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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Computer-vision-Based Industrial Algorithm for Detecting Fruit and Vegetable Dimensions and Positioning 19th

Computer-Vision-Based Industrial Algorithm for Detecting Fr...

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19th International conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： Guevara, Cesar Rostan, Julen Rodriguez, Javier Gonzalez, Silvia Sedano, Javier C. López Bravo 70 Burgos09001 Spain

ISBN: (纸本)9783031750090

A computer-vision-based industrial algorithm is proposed in this study for the detection of the dimensions and the spatial positioning of fruit and vegetables on a conveyor belt for their movement to a packing machine with a robotic arm. The principal purpose of the algorithm is to identify the dimensions (length, width), and position (angle of inclination, and Cartesian coordinates) of the vegetable mesh netting without taking the product label into account. The proposed algorithm has four functions. A convolutive neuronal network model is applied for object detection, with which all objects are identified in the image while the product label is suppressed, so that recognition of the vegetable mesh netting and its dimensions is not impaired. Moreover, the Canny edge detector and the Border following algorithms are applied to perform image pre-processing and edge improvements, respectively, yielding optimal results with more clearly defined noise-free objects. The feature extraction function for dimensions yielded detection precision results of 99.60% for length, 99.00% for width, and 97.80% for inclination angle, showing optimal performance in the test phases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Improving Myanmar image Caption Generation Using NASNetLarge and Bi-directional LSTM

Improving Myanmar Image Caption Generation Using NASNetLarge...

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2023 IEEE conference on Computer applications, ICCA 2023

作者： Aung, San Pa Pa Pa, Win Pa Nwe, Tin Lay University of Computer Studies Natural Language Processing Lab Yangon Yangon Myanmar Institute for Infocomm Research Visual Intelligence Department Singapore Singapore

ISBN: (纸本)9781665435994

The main objective of this paper is to improve the automatic Myanmar captions by learning the contents of images using NASNetLarge and Bi-LSTM model. Describing the contents of an image is a complex task for machine without human intervention. Computer vision and Natural Language processing are widely used to tackle this problem. This paper proposed a deep learning-based Myanmar image captioning system which used a NASNetLarge feature extraction model of CNN as an encoder and a deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) as a decoder. For corpus construction, we created and annotated the Myanmar image captions corpus (consists of over 40k Myanmar sentences), which is based on Flickr8k dataset. Furthermore, two different types of segmentations such as word segmentation level and syllable segmentation level are studied in text preprocessing step. In this work, the proposed Bi-directional LSTM model is compared with LSTM, GRU as well as the baseline model. Experiments on the updated dataset is presented that all of our models using syllable segmentation give higher and comparable BLEU scores than word segmentation for Myanmar image captioning system. NASNetLarge with Bi-directional LSTM model using syllable segmentation approach achieved the highest BLEU-4 score 40.05% which is 12.5% better than word segmentation in this work and 15.67% BLEU-4 score better than our previous work. © 2023 IEEE.

关键词： Long short-term memory

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Efficient Real-Time Okra Stage Identification using YOLOV8 10

Efficient Real-Time Okra Stage Identification using YOLOV8

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10th IEEE International conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Kumar, Nikhilesh B. Ferbin, F. J. Sivapatham, Shoba Kar, Asutosh Krithiga, R. Vellore Inst Technol Sch Mech Engn Chennai Tamil Nadu India Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Natl Inst Technol Elect & Commun Engn Jalandhar Punjab India Vellore Inst Technol Sch Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9798350385939;9798350385922

In modern agriculture, crop growth monitoring is a crucial component, as it offers intuitive information about the health and growth of the plant, assisting farmers and other agricultural specialists. This systematic growth monitoring is necessary for crop health and agricultural productivity. We preferred the YOLOv8, which utilizes machine learning and offers efficient plant analysis in agriculture. This preferred method predicts bounding boxes and the probability of each possible class, allowing it to achieve exceptional detection speed without trading off accuracy. Pre-processing was done on the created, "Okra-dataset" to standardize the image to a fixed resolution and enhance our dataset's strength. We tested our work using different models: YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Our test revealed that YOLOv8x achieved the highest mean average precision (mAP) of 82.9%. The implementation of research indicates that YOLOv8x is a good tool for agricultural applications, which can be very helpful to farmers.

关键词： Crop growth monitoring computer vision YOLOv8

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Contribution Unveiling Cutting-Edge machine Learning Techniques for image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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MUWS'2023: The 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media 23

MUWS'2023: The 2nd International Workshop on Multimodal Huma...

