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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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A Novel Machine Learning Algorithm for Prostate Cancer image Segmentation using mpMRI

A Novel Machine Learning Algorithm for Prostate Cancer Image...

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Tushar Dhar Shukla K Kalpana. Richa Gupta D Kalpanadevi Md. Abul Ala Walid K Keshav Kumar Department of Physical Education UITTR Chandigarh University Punjab India School of CSE and IS Presidency University Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan koil Tamilnadu India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Department of Humanities and Mathematics G.Narayanamma Institute of Technology and Science (for Women) Hyderabad India

Recently, the advancements in technology and the changes in lifestyle behaviors of people leads to a sedentary routine of everyday habits. For this reason, numerous cancers have been developed and causes death for millions of people every year. Although, cancer is a deadly disease, early detection can help for survival. Especially for prostate cancer (PCa), early detection helps to cure the disease. Several researches have been done in medical image processing using Artificial Intelligence (AI) algorithms, yet accuracy and computational complexities limits the performance. With the intension of introducing a novel model for PCa detection from multi-parametric Magnetic Resonance Imaging (mpMRI), this study introduces an enhanced image segmentation model using the efficiency of Machine Learning (ML) algorithm together with Moth Flame Optimization (MFO) Algorithm to eradicate the previous issues. Generally, segmentation of an image is a partition of the image into multiple regions which enhances the classification performances. The major phases in this research includes 1. Data Pre-processing, 2. Feature Extraction, and finally,3. Segmentation. In data pre-processing, noises in the input images are eliminated using Gaussian filtering. The efficiency of MFO is employed to extract the optimal features from the images, and the extracted images are further subjected for U-Net segmentation. Moreover, the performance of the proposed model is validated through a comparative analysis over state-of the-art models in terms of DSC.

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Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators

Reconfigurable Distributed FPGA Cluster Design for Deep Lear...

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ieee international conference on Electro-Information Technology

作者： Hans Johnson Tianyang Fang Alejandro Perez-Vicente Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

We propose a distributed system based on low-power embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, and we have implementations that consist of up to 12 Zynq-7020 chip-based boards as well as 5 UltraScale+ MPSoC FPGA boards connected through an ethernet switch, and the cluster will evaluate configurable Deep Learning Accelerator (DLA) Versatile Tensor Accelerator (VTA). This adaptable distributed architecture is distinguished by its capacity to evaluate and manage neural network workloads in numerous configurations which enables users to conduct multiple experiments tailored to their specific application needs. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the computation graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph.

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Evolving Outlines Of Antarctic Active Subglacial Lakes Using An image processing Algorithm On Gridded Altimetry Data

Evolving Outlines Of Antarctic Active Subglacial Lakes Using...

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ieee international Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wilson Sauthoff Matthew R. Siegfried Benjamin E. Smith Department of Geophysics Colorado School of Mines Earth and Space Sciences University of Washington

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This investigation quantifies the dynamic geometries of Antarctic active subglacial lakes by leveraging satellite altimetry data and an image processing algorithm. Using high-resolution altimetry satellite data from 2010 to 2023 and a computer-vision-based contour finding algorithm to delineate contours of ice-surface, height-change anomalies indicative of lake activity, we generated evolving outlines of 131 previously identified Antarctic active subglacial lakes. Evolving outlines provided a novel time series of wetted area to quantify changes in the bed substrate available for geochemical and microbial processes. Evolving outlines revealed previously unseen transitory lake lobes that refine preexisting time series of height change and ice-volume displacement, from which we can infer subglacial water flux. The findings underscore the value of employing evolving subglacial lake outlines to record active subglacial lake geometric changes across Antarctica and suggest broader glaciological applications, such as the identification of new active subglacial lake candidates.

关键词： Geometry Satellites Uncertainty Heuristic algorithms Time series analysis Lakes Antarctica

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Multi-Class Classification of Lung Cancer Using Machine Learning and image processing

Multi-Class Classification of Lung Cancer Using Machine Lear...

