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Multi-class object detection system using hybrid convolutional neural network architecture

MULTIMEDIA TOOLS AND applications 2022年第22期81卷 31727-31751页

作者： Borade, Jay Laxman Lakshmi, Muddana A. GITAM Deemed Univ Hyderabad India GITAM Deemed Univ CSE Dept Hyderabad India

Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SvR (Support vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL vOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL vOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with re

关键词： image processing Object localization Deep learning Object recognition machine learning

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Food Nutrient Extraction Based on image Recognition and Entity Extraction 19

Food Nutrient Extraction Based on Image Recognition and Enti...

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19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

作者： Gao, Hanzhong Liu, Yanjun Li, Jingjuan Gao, Jianwei Columbian Coll Arts Sci Phillips Hall801 22nd St NW Washington DC 20052 USA Shandong Acad Agr Sci Shandong Key Lab Greenhouse Vegetable Biol Shandong BranchNatl Vegetable Improvement Ctr Inst VegetablesHuanghuai Reg Vegetable Sci StnM Jinan 250100 Peoples R China

ISBN: (纸本)9798350336672

Nutrition is an important aspect of public health, and in recent years, there has been increasing interest in the nutritional information of food. However, processing this information can be a challenging task due to the large amounts of data involved. machine learning (ML) has emerged as a useful tool to address this challenge. In this paper, we present a data resource that uses the FoodData Central (FDC) nutrient database to explore the combination of food images, nutritional information, and text with ML. We begin by providing an overview of machine learning and its applications in nutrition research, including the use of ML algorithms to identify food intake patterns, predict nutrient intakes, and evaluate dietary guidelines. We then describe the features and applications of Inception-v3, Inception-v4, and MobileNetv2 in ML, highlighting how these models can be used to extract nutritional information from food images. To further explore the potential of ML in nutrition research, we developed a quick search app that integrates images, text, and nutritional information. This app uses image recognition algorithms to identify food items in pictures, and text processing techniques to extract food information from text data. Users can simply take a picture of a food item and the app will provide the details of its nutritional content. This app can be used to facilitate the study of food and nutrition information and help promote healthier eating habits. In conclusion, the development of data resources and apps that use ML algorithms can be particularly helpful in processing large amounts of nutrition data and making it more accessible to the public. By harnessing the power of ML, we can advance our understanding of the relationship between diet and health, and ultimately work towards improving public health outcomes.

关键词： Nutrient Information machine Learning image Recognition Text Recognition

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Rosette Plant Centre Detection and Tracking using YOLO: An Efficient Deep Learning Approach 3

Rosette Plant Centre Detection and Tracking using YOLO: An E...

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3rd International Conference on Computing and machine Intelligence (ICMI)

作者： Akagic, Amila Saric, Rijad Buza, Emir Kecman, Stefani Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia Sci Instruments Australia SIA 2 Res Ave Melbourne Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： Plant Phenotyping Arabidopsis thaliana Computer vision image processing Deep Learning Neural Networks

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Research on verification framework of image processing IP core based on real-time reconfiguration 18

Research on verification framework of image processing IP co...

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Colloidal Nanoparticles for Biomedical applications XvIII 2023

作者： Mo, Wei Zhao, Lu Wen, Jianping Xi’an Xwzn Technology Co. Ltd. Shaanxi Science and Technology Holding Group Co. Ltd. Xi’an China Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9781510658950

The verification of IP core with image processing algorithm is important for SoC and FPGA application in the field of machine vision. This paper proposes a verification framework with general purpose, real-time performance and agility for IP core with image processing algorithm by using heterogeneous platform composed of ARM and FPGA. In the verification framework, the Gigabit Ethernet communication between PC and ARM is established. The FPGA is used to build the data bus to be compatible with multiple types of images, and combine with a partial reconfiguration to achieve fast iteration of IP cores of the algorithm to be verified. The validation framework is reusable for the algorithm IP core, and the deployment speed of the IP cores to be verified is 25 times faster than global reconfiguration. Compared with the existing FPGA verification technology, it has better reusability, shorter verification cycle, more targeted test stimulus, and faster deployment of IP cores to be verified. © 2023 SPIE.

关键词： image processing

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Movie Recommendation System Based on Emotion Detection Using machine Learning Techniques

Movie Recommendation System Based on Emotion Detection Using...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： vaishnavi, S.R. Sreelakshmi, S. Anu Prabha, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

The face is a critical perspective in predicting human feelings and moods. More frequently than not human senti-ments are extricated with the utilization of the camera. various applications are being made based on the location of human sentiments. A few applications of feeling revelation are trade notice suggestion, e-learning, mental clutter, sadness disclosure, criminal conduct discovery, etc. This paper presents a novel real-time emotion-based movie recommendation system that combines computer vision, deep learning, and image processing strategies. The system coordinating OpenCv near DeepFace for effective emotion examination utilizing webcam input, giving clients personalized movie recommendations based on their recognized enthusiastic states. The system commences by using OpenCv to capture real-time webcam feeds and utilizes a Haar-Cascade classifier for facial discovery. The recognized faces are analyzed for prevailing feelings utilizing the DeepFace library, empowering exact feeling distinguishing proof. Within the recommendation stage, the system joins content-based filtering by processing a movie dataset utilizing TF-IDF. Genres and plot keywords serve as features for building the TF-IDF matrix. Cosine similarities between the user's emotion vector and relevant movie genres are then calculated, coming about in a list of personalized movie rec-ommendations. Index Terms-FER, OpenCv, DeepFace, Haar-Cascade Algorithm, Content-Based Filtering, TF-IDF © 2024 IEEE.

