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SE3D: A FRAMEWORK FOR SALIENCY METHOD EVALUATION IN 3D IMAGING 31

SE3D: A FRAMEWORK FOR SALIENCY METHOD EVALUATION IN 3D IMAGI...

引用

2024 international conference on Image processing

作者： Wisniewski, Mariusz Giulivi, Loris Boracchi, Giacomo Politecn Milan DEIB Milan Italy

ISBN: (纸本)9798350349405;9798350349399

For more than a decade, deep learning models have been dominating in various 2D imaging tasks. Their application is now extending to 3D imaging, with 3D Convolutional Neural Networks (3D CNNs) being able to process LIDAR, MRI, and CT scans, with significant implications for fields such as autonomous driving and medical imaging. In these critical settings, explaining the model's decisions is fundamental. Despite recent advances in Explainable Artificial Intelligence, however, little effort has been devoted to explaining 3D CNNs, and many works explain these models via inadequate extensions of 2D saliency methods. A fundamental limitation to the development of 3D saliency methods is the lack of a benchmark to quantitatively assess these on 3D data. To address this issue, we propose SE3D: a framework for Saliency method Evaluation in 3D imaging. We propose modifications to ShapeNet, ScanNet, and BraTS datasets, and evaluation metrics to assess saliency methods for 3D CNNs. We evaluate both state-of-the-art saliency methods designed for 3D data and extensions of popular 2D saliency methods to 3D. Our experiments show that 3D saliency methods do not provide explanations of sufficient quality, and that there is margin for future improvements and safer applications of 3D CNNs in critical fields.

关键词： Deep Learning Saliency Maps 3D Convolutions Computer vision

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ACCURATE COLON SEGMENTATION USING 2D CONVOLUTIONAL NEURAL NETWORKS WITH 3D CONTEXTUAL INFORMATION 31

ACCURATE COLON SEGMENTATION USING 2D CONVOLUTIONAL NEURAL NE...

引用

2024 international conference on Image processing

作者： Harb, Samir Elsayed, A. Yousuf, M. Alkabbany, I. Ali, A. Elshazley, S. Farag, A. Univ Louisville Comp Vision & Image Proc Lab CVIP Louisville KY 40292 USA Higher Technol Inst 10 Ramadan City Egypt Ain Shams Univ Fac Engn Cairo Egypt Assiut Univ Fac Engn Assiut Egypt Kentucky Imaging Technol Louisville KY USA

ISBN: (纸本)9798350349405;9798350349399

This study introduces an innovative framework designed specifically for accurate colon segmentation in abdomen CT scans, tackling the distinct challenges inherent to this task. Building upon well-established 2D segmentation models, our architecture adeptly incorporates 3D contextual information via a novel method that generates an attention map for a given slice by considering its neighboring slices in a sequence. Our approach accomplishes effective colon segmentation without requiring complex 3D convolutional neural networks (CNNs) or Long Short-Term Memory (LSTM) networks by combining 2D CNNs. Validated on a dataset of 98 CT scans from 49 patients, the architecture exhibits notable performance, successfully capturing nuanced details crucial for precise colon segmentation. The experiments encompass a thorough examination of model selection and cross-validation, providing valuable insights into the efficacy of our proposed approach. The outcomes underscore the potential for streamlined colon segmentation in medical imaging by judiciously integrating 2D and 3D information, employing solely 2D networks, and mitigating challenges associated with 3D networks. The code for model architecture is available at: https://***/Samir-Farag/***

关键词： CRC CTC colon segmentation deep learning

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vision-ENABLED robot-MILL COLLABORATION FOR HYBRID MANUFACTURING 18

VISION-ENABLED ROBOT-MILL COLLABORATION FOR HYBRID MANUFACTU...

引用

ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Goodwin, Jesse Saldana, Christopher Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9780791887240

Recently, convergent manufacturing, or the combination of multiple processes into a single system, has gained attention for potential uses in hybrid additive-subtractive manufacturing. Here, a robot-CNC machine combination is explored as a convergent solution to hybrid manufacturing. Usually, hybrid manufacturing is performed either using a sequential process where parts are moved from additive to subtractive cells or using a process where all operations occur in a single machine. A less explored solution to the hybrid machining problem is to use a combination of two machines operating on the same workpiece to complete the process, allowing for interleaved or simultaneous hybrid processing. In this work, a flexible system is presented where a 6-axis robotic arm for deposition is combined with a 3- axis CNC mill for hybrid manufacturing. With this system, the robot can be moved to and away from the CNC as needed for a flexible manufacturing framework. With the added flexibility of the system, comes the problem of robot localization. vision based robot localization methods were implemented to address this problem, and studies were conducted to determine the effects of disturbances encountered in a manufacturing environment on the localization performance. Results show that the proposed vision system can locate the robot within.5 mm and.25 deg, causing minimal disruption to the machining process.