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Computing (ICOCO), ieee international conference on

作者： Teerapat Charnsripinyo Naruemon Wattanapongsakorn Mathematics and Sciences Major Trium Udom Suksa School Bangkok Thailand Department of Computer Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

In this research paper, we present lung cancer classification using machine learning techniques. We used several techniques to classify the CT-scan images into three classes which are normal, benign and malignant (cancerous) classes. The dataset was obtained from a reliable source having 1096 images overall. The dataset was preprocessed to have balanced classes and feature extracted using multiple image filters. Many experimental cases were conducted using various machine learning techniques which are decision tree, random forest, support vector machine and K-nearest neighbor, and evaluated considering binary class classification and multi-class classification, which are normal vs. non-normal, cancerous vs. non-cancerous, and normal vs. benign vs. cancerous. Five performance metrices which are confusion matrix, accuracy, recall, precision and F1-score were considered. The results show that the random forest and K-nearest neighbor techniques that we consider performed very well. The normal class (healthy subjects) and the benign (not-harmful tumor) class can be correctly classified 97%-99%. Overall, we obtained the accuracy of 99.73 % for 2-class classification, and accuracy of 98.69 % for the 3-class classification.

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Downstream Task-agnostic Transferable Attacks on Language-image Pre-training Models

Downstream Task-agnostic Transferable Attacks on Language-Im...

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ieee international conference on Multimedia and Expo (ICME)

作者： Yiqiang Lv Jingjing Chen Zhipeng Wei Kai Chen Zuxuan Wu Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing

Vision-language pre-trained models (e.g., CLIP) trained on large-scale datasets via self-supervised learning, are drawing increasing research attention since they can achieve superior performances on multi-modal downstream tasks. Nevertheless, we find that the adversarial perturbations crafted on vision-language pre-trained models can be used to attack different corresponding downstream task models. Specifically, to investigate such adversarial transferability, we introduce a task-agnostic method named Global and Local Augmentation (GLA) attack to generate highly transferable adversarial examples on CLIP, to attack black-box downstream task models. GLA adopts random crop and resize at both global and local patch levels, to create more diversity and make adversarial noises robust. Then GLA generates the adversarial perturbations by minimizing the cosine similarity between intermediate features from augmented adversarial and benign examples. Extensive experiments on three CLIP image encoders with different backbones and three different downstream tasks demonstrate the superiority of our method compared with other strong baselines. The code is available at https://***/yqlvcoding/GLAattack.

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ACIGS: An automated large-scale crops image generation system based on large visual language multi-modal models

ACIGS: An automated large-scale crops image generation syste...

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ieee Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bolong Liu Hao Zhang Jie Liu Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science and Technology Harbin Institute of Technology (ShenZhen) ShenZhen China

Smart agriculture requires an extensive convergence of information technology and agriculture. Attaining intelligence mandates an enormous amount of data to train models. However, it is challenging to acquire a large number of crop image data, limiting the application and growth of computer vision technology in agriculture. To address this problem, we designed a crop image generation system that combines a large language model with visual language multi-modal large models to augment the scale, variety, and resolution of crop image data. First, the system inputs existing real crop images into the visual language multimodal model to extract features and represent crop images in text form. Then, the system passes the crop text representation to the language model for cleaning and processing, which generates prompts to create crop images. The prompts are input into the visual language multi-modal model to generate crop images based on text representation of crops. The resulting crop images undergo image quality evaluation in the visual language multimodal model, and high-quality crop images are saved to the crop image dataset based on the quality evaluation. These steps lead to the formation of the final generated crop image dataset. The experimental results indicate that the crop images generated using the proposed system are similar to but different from the example images. This characteristic enables the expansion of crop data while circumventing redundancy and allowing for resolution control, which is crucial for dense segmentation tasks. Using this method, the existing data can be enlarged up to 7.5 times.

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Bringing Monochrome to Life: A GAN-based Approach to Colorizing Black and White images

Bringing Monochrome to Life: A GAN-based Approach to Coloriz...

