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Gender Detection using Facial Features with Support Vector Machine

Gender Detection using Facial Features with Support Vector M...

引用

Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Jayaprada S. Hiremath Shantala S. Hiremath Sujith Kumar Manjunath S. Chincholi Shantakumar B. Patil Mrutyunjaya S. Hiremath Computer Science and Engineering Research Scholar at VTU Belgaum Karnataka India Specialist in Image Processing Tata Elxsi Limited Bangalore Karnataka India Principal Engineer (Artificial Intelligence / Machine learning) VVDN Technologies Pvt. Ltd. Bangalore Karnataka India Dept. of Image Processing eMath Technology Pvt. Ltd. Bangalore Karnataka India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9781665491129

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In image processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector Machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy.

关键词： Support vector machines Wireless communication Training Machine learning algorithms Face recognition Support vector machine classification Feature extraction

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image Super-Resolution Using T-Tetromino Pixels

Image Super-Resolution Using T-Tetromino Pixels

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Simon Grosche Andy Regensky Jürgen Seiler André Kaup Chair of Multimedia Communications and Signal Processing Friedrich-Alexander-Univeristät Erlangen-Nürnberg Erlangen Germany

For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a novel binning concept using tetromino-shaped pixels. It is embedded into the field of compressed sensing and the coherence is calculated to motivate the sensor layouts used. Next, we investigate the reconstruction quality using tetromino pixels for the first time in literature. Instead of using different types of tetrominoes as proposed elsewhere, we show that using a small repeating cell consisting of only four T-tetrominoes is sufficient. For reconstruction, we use a locally fully connected reconstruction (LFCR) network as well as two classical reconstruction methods from the field of compressed sensing. Using the LFCR network in combination with the proposed tetromino layout, we achieve superior image quality in terms of PSNR, SSIM, and visually compared to conventional single-image super-resolution using the very deep super-resolution (VDSR) network. For PSNR, a gain of up to +1.92 dB is achieved.

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Dataset Augmentation Strategies for visual Activity Recognition in Deep Neural Networks

Dataset Augmentation Strategies for Visual Activity Recognit...

引用

International conference on Computer and communications (ICCC)

作者： Awais Ahmed Nizamani Retrocausal Inc Seattle WA

Dataset augmentation involves synthetically increasing the size of a dataset by introducing appearance variations that help a machine learning model to generalize when trained over the dataset. Several augmentation strategies have been proposed and evaluated in the context of image recognition problems [7], however empirical studies on effectiveness of data augmentation in video recognition are few and far between. In this paper, we explore a number of dataset augmentation strategies and quanti-tatively assess their value on the task of visual activity recognition in a highly controlled manner. We extend a number of image augmentation strategies to the video domain, and also propose an entirely novel augmentation approach drawing inspiration from the idea of “Dropout” [20] in neural networks directly to the data domain and calling it “Blackout” augmentation. We thoroughly evaluate these augmentation strategies on the recently proposed dataset from Kumar et al. [15] which includes a complex manufacturing assembly activity controlling for variations in camera pose and human actors. Our detailed evaluation reveals interesting trends hitherto undiscussed in literature, and a major performance boost of 20.0 percentage points.

关键词： Deep learning visualization image recognition Neural networks Activity recognition Cameras Market research

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Traffic Sign Recognition using image processing for Autonomous Vehicles

Traffic Sign Recognition using Image Processing for Autonomo...

引用

ieee International conference on System, Computation, Automation and Networking (ICSCAN)

作者： P. Dharani Devi Nawfal. A Devi. A Subash. R Department of ECE IFET College of Engineering Villupuram India Department of CSE Puducherry Technological University

Vehicles are regarded as the primary form of transportation in many nations. People prefer to go by vehicle since it gets them where they're going faster. The majority of driving mistakes on the road result in traffic accidents. Drivers' awareness may be affected by a variety of factors, including visual complexity, the surroundings, and inadequate driving instruction. The traffic signs were kept to alert the driver about the condition of the road, speed limit, speed breakers, restricted zones like schools and hospitals, divisions, turnings and intimate them to control the speed of the vehicle accordingly. For autonomous vehicles, this information is much more essential since there is no manual assistance. The traffic signs should be identified by the autonomous vehicle and necessary control action should be taken in order to avoid accidents and violations of traffic rules. The proposed system suggested a model that can read the traffic sign boards and convert them into instructions for autonomous vehicle by using image processing, object identification and recognition techniques. A deep learning model is used for classification of the captured image.

