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A Performance Evaluation of Loss Functions for Deep Face Recognition 7th

A Performance Evaluation of Loss Functions for Deep Face Rec...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Srivastava, Yash Murali, Vaishnav Dubey, Shiv Ram Computer Vision Group Indian Institute of Information Technology Chittor Sri CityA.P. India

ISBN: (纸本)9789811586965

Face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of computer vision including face recognition. the loss function is used on the top of CNN to judge the goodness of any network. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. the experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Real-Time Puddle Detection Using Convolutional Neural Networks with Unmanned Aerial Vehicles

Real-Time Puddle Detection Using Convolutional Neural Networ...

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International conference on computer Science and Engineering (UBMK)

作者： Mehmet Bilge Han Taş Muhammed Coşkun Irmak Sedat Turan Abdulsamet Haşıloğlu Computer Engineering Erzincan Binali Yıldırim University Erzincan Turkey Computer Engineering Van Yüzüncü Yıl University Van Turkey Electronics and Automation Erzincan Binali Yıldırım University Erzincan Turkey Computer Engineering Atatürk University Erzurum Turkey

the study was carried out in order to enable systems with weak processing power and motion to detect objects using cloud services. In addition, the dataset is expanded by continuous labeling to create big data. In the study, it is aimed to detect objects using cloud-based deep learning methods with an unmanned aerial vehicle (UAV). In the study, training processes were carried out with Google Colaboratory, a cloud service provider. the training processes are a YOLO-based system, and a convolutional neural network was created by revising the parameters in line with the needs. the convolutional neural network model provides communication between neurons in the convolutional layers by bringing the image data to the desired pixel ranges. Unlabeled pictures are included in the training by being tagged. In this way, it is possible to continuously enlarge the data pool. Since the microcomputers used in UAVs are insufficient for these processes, a cloud-based training model has been created. As a result of the study, cloud-based deep learning models work as desired. It is possible to show the accuracy of the model with the low losses seen in the loss functions and the mAP value. Graphic cards with high processing power are needed to provide training. It is essential to use powerful graphics cards when working on image data. Cost reduced by using cloud services. the training was accelerated and high-rate object detections were made. YOLOv5x was used in the study. It is preferred because of its fast training and high frame rate. Recall 80% Precision 93% mAP 82.6% values were taken.

关键词： Training graphics Deep learning Neurons Object detection Microcomputers Unmanned aerial vehicles

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Analysing Facial Features Using CNNs and computer vision 6th

Analysing Facial Features Using CNNs and Computer Vision

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6th International conference on Modelling and Development of Intelligent Systems (MDIS)

作者： Borza, Diana Itu, Razvan Danescu, Radu Barbantan, Ioana Tech Univ Cluj Napoca Memorandumului St 28 Cluj Napoca 400114 Romania Tapptitude 31-33 Gheorghe Doja St Cluj Napoca 400068 Romania

ISBN: (纸本)9783030392376;9783030392369

this paper presents an automatic facial analysis system which is able to perform gender detection, hair segmentation and geometry detection, color attributes extraction (hair, skin, eyebrows, eyes and lips), accessories (eyeglasses) analysis from facial images. For the more complex tasks (gender detection, hair segmentation, eyeglasses detection) we used state of the art convolutional neural networks, and for the other tasks we used classical image processing algorithms based on geometry and appearance models. When data was available, the proposed system was evaluated on public datasets. An acceptance study was also performed to assess the performance on the system in real life scenarios.

关键词： Facial attributes analysis Gender detection Hair segmentation Convolutional neural networks Color analysis

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Unsupervised Domain Adaptation for Remote Sensing images Using Metric Learning and Correlation Alignment 7th

Unsupervised Domain Adaptation for Remote Sensing Images Usi...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Mahapatra, Aniruddha Banerjee, Biplab Indian Institute of Technology Roorkee India Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789811586965

We address the problem of domain adaptation (DA) in the context of remote sensing (RS) image classification in this paper. By definition, the problem of unsupervised DA aims at classifying samples from a target domain which is strictly devoid of any label information while assuming that enough training data are available from a related yet non-identical (in terms of data distributions) source domain. A number of existing approaches in this regard are focused towards matching the underlying distributions of the data from both the domains in a shared latent space without explicitly considering: i) the discriminativeness of the embedding space, ii) the usefulness of a manifold distance is pulling the domains towards each other over the standard Euclidean measures. However, we argue the importance of both the aspects in learning the latent space, particularly for fine-grained classes. Our model jointly optimizes both the terms in an end-to-end fashion and the learned latent space is found to properly align the classes with high precision. Experimental results obtained on a hyper-spectral and a multi-spectral dataset confirm the superior performance of the approach over a number of techniques from the literature. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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Messaging and Video Calling Application for Specially Abled people using Hand Gesture Recognition

Messaging and Video Calling Application for Specially Abled ...