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32nd ACM International conference on Information and Knowledge Management (CIKM)

作者： Cheema, Gullal S. Hakimov, Sherzod Kastner, Marc A. Garcia, Noa Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Kyoto Univ Kyoto Japan Osaka Univ Osaka Japan

ISBN: (纸本)9798400701245

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like Computer vision (CV), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop will be an interactive event and include keynotes by relevant experts, poster and demo sessions, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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CNN-based On-board Intelligent processing for Remote Sensing images Interpretation

CNN-based On-board Intelligent Processing for Remote Sensing...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Xiaojie Cui Yuanyuan Liu Xuehua Chen Gang Wang Beijing Institute of Remote Sensing Information Beijing China

Traditional remote sensing image processing is not able to provide timely information for near real-time applications due to the hysteresis of satellite-ground mutual communication and low processing efficiency. On-board intelligent processing is an important approach to improve the efficiency and intelligence of remote sensing satellites. This paper takes convolutional neural network (CNN) based on-board processing as the focus. Firstly, the basic workflow of CNN based on-board processing system is illustrated. Afterwards, the applications of lightweight CNN based on-board processing are thoroughly reviewed. The used CNN models are further analyzed to compare the advantages and disadvantages. Finally, current challenges are summarized and future works concerned with artificial intelligence are concluded.

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Xiao Jing, Haodong Chen, Hui Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi China

ISBN: (纸本)9798350368741

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings. © 2025 IEEE.

关键词： Compositional Zero-shot Learning Cross-Modal Fusion Fine-Grained Refinement Multimodal Models

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Traffic Monitoring and violation Detection Using Deep Learning 2nd

Traffic Monitoring and Violation Detection Using Deep Learni...

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2nd International conference on Big Data, machine Learning, and applications, BigDML 2021

作者： Sargar, Omkar Jain, Saharsh Chittupalli, Sravan Tatipamula, Aniket Department of Electronics Engineering Veermata Jijabai Technological Institute Mumbai India Department of Computer Science Veermata Jijabai Technological Institute Mumbai India AIRPIX Geoanalytics Navi Mumbai India

ISBN: (纸本)9789819934805

The traffic density on roads has been increasing rapidly for the past few decades, which has in turn been reflected in the increase in traffic violations and accidents. Official reports from various governments and private entities bolster the fact that, indeed, the current methods for traffic monitoring are inept to deal with the huge traffic density [1, 2]. These methods, which traditionally included the deployment of traffic police personnel at a select few junctions where the traffic density is high, ignore the majority of the other roads. Traffic monitoring systems that exploit image processing, computer vision and deep learning techniques thus come out to be a viable and optimal solution to monitor traffic and detect violations. These systems can easily be integrated with the architecture of law enforcement to penalize violators in real time. The proposed method—which utilizes YOLOv3 and SORT—is effective and accurate in detecting several violations like—over-speeding, wrong-way driving, signal jumping, driving without helmet and triple seat violation. It also helps to keep track of the count of vehicles, their types and also the number of axels for multi-axle vehicles, thus, asserting itself as a novel and indigenous solution to a widely recognized problem. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Quantum machine Learning for Computer vision: A Survey

Quantum Machine Learning for Computer Vision: A Survey

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International conference on machine Learning and applications (ICMLA)

作者： Md Majedul Islam Jing Selena He Department of Computer Science Kennesaw State University Marietta USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

This research delves into quantum machine learning (QML) in the context of computer vision analysis by exploring the progress made in quantum computing and its impact on machine learning applications such as managing datasets and improving large-scale data processing efficiency through QML techniques specialised for tasks like image segmentation and classification in computer vision projects, along with findings, from trials conducted using the EMNIST benchmark *** tests reached an accuracy level above 90% successfully categorising tasks, with precision. This study explores the uses of quantum machine learning (QML) in areas like identification medical scans and distant monitoring. It also delves into the existing constraints and hurdles linked to quantum computer technologies.

关键词： Surveys Computer vision Adaptation models Quantum computing Accuracy Reviews Computational modeling machine learning Remote sensing Biomedical imaging

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Bridging the machine-Human Gap in Blurred-image Classification via Entropy Maximisation

Bridging the Machine-Human Gap in Blurred-Image Classificati...

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International image processing, applications and Systems conference (IPAS)

作者： Emilio Sansano-Sansano Marina Martínez-García Javier Portilla INIT Universitat Jaume I Castellón de la Plana Spain IMAC Universitat Jaume I Castellón de la Plana Spain Instituto de Óptica CSIC Madrid Spain

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image.

关键词： Measurement image quality visualization Computer vision Artificial neural networks Entropy Kernel Optimization Information theory image classification

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