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FruitMMBench: A Multi-modal Benchmark for Fruit Quality Assessment

FruitMMBench: A Multi-modal Benchmark for Fruit Quality Asse...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiawei Chen Gong Huang Liu Liu Zhenbo Xu Qinghong Yang Beihang University Beijing China Beijing Univ. of Posts and Telecomm. Beijing China ShiFang Technology Inc. Hangzhou China Hangzhou International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapid advancement of Large Vision-Language Models (LVLMs) has brought notable improvements in tasks like visual recognition and multi-modal understanding, demonstrating significant potential in real-world applications. However, their performances on issues related to daily life such as fruit quality assessment have never been explored due to the lack of a benchmark for fruit quality assessment. To bridge this gap, we introduce FruitMMBench, a comprehensive multi-modal benchmark designed to assess the ability of fruit quality assessment. A comprehensive metric is carefully designed by jointly considering many aspects. The resulting large-scale FruitMMBench contains 5,465 collected fruit images and dedicated quality labels including the following aspects: fruit classification, quantity recognition, maturity, surface condition, quality status, and edibility recommendation. By extensive evaluations on FruitMMBench, we find popular LVLMs struggle to provide reliable results in fruit quality assessment and their performances vary greatly. The test results reveal that the ability of current LVLMs to analyze fruit quality in real-world scenarios is still weak and needs to be paid attention to and enhanced in the future. All evaluation codes and dataset will be publicly accessible shortly.

关键词： Measurement Bridges visualization image recognition Codes Benchmark testing Signal processing Quality assessment Reliability Speech processing

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Construction and Application of the Knowledge Graph in Endangered Plants 22

Construction and Application of the Knowledge Graph in Endan...

引用

22nd ieee/ACIS International conference on Computer and Information Science, ICIS 2022

作者： Wei, Haochuan Zhang, Qianchi Gao, Weixuan Meng, Xianghao Beijing Forestry University School of Information Science and Technology Beijing China Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Engineering Beijing China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

The current network information of endangered plants is scattered, making acquiring and reusing relevant knowledge difficult. The endangered plants' knowledge graph ePlantKG constructed in this paper can form a visual semantic information network. The data sources in this paper include China Rare and Endangered Plant Information Network, Baidu Encyclopedia, Wikipedia, and Kuaiming Encyclopedia. Firstly, we use a Python crawler to obtain network data and preprocess them. Then we use the obtained structured data combined with previously constructed plant ontology to define and build new ontologies. Next, through the rule-based method, we extract triples from semi-structured and unstructured data, fuse knowledge between heterogeneous data, and store them in the Neo4j database to form ePlantKG. Finally, we design and build a knowledge service platform to illustrate knowledge graphs and intelligent question answering. The intelligent question answering algorithm extracts features from the user's input text with TF-IDF and classifies questions with Naive Bayes. After realizing the similarity matching between entities and relations, we retrieve answers with the returned Cypher statement. The ePlantKG records 1926 species of endangered plants and 37860 species of common plants, and the image filling rate of endangered plants is more than 99 %. The platform implements several functions, e.g., graph display, entity recognition, and intelligent question answering. This paper realizes the information sharing and reuses on endangered plants, providing a method reference for applying knowledge graph in forestry intelligent question answering system and forestry big data analysis. © 2022 ieee.

关键词： Knowledge graph

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An Effective Automatic Recognition Approach of Chinese Herbal Medicine by Micro image processing

An Effective Automatic Recognition Approach of Chinese Herba...

引用

ieee International Symposium on Parallel and Distributed processing with Applications and ieee International conference on Ubiquitous Computing and communications (ISPA/IUCC)

作者： Guangyao Pang Zhenming Yu Honglin Pan Chengwen Xie Ping Gong Xiaoying Zhu Zizhen Peng Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： Deep learning image recognition Costs Microscopy Transfer learning Ubiquitous computing Raw materials

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SAGNet: A Dual-Branch Segmentation Model Based on Shape Priors and Boundary processing

SAGNet: A Dual-Branch Segmentation Model Based on Shape Prio...

