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

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image Captioning of Satellite images Using Transfer Learning and LSTM Blending 6th

Image Captioning of Satellite Images Using Transfer Learning...

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6th International conference on Information Systems and Management Science, ISMS 2023

作者： Sharma, Rohit Shree, Aishwary katoch, Aayush Verma, Sourabh Singh Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9783031707889

In recent years, the field of image captioning has gained substantial attention, posing a complex challenge that necessitates the integration of computer vision (CV), natural language processing (NLP), and machine learning techniques. Our study introduces an advanced model designed to generate natural language descriptions for satellite images. We harnessed the capabilities of convolutional neural networks (CNNs) and utilized state-of-the-art architectures such as VGG-19, ResNet-50, DenseNet-201, and EfficientNet B7 as encoders to extract intricate features from satellite images. These encoded features were seamlessly incorporated into a Long Short-Term Memory (LSTM) network, which is a type of recurrent neural network (RNN), serving as a robust decoder. To assess the effectiveness of our approach, we conducted extensive experiments using two datasets: the Sydney Dataset and the RSIC Dataset, both widely recognized benchmarks in the field of image captioning. The comparative analysis results obtained from our evaluations not only show promise but also underscore the competitiveness of our model. By achieving significant milestones in satellite image captioning, this research not only contributes to the expanding body of knowledge in this domain but also opens avenues for real-world applications, spanning from environmental monitoring to urban planning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Satellite imagery

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FHAM: FPGA-based High-Efficiency Approximate Multipliers via LUT Encoding 40

FHAM: FPGA-based High-Efficiency Approximate Multipliers via...

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IEEE 40th International conference on Computer Design (ICCD)

作者： Yao, Shangshang Zhang, Liang Natl Univ Def Technol Changsha Peoples R China

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

Approximate computing as a promising technique is widely used in a variety of error tolerance applications, such as computer vision, machine learning and image processing. Since multiplication is one of the basic arithmetic operations used extensively among these applications which creates a great potential to reduce the circuit's area and power consumption by exploring approximate multipliers. In this paper, we present FHAM, a novel methodology for FPGA-based approximate softcore multiplier architecture via modifying INIT parameter values in LUT primitives. Our proposed approximate multipliers can achieve up to 24.4%, 52.9%, 56.4% improvement in delay, area and power over Xilinx soft multiplier IP core, respectively. We deployed the proposed approximate multiplier designs in the application of image blending, the results demonstrate that proposed multiplier design has a better accuracy-hardware trade-off than other designs.

关键词： Error-Tolerant computing approximate multipliers image processing

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Software to Assist visually Impaired People During the Craps Game Using machine Learning on Python Platform 2nd

Software to Assist Visually Impaired People During the Craps...

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2nd International conference on Smart Technologies, Systems and applications (SmartTech-IC)

作者： Hernandez Diaz, Nicolas Penaloza, Yersica C. Yuliana Rios, Y. Magre Colorado, Luz A. Univ Tecnol Bolivar Parque Ind & Tecnol Carlos Velez Pombo Cartagena De Indias Colombia Univ Pamplona Km 1 Via Bucaramanga Pamplona Colombia

ISBN: (纸本)9783030991708;9783030991692

Pattern recognition is a prominent area of research in computer vision, where different methods have been proposed in the last 50 years. This work presents the development of a Python API to identify the result of two six-sided dice used in the game called "Craps" as a no-controlled environment to help visually impaired people. The software is structured in four stages. The first one is capturing images through a device with a digital camera connected to the web via IP address. The second stage corresponds to the captured image processing;it is necessary to establish a standard image size and resize and equalize the digitized image. The third stage seeks to segment the object of study by artificial vision techniques to identify the result of the dice after being thrown. Finally, the fourth stage is to interpret the result and play it through a speaker. The expected possible result is a system that integrates the four stages mentioned above through an intuitive and accessible low-cost Python API, mainly aimed at visually impaired people.

关键词： Craps game visually impaired people Non controlled environment Python API Artificial vision techniques image processing

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ComAI: Enabling Lightweight, Collaborative Intelligence by Retrofitting vision DNNs 41

ComAI: Enabling Lightweight, Collaborative Intelligence by R...

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41st IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Jayarajah, Kasthuri Wanniarachchige, Dhanuja Abdelzaher, Tarek Misra, Archan Univ Maryland Baltimore Cty Baltimore MD 21228 USA Singapore Management Univ Singapore Singapore Univ Illinois Champaign IL USA

ISBN: (纸本)9781665458221

While Deep Neural Network (DNN) models have transformed machine vision capabilities, their extremely high computational complexity and model sizes present a formidable deployment roadblock for AIoT applications. We show that the complexity-vs-accuracy-vs-communication tradeoffs for such DNN models can be significantly addressed via a novel, lightweight form of "collaborative machine intelligence" that requires only runtime changes to the inference process. In our proposed approach, called ComAI, the DNN pipelines of different vision sensors share intermediate processing state with one another, effectively providing hints about objects located within their mutually-overlapping Field-of-views (FoVs). CoMAI uses two novel techniques: (a) a secondary shallow ML model that uses features from early layers of a peer DNN to predict object confidence values in the image, and (b) a pipelined sharing of such confidence values, by collaborators, that is then used to bias a reference DNN's outputs. We demonstrate that CoMAI (a) can boost accuracy (recall) of DNN inference by 20-50%, (b) works across heterogeneous DNN models and deployments, and (c) incurs negligible processing, bandwidth and processing overheads compared to non-collaborative baselines.

