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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5168 条记录，以下是661-670 订阅

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A Household Garbage Classification and Collection Device Based on Machine vision and Deep Learning 4

A Household Garbage Classification and Collection Device Bas...

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4th international conference on Robotics and computer vision, ICRCV 2022

作者： Xie, Wenzhuo Wang, Xuehua Li, Shiping Xu, Wei Duan, Xianbao Wuhan Institute of Technology Materials Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Garbage classification is the key link of garbage recycling. Based on machine vision and lightweight convolutional neural network MobileNet V2, a garbage collection system for municipal solid waste classification and identification is developed in this paper. Firstly, the MobileNet V2 network model was reconstructed using Python language in the framework of TensorFlow2.0, and the transfer learning was carried out using the garbage sample made by our own. the results show that the validation accuracy of 99.24% on the validation dataset can be obtained when the input image size is 160×160. Secondly by using c # language a PC control software with functions of image acquisition, classification recognition and condition monitoring is developed, by which living garbage can be divided into recyclable waste, kitchen waste, hazardous waste and other waste four categories quickly and intelligently without or reduce human participation. the delivery accuracy of the device can be high as 98.9%, and the pitch time is 3-6 seconds. the system can be used as an intelligent home kit to achieve the purpose of garbage classification from the source. © 2022 IEEE.

关键词： computer vision

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Automatic Analysis of Architectural Floor Plans Based on Deep Learning and Morphology 4

Automatic Analysis of Architectural Floor Plans Based on Dee...

引用

4th international conference on Information Science, Electrical, and Automation Engineering, ISEAE 2022

作者： Shen, Jingyi Wang, Ying Liang, Tianlin Gong, Xiangyang Beijing University of Posts and Telecommunications School of Computer Science Beijing China

ISBN: (纸本)9781510655157

In the context of construction engineering, the classification and calculation of the amount of decoration has become the main problem. the structure of engineering drawings is complex and the drawing standards are not unified. this paper proposed a structure extraction algorithm to filter interference lines, extract wall structures and divide spatial regions through image morphology processing based on the image features of floor plan. Based on the target detection algorithm, the drawing scale detection and recognition are realized, and the spatial area calculation is completed through the coordinate system transformation. the results are evaluated by the evaluation standard of graph recognition performance and annotation verification set. the algorithm proposed in this paper is superior to other algorithms in spatial structure analysis, and can effectively analyze and calculate the spatial structure of floor plan in architectural engineering scenes. © 2022 SPIE.

关键词： Morphology

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Post-hoc XAI Method for Visual Question Answering (VQA) 4th

Post-hoc XAI Method for Visual Question Answering (VQA)

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Muddamsetty, Satya M. Schmidt, Alina B. Moeslund, thomas B. Aalborg University Rendsburggade 14 Aalborg9000 Denmark

ISBN: (纸本)9789819787043

Visual Question Answering (VQA) systems, while advancing in intelligence, still face challenges in handling complex queries. Understanding the behavior of VQA models is crucial, especially in assessing their reliability in identifying relevant image regions for accurate responses. Although post-hoc explainable artificial intelligence (XAI) methods have demonstrated success in simpler tasks like image classification, their efficacy in more complex tasks like VQA remains unexplored. Moreover, the lack of a standardized evaluation method for the explanation heatmaps in VQA models raises questions about the trustworthiness of XAI methods. this paper proposes extending SIDU-XAI to VQA applications. It assesses visual explanations using the VQA-HAT dataset, which includes human-attention ground-truth maps, and the CLEVR-XAI dataset, comprising synthetic 3D scenes with objective ground-truth masks. Additionally, user studies were conducted to assess the impact of our post-hoc visual explanations on users’ understanding of model decisions. Our experiments led to the conclusion that the proposed SIDU-VQA method surpasses state-of-the-art RISE-VQA methods in both quantitative and qualitative aspects across three experiments. However, post-hoc XAI methods fall short of entirely fulfilling the complex standards of human expectations, which highlights the critical need for continued research into XAI techniques appropriate for VQA applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image classification

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A Comparative Analysis of image Captioning Techniques

A Comparative Analysis of Image Captioning Techniques

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Emerging Technology (INCET), international conference for

作者： Diya theresa Sunil Seema Safar Abhijith Das Amijith M M Devika M Joshy Department of Computer Science And Engineering Rajagiri School of Engineering and Technology

image captioning is the task of generating a textual description that accurately represents the content of an image. this task involves combining computer vision techniques, such as object recognition and scene understanding, with natural language processing to produce a human-like description of an image. Over time, various models have been introduced to perform image captioning, all aiming to accurately describe the content of an image. these models have practical applications such as improving the accessibility of multimedia content, assisting individuals with visual impairments, medical image captioning, and enhancing image search and retrieval. this paper explores some of the models and studies their efficiency using different evaluation metrics.

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Remote Sensing image Captioning Using Hire-MLP

Remote Sensing Image Captioning Using Hire-MLP

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： Yichao Wei Lin Li Shengling Geng School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Academy of Plateau Science and Sustainability Qinghai Normal University Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

Remote sensing image captioning has been widely applied to traffic management, geographic research, etc. Although the neural network approach has been successfully improving the performance of the Remote sensing image captioning system, Remote sensing image captioning is still facing object identification challenges due to the small size of objects, uneven distribution, and high coupling with the surrounding image background. In this paper, we propose a novel remote sensing image captioning encoder-decoder model Hierarchical rearrangement-Multi-Layer Perceptron (HMLP) whose encoder adapts hierarchical rearrangement-multi-layer perceptron to improve the capability of objection recognition. Extensive experiments have been conducted to testify HMLP by three datasets RSCID, UCM-caption, and NWPU-caption. Results show that HMLP outperforms many image captioning systems in the evaluation metrics BLEU4, METEOR, ROUGE-L, and CIDEr.