引用

Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee International conference on

作者： Yechuan Duan Guangyu Hou Runxin Niu Hongzhi Xie University of Science and Technology of China Hefei Institutes of Physical Science Chinese Academy of Sciences Chinese Academy of Sciences Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Chinese Academy of Sciences Hefei China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

In recent years, remote sensing analysis has garnered significant attention in visual analysis applications, particularly in segmenting and recognizing remote sensing images. However, existing studies relying on deep convolutional neural network-based segmentation algorithms often fail to adequately address target boundary processing and lack attention to the distinctive shape characteristics of objects. To alleviate these issues, this paper introduces the SAGNet. SAGNet consists of two continuously integrating branches designed to maintain high resolution while enhancing the extraction of contextual features of the network. To integrate features from both branches, we develop a DISPP module. In addition, we propose an EdgeEnhanceRend module to refine object boundaries for enhanced delineation. Considering the shape characteristics of AGs, we incorporate a SA module within the dual-branch structure to integrate segmentation tasks with shape prior learning. This integration allows them to share and interact with potential high-level semantics and shape priors to achieve better segmentation results. Experimental results show that SAGNet significantly outperforms other methods in precise identification of AGs.

关键词： image segmentation visualization image resolution Shape Semantics Greenhouses Lightweight structures Feature extraction Satellite images Remote sensing

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A Traffic-Aware Trust Model Based on Edge Computing for Underwater Wireless Sensor Networks

A Traffic-Aware Trust Model Based on Edge Computing for Unde...

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ieee International conference on communications (ICC)

作者： Rongxin Zhu Azzedine Boukerche Pengcheng Li Qiuling Yang School of Computer Science Hainan University Haikou China Paradise Laboratory EECS University of Ottawa Ottawa Canada Innovation Platform for Academicians of Hainan Haikou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The burgeoning deployment of Underwater Acoustic Sensor Networks (UASNs) for maritime applications highlights the critical need for reliable trust models to defend against internal security threats. Existing trust models are often inadequate due to high packet error rates inherent in underwater communication and a lack of accounting for nodes' traffic behavior. Additionally, conventional UASN architectures suffer from significant latency in gathering and processing trust evidence, which delays the identification of adversarial nodes. Addressing these limitations, this paper proposes the Traffic-Aware and Edge Computing-Enabled Trust Model (TECTM), a solution expressly conceived for UASNs. TECTM integrates environmental models to assess the acoustic environment's impact on communication and employs network traffic analysis as a trustworthy metric for identifying attack patterns. Autonomous Underwater Vehicles (AUVs) serve as edge computing nodes, leveraging a machine learning algorithm to enhance trust assessments within node clusters. Moreover, TECTM introduces a refined trust update mechanism, designed to be responsive to the dynamic underwater environment and complex attack behaviors. Through comparative simulations, TECTM demonstrates enhanced accuracy in the detection of malicious nodes, outperforming other methods.

关键词： Measurement Wireless sensor networks Underwater communication Accuracy Computational modeling image edge detection Telecommunication traffic

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A method of automatic ISAR image quality evaluation

A method of automatic ISAR Image quality evaluation

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2021 CIE International conference on Radar, Radar 2021

作者： Li, Bingning Ma, Yan Wang, Ruohui Xu, Tao Chen, Falong China xi'An Satellite Control Center Hangzhou China Beijing Institute of Tracking Telemenntry & Telecommunication Beijing China Laboratory of Pinghu Pinghu China China Academy for Network & Communications of CETC Beijing China

ISBN: (纸本)9781665498142

It is of great significance to select high quality ISAR imaging results from massive data processing results to effectively obtain target information for target recognition. In this paper, an automatic quality evaluation method for ISAR images is proposed, which can automatically filtering and excluding low-quality images. Firstly, the quality evaluation standard image database was established by expert evaluation and objective model evaluation. Then, the features of ISAR images are extracted by convolutional neural network. Finally, SVM classifier is used to evaluate the image quality. Experimental results show that the proposed method is more reasonable, accurate and efficient than the objective image quality evaluation method without reference. © 2021 ieee.

关键词： CNN-SVM ISAR lmage quality evaluation

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