关键词： Emotion recognition visualization Webcams Face recognition Motion pictures Real-time systems Libraries

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image processing using Cloud for Surveillance 5

Image Processing using Cloud for Surveillance

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5th International Conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Tiwari, Shivam Phukan, Aarhee vedhavathy, T.R. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis. © 2023 ACM.

关键词： Computer vision

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Optical Preprocessing for Low-Latency machine vision

Optical Preprocessing for Low-Latency Machine Vision

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作者： Muminov, Baurzhan University of California Riverside

学位级别：Ph.D., Doctor of Philosophy

In recent years there has been an increased interest towards edge computing, i.e., computing performed on distributed devices as opposed to centralized high-power hubs. Examples of edge computing would be the local image processing performed on Unmanned Autonomous vehicles (UAv's) or the specialized machine vision systems on drones. These edge computing applications require schemes that are efficient with power and memory and typically must operate real-time. Many state-of-the-art image processing solutions that employ advanced optimization and deep neural networks (NNs) achieve impressive benchmark results, but are computationally demanding and thus on many occasions, impractical. The additional requirement for a range of applications is noise robustness or the ability to work in (extreme) low-light conditions; reasonable quality image or accurate object classification may be critical when there is low light flux or when the environment is over-saturated with other signals. Here, we approach edge computing with a combination of optical preprocessing and shallow NN and we show that this hybrid approach greatly reduces the computational requirements. For low-SNR imaging, we develop a technique that reconstructs objects and scenes from their Fourier-plane images. The optical preprocessing is performed via encoded diffraction with optical vortex singularities. The optical vortex encoder achieves differentiation of the already-compressed Fourier-plane patterns and enables facile inverse inference of the original object scene. We demonstrate that our method is robust to noise. And for a simple NN architecture (one or two layers), leads to generalization, i.e., reconstruction of objects from classes that are greatly different from the ones the NN was trained on. Our research identifies strong potential for swift hybrid imaging systems with edge computing applications and highlights the valuable function of the vortex encoder for spectral differentiation.

关键词： Low-latency machine learning machine vision Noise robustness Topological optics vortices

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An AI pipeline for garment price projection using computer vision

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Neural Computing and applications 2024年第25期36卷 15631-15651页

作者： Rico Gómez, Rodrigo Lorentz, Joe Hartmann, Thomas Goknil, Arda Pal Singh, Inder Halaç, Tayfun Gökmen Boruzanlı Ekinci, Gülnaz DataThings 5 rue de l’industrie Luxembourg1811 Luxembourg SnT University of Luxembourg Campus Kirchberg Luxembourg1359 Luxembourg SINTEF Digital Oslo Norway Galaksiya Information Technologies Izmir Turkey Department of Mathematics Ege University Izmir Turkey

The fashion industry’s traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanced Artificial Intelligence (AI) techniques. These AI methods should analyze data from various sources, including social media and e-commerce, to predict future fashion trends and prices. In this paper, we propose, apply, and assess a data analytics approach, i.e., FashionXpert, employing several image processing and machine learning techniques in an AI pipeline for garment price prediction. It integrates various heterogeneous data sources (e.g., textual and image data from e-stores, brand websites, and social media) to obtain more consistent, accurate, and beneficial information. We evaluated its effectiveness with an industrial data set obtained by a fashion search tool from the electronic commerce sites of clothing brands. FashionXpert predicted garment prices with an average Mean Absolute Error (MAE) of 15.31 EUR on a data set that has a standard deviation of 72.99 EUR. © The Author(s) 2024.

关键词： image segmentation

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Greedy Ensemble Hyperspectral Anomaly Detection

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JOURNAL OF IMAGING 2024年第6期10卷 131-131页

作者： Hossain, Mazharul Younis, Mohammed Robinson, Aaron Wang, Lan Preza, Chrysanthe Univ Memphis Comp Sci Dept Memphis TN 38152 USA Univ Memphis Elect & Comp Engn Dept Memphis TN 38152 USA

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and

关键词： hyperspectral images anomaly detection machine learning stacking ensemble image processing remote sensing statistical methods for HSI unmanned aerial vehicles UAv unmixing near infrared NIR

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machine Learning Based Leaf Disease Diagnosis System

Machine Learning Based Leaf Disease Diagnosis System

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shalini, v. Baby Kumar, Bittu varma, P. varun Reddy, P. vinay Kumar Kumar, T. Tarun Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Krishnankoil India

ISBN: (纸本)9798331509828

This research work suggests developing a diagnostic tool by using the techniques of machine learning and computer vision for the identification of plant diseases based on leaf images. It incorporates various features such as spots, lesions, abnormal shapes, and signs of insect damage to identify potential health problems in the plants. The tool can use image-processing methods like contour analysis, colour space transformation, and morphological operations to detect and classify the diseases based on risk factors like shape and area of spots, along with the extent of damage on the leaf. The tool is designed to work with uploaded leaf images from Google Drive and then provide an extensive report on plant health indicators such as spots, shape, and insect damage. It's early enough to detect and consequently reduce crop losses. This detection system equally ensures more targeted application of pesticides. © 2025 IEEE.

关键词： Diagnosis

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