关键词： Rapid Prototyping and Solid Freeform Fabrication Welding and Joining Control and Automation

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Control System of an Autonomous Object Tracking robot 17

Control System of an Autonomous Object Tracking Robot

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17th international conference on Engineering of Modern Electric Systems, EMES 2023

作者： Barabas, Tiberiu Silaghi, Helga Spoiala, Dragos Gamcova, Maria University of Oradea Control Systems Engineering and Management Department Oradea Romania Technical University of Kosice Faculty of Electrical Engineering and Informatics Kosice Slovakia

ISBN: (纸本)9798350310634

This paper presents the control system of a mobile robot, capable of detecting, tracking and following an object of a certain color. The mobile robot was created for educational purposes, in the CIM laboratory of the University of Oradea. It is based on a crawler robot, controlled with a Raspberry Pi development system. This system controls the movements necessary for tracking the object: moving forward, backward, left turn or right turn. For detecting and locating the object, the robot is also equipped with a video camera connected to Raspberry Pi. The acquisition and processing of images in real time, provided by the camera, are performed with programming functions provided by the OpenCV (Open Source Computer vision Library). These functions are implemented in a control program, written in Python language. In order to facilitate the tracking of the images obtained and processed during the movements, the robot has also been provided with a graphic display connected to the Raspberry Pi board. This paper also presents the implementation of a numerical control algorithm used for tracking the objects. © 2023 IEEE.

关键词： Python

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Read Right: Empowering the Blind to Read the World

Read Right: Empowering the Blind to Read the World

引用

2024 IEEE international conference on signal processing and Advance Research in Computing, SPARC 2024

作者： Prasuna, Vempati Lakshmi Fathimabi, Sk. Adusumilli, Apoorva Rajesh, Bandaru Veera Venkata Velagapudi Ramakrishna Siddhartha Engineering College Information Technology Vijayawada India

ISBN: (纸本)9798350385199

In the real world, knowledge comes from books and papers. Now that information only reaches to those with clear vision. In the community there are a part of people suffering either from poor eyesight or blindness. Braille is one of the methods employed to interpret several reports or books, however obtaining all these files in braille may be prohibitively expensive as well as regularly impossible. Hence, considering these issues and flaws in existing systems this paper is designed as a simple user-friendly mobile application 'Read Right' that works on voice commands like 'Take Picture' to click an image of the document which extracts text from picture as well plays it out (audio) also providing futuristic object detection. All this is designed using Firebase ML kit which predominantly uses bitmap class for preprocessing, text-to-speech synthesizer and FirebasevisionObjectDetector, for joining machine learning capabilities into portable applications. Hence, the application stands out for its flexibility in dealing with official archives through voice. © 2024 IEEE.

关键词： Natural language processing systems

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Real-Time Human Pose Estimation Using Media-Pipe an Artificial Intelligence Applications in Health and Fitness 4

Real-Time Human Pose Estimation Using Media-Pipe an Artifici...

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4th international conference on Artificial Intelligence and signal processing

作者： Totlani, Ketan Dhavala, Shiva S. Vijayarao, S. Sandeep Kumar Challagundla, Yagnesh Roy, B. Zhuo, Eugenia R. Jaipur Natl Univ Sch Comp & Syst Sci Jaipur Rajasthan India Rashtreeya Vidyalaya Coll Engn Sch Informat Sci & Engn Bengaluru India RMK Engn Coll Dept Comp Sci & Engn Kavaraipettai Tamil Nadu India VIT AP Univ Sch Elect Engn Amaravati Andhra Pradesh India Univ Santo Tomas Manila Coll Informat & Comp Sci Manila Philippines

ISBN: (纸本)9798350350661;9798350350654

Significant progress has been achieved in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in recent years. Nevertheless, a significant gap continues to exist in the effective surveillance of human actions, activities, and the recognition of human movements or poses. Machines and computers are fundamentally incapable of comprehending human activities without user intervention. Consequently, we now depend on technology to record and archive human actions through live broadcast data, videos, or images. The field of pose estimation, which is a critical area of research with applications in fields such as Augmented Reality, Animation, Gaming, Healthcare, and Sports, is expanding due to the rapid advancement of computer vision technology. This project implements innovative methods to train models and determine poses, utilizing random inputs from internet images, videos, and live streaming. In particular, models that have been trained with artificial intelligence have been created for daily physical activities that necessitate intricate pose estimation. The initial step in the project is to utilize MediaPipe to estimate the poses of the human body. Subsequently, the angles between the body joints are calculated to create the desired models. Additionally, a web-based user interface (WEB-UI) was developed to enable access from any location. The methodology involves the extraction of data from critical body locations, the framing of models, and the utilization of the MediaPipe model due to its superior performance and features in comparison to other models. The objective of this method is to develop a web application for end-users, train AI models for daily physical activities, and establish an efficient system for single human pose estimation. This approach provides a cost-effective alternative to physical trainers and enables the monitoring of health and sports activities to be enhanced.