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Egocentric View Hand Action recognition by Leveraging Hand Surface and Hand Grasp Type 4th

Egocentric View Hand Action Recognition by Leveraging Hand ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Seo, DongYoon Chi, Hyunggun Hong, Sunghee Koh, Byoung Soo Ramani, Karthik Kim, Sangpil Korea University 145 Anam-ro Seoul Korea Republic of Purdue University West LaffayetteIN United States Korea Electric Technology Institute Gyeonggi-do Seongnam-si13509 Korea Republic of DigiCAP Inc. Seoul Gangseo-gu Korea Republic of

ISBN: (纸本)9789819787012

We introduce a multi-stage framework that uses local geometry changes on a hand surface and focuses on learning interaction between a primary and assistive hand/object for hand action recognition in videos from a egocentric view RGB camera. Our method does not require 3D information of objects such as the 6D object pose which is difficult to annotate or the depth of the image requires additional a depth sensor for learning an objects’ behavior while it interacts with hands. Instead, the proposed method learns the changes within the surface of the hand, the hand type which is positively correlated with the hand action and the location of objects and hands in the 2D image space. the framework synthesizes the mean curvature of the primary hand mesh model to encode the hand surface geometry. Also, we introduce a feature pooling layer to handle diverse scenarios: having one hand, two hands, one hand with one object, and two hands with two objects. Our method outperforms the state-of-the-art hand action recognition methods that use 6D object poses of objects or a depth sensor. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image annotation

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SSPS 2022 - 2022 4th international Symposium on Signal processing Systems

SSPS 2022 - 2022 4th International Symposium on Signal Proce...

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4th international Symposium on Signal processing Systems, SSPS 2022

ISBN: (纸本)9781450396103

the proceedings contain 16 papers. the topics discussed include: dangerous behavior recognition based on pose estimation and action analysis;simultaneous recognition algorithm of human activity and phone position based on multi-sensor data fusion;bilateral pose transformer for human pose estimation;GBSAR moving target detection capability evaluation and refocus based detection algorithm;a method of dual-spectrum feature fusion for face recognition under non-ideal lighting conditions;suppress target height induced false alarm in CSAR moving target detection;threshold selection on circular histogram using Renyi entropy;hierarchical vision transformer with channel attention for RGB-D image segmentation;uni-dimensional autoencoder reinforced multilayer perceptron network for individual behavior detection;and a lightweight video summarization method considering the subjective transition degree for online educational screen content videos.

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AI Based Criminal Detection and recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

the recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. the system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Vessel Segmentation Approach with Deep Neural Network Model for Detection of Diabetic Retinopathy 4

Vessel Segmentation Approach with Deep Neural Network Model ...

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4th international conference on Recent Trends in computer Science and Technology, ICRTCST 2021

作者： Pahadiya, Prasham Tiwari, Prakhar Singh, Prashant Gupta, Surendra Shri G. S. Institute Of Technology And Science Electronics And Telecommunications Engineering Indore India Acropolis Institute Of Technology And Research Computer Science And Engineering Indore India Shri G. S. Institute Of Technology And Science Computer Science And Engineering Indore India

ISBN: (纸本)9781665466332

Diabetic Retinopathy is one reason of avoidable vision loss that frequently affects working people around the world. Blood vessel segmentation aids in the early identification of retinal fundus pictures, which is critical for diagnosing a variety of eye disorders. In this study, several U-net-based methods for identifying vessel segmentation pictures from the original retinal images are suggested. Pre-processing, U-net based model construction, model testing, and model assessment are the four phases in the suggested model's procedure. the original retinal images are sent via pre-processing, which uses image augmentation to enhance the amount of training data available. To train the model, augmented pictures are input into a U-net based model, which consists of encoder-convolution-decoder layers that create a U shape. On the DRIVE and STARE dataset, sensitivity, specificity, and accuracy are used to evaluate the model. State-of-the-art approaches are used to evaluate the proposed models. In terms of sensitivity, the model fared best. the model scored 94.3 percent on the DRIVE dataset and 90.6 percent on the STARE dataset, outperforming current approaches. © 2022 IEEE.

关键词： Deep neural networks

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A Super Feature Transform for Small-Size image Forgery Detection 4th

A Super Feature Transform for Small-Size Image Forgery Dete...

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4th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2021

作者： Greeshma, M.S. Bindu, V.R. School of Computer Sciences Mahatma Gandhi University Kerala Kottayam India

ISBN: (纸本)9783031070044

In this digital era, we have a wide variety of image editing software that is prone to create malicious alterations on images. Hence, the evaluation for authenticity of image contents and identification of malicious modifications is an open problem. In this work, an efficient small-size image forgery detection algorithm is presented based on Super Feature Transform - combining Super Resolution and Feature Transform. the approach enhances detection of small-size forgery by pre-processing the input image using super resolution algorithm. A robust feature transform is suggested to extract potential feature points from small-size patches with entanglement properties. Subsequently, feature matching and filtering is achieved by fuzzy threshold so that the false matches are filtered out. Also, the feature matching module employs a soft clustering to determine the matching points between identical and semi-identical feature points in different clusters. the experimental evaluations demonstrated that the proposed method outperforms existing techniques particularly when the forgery size is small and detects manifold duplicate forged regions in terms of TPR and FPR recognition rate. © 2022, Springer Nature Switzerland AG.

关键词： Optical resolving power

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