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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language processing 45th

Building Safe and Reliable AI Systems for Safety Critical Ta...

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45th European conference on Information Retrieval (ECIR)

作者： Ao, Shuang Open Univ Walton Hall Milton Keynes MK7 6AA Bucks England

ISBN: (纸本)9783031282409;9783031282416

Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for safety-critical tasks. One shared characteristic of these critical tasks is their risk sensitivity, where small mistakes can cause big consequences and even endanger life. There are several factors that could be guidelines for the successful deployment of AI systems in sensitive tasks: (i) failure detection and out-ofdistribution (OOD) detection;(ii) overfitting identification;(iii) uncertainty quantification for predictions;(iv) robustness to data perturbations. These factors are also challenges of current AI systems, which are major blocks for building safe and reliable AI. Specifically, the current AI algorithms are unable to identify common causes for failure detection. Furthermore, additional techniques are required to quantify the quality of predictions. All these contribute to inaccurate uncertainty quantification, which lowers trust in predictions. Hence obtaining accurate model uncertainty quantification and its further improvement are challenging. To address these issues, many techniques have been proposed, such as regularization methods and learning strategies. As vision and language are the most typical data type and have many open source benchmark datasets, this thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering. In this thesis, we aim to build a safeguard by further developing current techniques to ensure the accurate model uncertainty for safety-critical tasks.

关键词： Deep learning Model calibration Uncertainty

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Algorithm for detection and classification of small objects on a complicated background to automated robotic complex 10

Algorithm for detection and classification of small objects ...

引用

conference on Optical Metrology and Inspection for Industrial Applications X

作者： Semenishchev, Evgenii Urunov, Slavat Ilyukhin, Yuriy Siryakov, Aleksey Voronin, Viacheslav Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a multi-criteria image filtering block built on an objective function that minimizes the weighted average sum of the average square of the first-order finite difference, as well as the average square of the distance difference between the input implementation and the generated data;parallel separation of objects by analyzing local features, statistical analysis of histogram changes, building a mask of object detailing and frequency analysis;the formation of a feature mask and the search for similarity elements by analyzing the generated features. On the test data set, an example of determining small-sized objects on a complex background with their subsequent classification into class objects is presented. The data were obtained by a machine vision system installed on a robotic complex. Data on the required parameters of the formed machine vision systems are given, recommendations on the required parameters of the algorithms are presented.

关键词： image/signal smoothing edge detection preprocessing multicriteria method objects detection

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image processing-Based Methods to Improve the Robustness of Robotic Gripping 22

Image Processing-Based Methods to Improve the Robustness of ...

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IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics / 8th IEEE International conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)

作者： Takacs, Kristof Elek, Renata Nagyne Haidegger, Tamas Obuda Univ Antal Bejczy Ctr Intelligent Robot Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary Obuda Univ Univ Res & Innovat Ctr EKIK Budapest Hungary Austrian Ctr Med Innovat & Technol Wiener Neustadt Austria

ISBN: (纸本)9798350398823

image processing techniques are having a huge impact on most fields of robotics and industrial automation. Real-time methods are usually employed in complex automation tasks, assisting with decision making or directly guiding robots and machinery, while post-processing is usually used for retrospective assessment of systems and processes. While artificial intelligence-based image processing algorithms (relying usually on neural networks) are more common nowadays, "classical" image processing methods can also be used effectively for most modern applications. This paper focuses on optical flow-based image processing, proving its efficiency by presenting optical flow-based solutions for modern challenges in different fields of robotics, such as robot-assisted surgery and food processing. The application domain introduced in this paper is based on a smart robotic gripper designed to support automated robot cells in the meat industry. The gripper is capable of slip detection and secure gripping of soft, slippery tissues with the help of the implemented real-time algorithm.

关键词： image processing Optical flow Slip detection Robotic gripping

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Designing Resilient Autonomous systems with the Reflex Pattern

Designing Resilient Autonomous Systems with the Reflex Patte...

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Design, Automation and Test in Europe conference and Exhibition

作者： Julian Demicoli Sebastian Steinhorst Department of Computer Engineering Technical University of Munich Germany

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Autonomous systems face significant challenges due to fluctuating resources and unstable environments, where traditional redundancy strategies for resilience can be inefficient. We present the Reflex pattern, inspired by biological reflexes, promoting system resilience by dynamically adapting to changing resource conditions. By switching between complex and resource-efficient algorithms based on availability, the pattern optimizes efficient resource utilization without extensive redundancy, ensuring essential functionalities remain operational under constraints. To facilitate adoption, we introduce ReflexLang, a domain-specific language (DSL) enabling automated code generation for reflex-pattern-based systems. We validate the pattern's effectiveness in a drone image processing scenario, demonstrating its potential to enhance operational integrity and resilience.