关键词： Human Pose Estimation Artificial Intelligence Machine Learning Computer vision Deep Learning MediaPipe Physical Activity Monitoring Web Application Development

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RCDPT: Radar-Camera Fusion Dense Prediction Transformer 48

RCDPT: Radar-Camera Fusion Dense Prediction Transformer

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Lo, Chen-Chou Vandewalle, Patrick Jan de Nayerlaan 5 Sint-Katelijne-Waver2860 Belgium

ISBN: (纸本)9781728163277

Recently, transformer networks have outperformed traditional deep neural networks in natural language processing and show a large potential in many computer vision tasks compared to convolutional backbones. In the original transformer, readout tokens are used as designated vectors for aggregating information from other tokens. However, the performance of using readout tokens in a vision transformer is limited. Therefore, we propose a novel fusion strategy to integrate radar data into a dense prediction transformer network by reassembling camera representations with radar representations. Instead of using readout tokens, radar representations contribute additional depth information to a monocular depth estimation model and improve performance. We further investigate different fusion approaches that are commonly used for integrating additional modality in a dense prediction transformer network. The experiments are conducted on the nuScenes dataset, which includes camera images, lidar, and radar data. The results show that our proposed method yields better performance than the commonly used fusion strategies and outperforms existing convolutional depth estimation models that fuse camera images and radar. © 2023 IEEE.

关键词： depth estimation multi-modality nuScenes radar vision transformer

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Proceedings - 2024 international conference on Computing, robotics and System Sciences, ICRSS 2024

Proceedings - 2024 International Conference on Computing, Ro...

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2024 international conference on Computing, robotics and System Sciences, ICRSS 2024

ISBN: (纸本)9798331511296

The proceedings contain 50 papers. The topics discussed include: Space-based measurement and control system: satellite communication channel optimization and network security synergy technology;research on collaborative decision making mechanism for helicopter rescue tasks based on intelligent computing;research on deep learning model for real-time data processing of brain-computer interface system;innovative applications of human-computer interaction technology in software development;research on the application of the bionic ant path planning algorithm in robot navigation;research on the human-machine collaborative optimization algorithm for intelligent traffic signal control systems;and design of precision planetary gear reducer for industrial robot joints optimized through the combination of genetic algorithm and ant colony algorithm.

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Neural Network Style Transfer of Defects from Concrete to Metal to Improve Monitoring Efficiency 26

Neural Network Style Transfer of Defects from Concrete to Me...

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26th international conference on Digital signal processing and its Applications, DSPA 2024

作者： Andriyanov, N.A. Dementiev, V.E. Fu, L. Financial University under the Government of the Russian Federation Moscow Russia Ulyanovsk State Technical University Ulyanovsk Russia College of Mechanical and Electronic Engineering Northwest A&f University Yangling Shaanxi China

ISBN: (纸本)9798350361728

The article is devoted to the study of dependence of the quality of defect detectors on metal products on the augmentation of the initial dataset. Special attention is paid to a non-standard method of augmentation - the method based on style transfer. For 2000 reference metal images, crack formation was generated from concrete images. The analysis showed that such an extension of the training sample leads to 2-3% accuracy improvement on the test sample. Various architectures have been explored. Converged dual-pass detectors, single-pass detectors and transformer detectors. All the architectures show an accuracy gain using the proposed augmentation. © 2024 IEEE.

关键词： Computer vision

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EFFICIENT PER-SHOT TRANSFORMER-BASED BITRATE LADDER PREDICTION FOR ADAPTIVE VIDEO STREAMING 30

EFFICIENT PER-SHOT TRANSFORMER-BASED BITRATE LADDER PREDICTI...

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30th IEEE international conference on Image processing (ICIP)

作者： Telili, Ahmed Hamidouche, Wassim Fezza, Sid Ahmed Morin, Luce Univ Rennes CNRS INSA Rennes IETRUMR 6164 Rennes France Technol Innovat Ins POB 9639 Abu Dhabi U Arab Emirates Natl Higher Sch Telecommun Oran Algeria ICT Oran Algeria

ISBN: (纸本)9781728198354

Recently, HTTP adaptive streaming (HAS) has become a standard approach for over-the-top (OTT)-based video streaming services due to its ability to provide smooth streaming. In HAS, stream representations are encoded to target a specific bitrate providing a wide range of operating bitrates known as the bitrate ladder. In the past, a fixed bitrate ladder approach for all videos has been widely used. However, such a method does not consider video content, which can vary considerably in motion, texture, and scene complexity. Moreover, building a per-title bitrate ladder based on an exhaustive encoding is quite expensive due to the large encoding parameter space. Thus, alternative solutions allowing accurate and efficient per-title bitrate ladder prediction are in great demand. On the other hand, self-attention-based architectures have achieved tremendous performance in large language models (LLMs) and particularly vision transformers (ViTs) in computer vision tasks. Therefore, this paper investigates ViT's capabilities in building an efficient bitrate ladder without performing any encoding process. We provide the first in-depth analysis of the prediction accuracy and the complexity overhead induced by the ViTs model in predicting the bitrate ladder on a large and diverse video dataset. The source code of the proposed solution and the dataset will be made publicly available.

关键词： Bitrate ladder video compression HEVC vision transformer adaptive video streaming

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