieee Colombian conference on Applications of Computational Intelligence, ColCACI 2021

ISBN: (纸本)9781665435345

The proceedings contain 13 papers. The topics discussed include: low cost classification method for differentiated white blood cells using digital image processing and machine learning algorithms;respiratory sounds classification employing a multi-label approach;stock price analysis with deep-learning models;mobile application for anemia detection through ocular conjunctiva images;face gesture recognition using deep-learning models;near helipad auto guidance system for landing and take-off of helicopter;low cost classification method for differentiated white blood cells using digital image processing and machine learning algorithms;automated preprocessing pipeline for EEG analysis in visual imagery tasks;and Coccinellidae beetle specimen detection using convolutional neural networks.

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Unsupervised image Region of Interest Detection using Discrete Cosine Transform 4

Unsupervised Image Region of Interest Detection using Discre...

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4th International conference on Information Communication and Signal processing, ICICSP 2021

作者： Olaode, Abass Naghdy, Golshah Utaminingrum, Fitri School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong Australia Computer Vision Research Groups Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9781665407571

Spatial domain analysis of images has been recognised as an important task in image retrieval and computer vision, however, its application in the determination of image Region of Interests often require the inclusion of supervised training and similarity computations, both of which can be difficult to implement during the modelling of the images in a large number of images. however, unsupervised image Region of Interest determination via Frequency domain analysis can be implemented without training or similarity computation therefore it is more suitable for the modelling of images in a large set. This paper demonstrates image frequency domain analysis via Discrete Cosine Transform and presents its application in the unsupervised determination of Region of Interests for the elimination of the spatial incoherency often associated with the Bag-of-visual Word Modelling. © 2021 ieee.

关键词： Discrete cosine transforms

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Implementation of Object Detection and Recognition Based On Exploration Deep Neural Network Features for Quadcopter 5

Implementation of Object Detection and Recognition Based On ...

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5th International conference on Information and communications Technology, ICOIACT 2022

作者： Corputty, Felix Wibowo, Suryo Adhi Rizal, Syamsul Telkom University Image Processing and Vision Laboratory Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Deep learning is part of Machine Learning whose algorithm is based on the structure of the neural network in the human brain. Object detection is a technology that uses the concept of deep learning. The development of object detection has penetrated into various fields, one of which is the quad-copter. However, object detection still has some problems in the quadcopter field. One of the problems is that the implementation of deep learning models for small quadcopters is very difficult because of the limited hardware capabilities of the quadcopter. Based on these problems, in this study an object detection system was designed and implemented using the YOLOv5 method. The feature exploration carried out focuses on the first convolutional layer in the YOLOv5 algorithm by using kernel sizes of 5×5, 7× 7 and 8 × 8 on that layer, which originally was 6 × 6. Based on experimental results, the highest mAP value is obtained by kernel size 5×5 with a mAP value 89.1% or 1.2% greater than the original size YOLOv5 which has an mAP value of 87.9% © 2022 ieee.

关键词： Object detection

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Classification of CXR of COVID-19 Based on ICA Dimensionality Reduction 2

Classification of CXR of COVID-19 Based on ICA Dimensionalit...

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2nd International conference on Advances in Engineering Science and Technology, AEST 2022

作者： Al-Mothafar, Mohanad S. Abdullah, Ahmed Kareem Al-Juaifry, Faris Mohammed Technical Collage Al-Najaf Department of Communication Engineering Najaf Iraq AlFurat Alawsat Technical University Technical College-AL Mussaib-Babel Department of Electrical Power Engineering Technique Iraq Department of Optical Communications Engineering Najaf Iraq

ISBN: (纸本)9798350334906

The medical image data like chest x-ray (CXR) image tends to be more complex than traditional of other image data and needs dimensionality reduction. Whereas reducing the dimensions of the CXRs images should not affect the main characteristics, and maintain the same analytical results, therefore, reducing the dimensions is important in data pre-processing, to obtain faster time during the training process of the neural network and high accuracy results. In this paper, a CXRs images dimensional reduction method based on independent component analysis (ICA) is presented to Improve convolution neural network (CNN) Training Times, where the shorter training times give to allow for experimentation more with new datasets. Training and validation were performed on the datasets after Dimensionality Reduction through deep learning (DL) algorithms. The results demonstrate that the pre-processed dataset obtained after dimension reduction demonstrates the highest training rate and best accuracy in comparison with the original datasets. According to experimental findings, using ICA with the CNN method, which provides a solution for network training times. The results demonstrate that the proposed CNN method achieves up to 97% classification accuracy and out-performs other dimensionality reduction methods. © 2022 ieee.

关键词： Independent component analysis

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