关键词： Autonomous systems Software architecture image processing Heuristic algorithms Redundancy Europe Switches Resource management Faces Resilience

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Research on the Construction of image processing Model Based on Computer Vision Algorithm

Research on the Construction of Image Processing Model Based...

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Control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Wang Yi Economic Law School of Law Shandong University of Technology Zibo Shandong China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

This research focuses on constructing an efficient image processing model, which is rooted in computer vision algorithms, to ameliorate image distortion and optimize visual display systems. The article initially discusses the significance of image distortion correction and its processing flow, encompassing detailed steps ranging from image preprocessing, model construction, parameter estimation, to pixel coordinate transformation. Furthermore, the design approach of computer vision display systems is explored, covering the theory of light field reconstruction, software and hardware design essentials, ensuring high fidelity in image presentation. Through multiple experiments conducted on datasets of distorted images, the effectiveness of the model in correcting distortion and enhancing image quality is empirically demonstrated, with the experimental results strongly supporting the feasibility and superiority of the model. This research holds positive implications for advancements and practical applications in the field of image processing.

关键词： Computer vision Visualization Computational modeling image processing Display systems Distortion Solids

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Improving Efficiency of Spinal Cord image Segmentation Using Transfer Learning Inspired Mask Region-Based Augmented Convolutional Neural Network 1

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International conference on Data Analytics and Management, ICDAM 2023

作者： Garg, Sheetal Bhagyashree, S.R. Department of Electronics and Communication Engineering ATME College of Engineering Mysuru India

ISBN: (数字)9789819965441

ISBN: (纸本)9789819965434

Spinal cord magnetic resonance images (MRIs) consists of 7 levels of cervical vertebrae, 12 levels of thoracic vertebrae, 5 levels of lumbar vertebrae, one level each of sacrum and coccyx components. Segmentation of these components is essential for effective classification and post-processing analysis of spinal cord images. In order to perform this task, separate algorithms are needed for each of the components. Due to which, their segmentation performance is not uniform, which limits their integration capabilities. Moreover, performance for each type of segmentation has scalability issues, which must be improved via augmentation, aggregation, and machine learning for better clinical use. To resolve these issues, this text proposes design of a novel spinal cord image segmentation model using transfer learning inspired mask region-based augmented convolutional neural network (MRACNN). The proposed model utilizes initial weights from the pre-trained COCO mask RCNN model, and modifies them to incorporate spine, torso, and L1 to L5 spinal cord components. When compared to several state-of-the-art models, it is found that the suggested model has improved region of interest (RoI) extraction and an accuracy of 91% for segmenting these components. Moreover, the proposed model was evaluated on multiple datasets, and a consistent performance was observed. Furthermore, the model was fused with a XRAI-based convolutional neural network which assisted in further improving overall efficiency of segmentation. Fusion of XRAI CNN with MRACNN is capable of achieving segmentation accuracy of 94%, along with better RoI performance when compared with individual models. Delay requirement of the fused model is high, and requires large dataset for training & validation, thus, this text also recommends selective ensembling techniques for redundancy reduction, which assists in improving segmentation speed, while maintaining high segmentation quality. © 2024, The Author(s), under exclusive l

关键词： image enhancement

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Precision Fruit Harvesting: Stem Detection and Cutting Techniques using image processing for Robotic systems

Precision Fruit Harvesting: Stem Detection and Cutting Techn...

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Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： Madhura Rajesh Shankarpure Dipti D. Patil STE's Smt Kashibai Navale College of Engineering Savitribai Phule Pune University Pune Maharashtra India MKSSS's Cummins College of Engineering for Women Savitribai Phule Pune University Pune Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

To enhance robotic fruit harvesting systems, the proposed methodology uses image processing techniques to recognize orange stems. The method highlights only the stem of an orange fruit in an image by utilizing gray-scale conversion, contour-based filtering, Gaussian blur, and Canny edge recognition. The technique can be incorporated into agricultural robots to improve fruit harvesting accuracy. After detecting the fruit stem, it is crucial to ensure precise cutting of the fruit to prevent damage to both the fruit and the tree. The paper also assesses the performance of the method in terms of robustness in complicated situations and detection accuracy, demonstrating its potential for real-world agricultural applications.

关键词： Accuracy Machine learning algorithms Filtering image edge detection Heuristic algorithms Machine learning Gray-scale Robustness Informatics Mobile computing

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15th EAI International conference on Simulation Tools and Techniques, SIMUTools 2023

15th EAI International Conference on Simulation Tools and Te...