关键词： Deep learning Runtime Computational modeling machine vision conferences Pipelines Neural networks

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Domain-Specific Adaptation for Enhanced Gait Recognition in Practical Scenarios 24

Domain-Specific Adaptation for Enhanced Gait Recognition in ...

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6th International conference on image, video and Signal processing, IVSP 2024

作者： Jaiswal, Nitish Huan, vi Duc Limanta, Felix Shinoda, Koichi Wakasa, Masahiro Asilla Inc. Japan Asilla Inc. Viet Nam Dept. Of Computer Science Tokyo Institute Of Technology Japan

ISBN: (纸本)9798400716829

Gait recognition is a burgeoning field within biometric recognition which utilizes computer vision technology to extract silhouette images or body skeletons to identify users by leveraging the unique walking patterns of individuals. However, with its huge potential for user identification in diverse settings especially in security and surveillance applications, it faces challenges in transitioning from controlled datasets to real-world applications. In the regime of silhouette-based models, the most challenging covariate is associated with varying viewing angles, which has often been a bottleneck to achieving optimal accuracy for practical application in a real-world situation. Addressing this challenge, this paper introduces a novel domain adaptation technique tailored for gait recognition for practical applications, capitalising on expansive dataset pretraining and precise fine-tuning on targeted, smaller datasets pertaining to specific camera views. Our deep dive reveals that models adopting this adaptive training approach, especially when fine-tuned with viewing angles mirroring the test domain, witness a significant boost in pure cross-domain performance. Moreover, in a stride towards practical gait recognition, we present Asilla-Office - a non-synthetic dataset captured in an indoor office simulating real walking patterns of people in a real application environment. With its roots in real-world challenges, Asilla-Office is poised to be an initial benchmark, promoting research reflecting genuine application needs. In-depth experiments show that our domain-adapted fine-tuning approach trumps traditional single-staged training, marking a notable leap of more than 11% in Rank-1 accuracy on the new Asilla-Office dataset. In the spirit of fostering community-driven progress, the Asilla-Office dataset will be made publicly available. © 2024 ACM.

关键词： Benchmarking

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Enhancing Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction 2

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Yogaraja, C.A. Priyanka, C. Priyadharshini, S.P. Babu, J. Jagan Rajagopal, S. Poorani, S. Ramco Institute of Technology Rajapalayam India School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus India St. Joseph's College of Engineering OMR Chennai119 India Department of ECE R.M.D Engineering College Kavaraipettai India Department of IT National Engineering College Kovilppati India Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350383867

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species. © 2024 IEEE.

关键词： Convolutional neural networks

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Hand Gesture-Based AI System for Accessing Windows applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

引用

4th International conference on image processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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2024 IEEE International conference on Signal processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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A machine vision Measurement Method for Large Plates Based on Reference Point Assistance 5

A Machine Vision Measurement Method for Large Plates Based o...

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5th International conference on Electronics and Electrical Engineering Technology (EEET)

作者： Ji, Tianyi Zhao, Zhiwei Zhao, Ning Southeast Univ Coll Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350320428

In this work, a coordinate positioning method based on machine vision with reference point assistance is proposed to apply to the size measurement for large rectangular plate parts. This method first obtains the coordinate information of the reference point through circular detection, and uses the line detection and mean-shift clustering algorithm to fit the plate vertex information. Then the pixel coordinate relationship between the reference point and the plate vertex is used to solve the world coordinate of the plate vertex. Finally, the world coordinate of the plate vertex is used to calculate the length and width dimensions of the plate. Instead of other methods which unify coordinate system by moving cameras, this method uses reference point assistance system on measuring plate to unify the coordinate system, simplifying the difficulty of the unification of coordinate system and ensuring accuracy. Effectiveness of such method is verified by measuring rectangular plates with scales from centimeters to meters. The results showed that the measurement error could be controlled within 5 millimeters. In practical applications, the measurement accuracy has been much higher than the processing accuracy during the plate manufacturing, meeting the needs of size measurement.

关键词： reference points size measurement machine vision image processing

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Picture processing Optimization Technology Based on Mask R-CNN Algorithm 3

Picture Processing Optimization Technology Based on Mask R-C...

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3rd International conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Tan, Guihua Liu, Yiran College of Information Engineering Loudi Xiaoxiang Vocational College Hunan Loudi417000 China

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. © 2023 The Authors. Published by Elsevier B.V.

关键词： image retrieval

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