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international conference for Convergence of Technology (I2CT)

作者： Vedant Jolly Mansi Dwivedi Yash Patel Tejal Yadav Sudhir Dhage Computer Engineering Department Sardar Patel Institute of Technology Mumbai India Information Technology Sardar Patel Institute of Technology Mumbai India

The automatic image colorization technique has garnered a lot of attention over the past ten years for a variety of applications, including the restoration of old or damaged photos. Due to the many degrees of freedom in the assignment of color information, this problem is very poorly presented. Recent advances in automatic colorization either use photographs with a recurring theme or need highly processed data, like semantic maps, as input. CNN is highly adept at classifying objects and can quickly identify skies, trees, and other vegetation, faces, and people. Even if flags and famous monuments are represented in the data sets, colorization fails to effectively depict them when applied to these items. In our method, we use a conditional Conditional Generative Adversarial Network (Conditional GAN) to try and fully generalize the colorization process. The network is trained using publicly accessible datasets like CIFAR-10.

关键词： Training image color analysis Semantics Vegetation mapping Vegetation Gray-scale Generative adversarial networks

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PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

PointMBF: A Multi-scale Bidirectional Fusion Network for Uns...

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international conference on computer Vision (ICCV)

作者： Mingzhi Yuan Kexue Fu Zhihao Li Yucong Meng Manning Wang Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China Shandong Computer Science Center (National Supercomputer Center in Jinan)

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous learning-based works commonly focus on supervised registration, which have limitations in practice. Recently, with the advance of inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to achieve unsupervised registration. However, most of existing unsupervised methods follow a cascaded design or fuse RGB-D data in a unidirectional manner, which do not fully exploit the complementary information in the RGB-D data. To leverage the complementary information more effectively, we propose a network implementing multi-scale bidirectional fusion between RGB images and point clouds generated from depth images. By bidirectionally fusing visual and geometric features in multi-scales, more distinctive deep features for correspondence estimation can be obtained, making our registration more accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our method achieves new state-of-the-art performance. Code will be released at https://***/phdymz/PointMBF.

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Monocular 3D Human Pose Estimation Based on Global Temporal-Attentive and Joints-Attention In Video

Monocular 3D Human Pose Estimation Based on Global Temporal-...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ruhan He Shanshan Xiang Peng Tao Yongsheng Yu School of Computer and Artificial Intelligence Wuhan Textile University Wuhan China State Key Laboratory of Silicate Materials for Architectures Wuhan University of Technology Wuhan China

Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video, which is widely used in many 3D applications. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local contextual relations of human motion and ignores human joint hierarchies. To address this problem, we propose a Global Temporal-Attentive and Joints-Attention network (GTAJA-Net). This method introduces a Global Attention Feature Integration (GAFI) module and a Motion Tree Fusion Decoder (MTFD) module on the basis of a temporally consistent mesh recovery system (TCMR). A GAFI consisting of a collection of temporal features obtains final temporal features carrying spatial information that enhances temporal correlation and refine the features of the current frame. Meanwhile, MTFD aims at modeling the joint level attention. MTFD considers pose estimation as a top-down hierarchical process similar to SMPL kinematic tree. Though conceptually simple, our GTAJA-Net outperforms the state-of-the-art methods on the 3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets. Our code is available at https://***/xiangcece/GTAJA-Net.

关键词： Convolutional codes Three-dimensional displays Correlation Shape Pose estimation Kinematics Signal processing

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Improving Endoscopic image Quality through the Use of High Dynamic Range Imaging-Like Method with Real-Time Performance

Improving Endoscopic Image Quality through the Use of High D...

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Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Gurrala Ajay Kumar Rahul G S Preejith S P Mohanasankar Sivaprakasam Healthcare Technology Innovation Centre (HTIC) Indian Institute of Technology (IIT) Madras India Department of Electrical Engineering IIT Madras India

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

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