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15th EAI International conference on Simulation Tools and Techniques, SIMUTools 2023

ISBN: (纸本)9783031575228

The proceedings contain 23 papers. The special focus in this conference is on Simulation Tools and Techniques. The topics include: KNXsim: Simulator Tool for KNX Home Automation Training by Means of Group Addresses;Development of a 3D Visualization Interface for Virtualized UAVs;test-Driven Simulation of Robots Controlled by Enzymatic Numerical P systems Models;PySPN: An Extendable Python Library for Modeling & Simulation of Stochastic Petri Nets;replacing Sugarscape: A Comprehensive, Expansive, and Transparent Reimplementation;Generative AI with Modeling and Simulation of Activity and Flow-Based Diagrams;wildfire Risk Mapping Based on Multi-source Data and Machine Learning;an Intelligent Ranking Evaluation Method of Simulation Models Based on Graph Neural Network;simulation of Drinking Water Infrastructures Through Artificial Intelligence-Based Modelling for Sustainability Improvement;spatio-Temporal Speed Metrics for Traffic State Estimation on Complex Urban Roads;integrating Efficient Routes with Station Monitoring for Electric Vehicles in Urban Environments: Simulation and Analysis;comparing the Efficiency of Traffic Simulations Using Cellular Automata;multi-agent Simulation for Scheduling and Path Planning of Autonomous Intelligent Vehicles;ECG Pre-processing and Feature Extraction Tool for Intelligent Simulation systems;OTOVIRT: An image-Guided Workflow for Individualized Surgical Planning and Multiphysics Simulation in Cochlear Implant Patients;Adaptive Sharing of IoT Resources Through SDN-Based Microsegmentation of Services Using Mininet;UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels;trajectory-Aware Rate Adaptation for Flying Networks;rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning;RateRL: A Framework for Developing RL-Based Rate Adaptation algorithms in ns-3;on the Analysis of Computational Delays in Reinforcement Learning-Based Rate Adaptation algorithms.

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Comprehensive Analysis and Benchmarking of De-raining Techniques Through a Rain Model Approach

Comprehensive Analysis and Benchmarking of De-raining Techni...

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conference on Multimodal image Exploitation and Learning

作者： Badrieva, Shokhina Bernabel, Silvano Agaian, Sos GC CUNY Coll Staten Isl CSI Dept Comp Sci New York NY 10314 USA

ISBN: (纸本)9781510650770;9781510650763

Adverse weather conditions, such as rain, impact the visual quality of images and significantly impact the performance of vision systems for drone-based video surveillance and self-driving car applications. It is essential to develop algorithms that can automatically remove these artifacts and not degrade the rest of the image. Several methods have been proposed in literature and practice to address this problem. They mainly focus on specific rain models, such as droplets, streaks, mist, or a combination of these. Real-life rain images are largely randomized with diverse rain sizes, types, densities, and directions. Furthermore, rain impacts various image parts differently and is often randomly distributed. Most existing de-raining algorithms can't remove drops, streaks, and mist from images simultaneously. This paper addresses this issue by reviewing existing algorithms and datasets through a rain model lens. We present surveys and quantitative benchmarking of state-of-the-art intelligence algorithms based on the rain types they aim to remove. While other review papers exist on single image de-raining, our work looks at and outlines the different algorithms and datasets available for each specific rain model. Finally, the paper makes the following contributions: Select the most recent state of the art algorithms and show their performance for each rain type on our combination dataset called the Combination Rain Model Dataset Offers insights on the issues that still exist in the developing field of image de-raining and future steps in the field

关键词： De-rain rain removal deep learning algorithms digital image processing de-raining image applications rain model rain streak raindrop rain mist

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Enhancing Robotic systems for Revolutionizing Healthcare Using Markov Decision Processes 6th

Enhancing Robotic Systems for Revolutionizing Healthcare Us...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Dahamou, Ikram Daoui, Cherki Laboratory of Information Processing and Decision Support Faculty of Sciences and Techniques Sultan Moulay Slimane University Béni-Mellal Morocco

ISBN: (纸本)9783031530814

This work is part of a research project carried out during the COVID-19 pandemic, involving the design and realization of an autonomous mobile hospital robot. Many real-world robotic tasks suffer from the critical characteristics: Noisy sensing, imperfect control, and environment changes. The Markov decision process MDP and its variants provide a mathematically based framework for modeling and solving robot decision and control tasks under uncertainty. This paper presents a review of Markov Decision Processes (MDPs) and their variants in the Control of Robotic systems. We begin by introducing the basic concepts of MDPs and their algorithms for solving completely observable decision problems, including value iteration and policy iteration. We then discuss the challenges associated with partially observable decision problems POMDPs. We review various approaches for solving POMDPs, including belief-state planning and Monte Carlo tree search. Finally, we discuss the concept of POMDP augmentation, which involves incorporating additional information into the decision-making process to improve performance. We present several examples of POMDP augmentation techniques, including the use of deep neural networks and transfer learning. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Markov processes

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