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International conference for Convergence of Technology (I2CT)

作者： Rachana R. Chhajed Komal P. Parmar Manvi D. Pandya Neha G. Jaju Pune Institute of Computer Technology Pune Maharashtra India

ISBN: (纸本)9781728188768;9781728188775

According to the World Health Organization (WHO) over 5% of the world's population have impaired hearing and speaking loss. It is very difficult for people with disabilities to communicate. this paper proposes a system to overcome these barriers and allow everyone to interact with each other irrespective of their disabilities and facilitate everyone to communicate with each other through messaging and video calling irrespective of their disabilities. this paper proposes a vision based application which can be used for the communication of such people using text and video calling. For better accuracy various object detection and image classification algorithms are implemented. the application uses indian Sign Language as the dataset.

关键词： Databases Assistive technology Sociology Gesture recognition Speech recognition Object detection Organizations

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Single ship target detection based on the concept of edge computing

Single ship target detection based on the concept of edge co...

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International conference on Transportation Information and Safety (ICTIS)

作者： Ning Wang Feng Ma School of computer technology Wuhan University of Technology Wuhan P.R.China Intelligent Transportation Systems Research Center Wuhan University of Technology National Engineering Research Center for Water Transport Safety) Wuhan P.R.China

ISBN: (纸本)9781665497145

Target detection is an important research direction in the subject of computer vision, and it has a wide range of applications. Inland river ship target detection is of great significance to inland river environment detection, inland river ship obstacle avoidance, and other fields. However, as the requirements for the intelligence of ships become higher and higher, there will be more and more equipment on ships. these devices will rely on the computing power of the terminal computer to complete data processing and other related tasks. this will lead to problems such as excessive load on the terminal computer equipment and tight computing power. therefore, this paper introduces the concept of edge computing on the basis of traditional target detection methods. We use edge devices with computing power to complete single target detection of moving ships. this will alleviate problems such as tight computing power of terminal computer equipment to a certain extent. Since the edge device is close to the edge side, the data transmission delay problem can also be optimized, which is of great significance to the development of smart ships.

关键词： image edge detection Transportation Object detection Data processing Rivers Safety Delays

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PoshakNet: Framework for Matching Dresses from Real-Life Photos Using GAN and Siamese Network 7th

PoshakNet: Framework for Matching Dresses from Real-Life Pho...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Khaund, Abhigyan thapar, Daksh Nigam, Aditya Indian Institute of Technology Mandi India

ISBN: (纸本)9789811586965

Online garment shopping has gained many customers in recent years. Describing a dress using keywords does not always yield the proper results, which in turn leads to dissatisfaction of customers. A visual search based system will be enormously beneficent to the industry. Hence, we propose a framework that can retrieve similar clothes that can be found in an image. the first task is to extract the garment from the input image (street photo). there are various challenges for that, including pose, illumination, and background clutter. We use a Generative Adversarial Network for the task of retrieving the garment that the person in the image was wearing. It has been shown that GAN can retrieve the garment very efficiently despite the challenges of street photos. Finally, a siamese based matching system takes the retrieved cloth image and matches it with the clothes in the dataset, giving us the top k matches. We take a pre-trained inception-ResNet v1 module as a siamese network (trained using triplet loss for face detection) and fine-tune it on the shopping dataset using center loss. the dataset has been collected inhouse. For training the GAN, we use the LookBook dataset [14], which is publically available. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Search for Damage of the Citrus Miner to the Lemon Leaf, Implementing Artificial vision Techniques 6th

Search for Damage of the Citrus Miner to the Lemon Leaf, Imp...

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6th International conference on Technologies and Innovation

作者： Luis Carranza-Flores, Jose Martinez-Arroyo, Miriam Antonio Montero-Valverde, Jose Luis Hernandez-Hernandez, Jose IT Acapulco Tecnol Nacl Mexico Acapulco Mexico IT Chilpancingo Tecnol Nacl Mexico Chilpancingo Mexico

ISBN: (纸本)9783030620141;9783030620158

computer vision it is a scientific discipline that in recent years it is being widely used in applications via computer. Such discipline includes mechanisms to capture, process, analyze and understand the images from some specific domain. Currently has become a very important tool and powerful in agronomy to monitor growth and fruit development and vegetables that are produced in the Mexican fields. In this investigation digital image processing is applied to preprocess, segment, extract features and classify the marks left by the miner in the leaves of the Mexican lemon. the digital image processing, segment and classify the leaves on the lemon trees, making use of the established color models by Commission Internationale d'Eclairage (CIE). the techniques used in this work to segment and classify the leaves of the lemon tree;are able to process the images even if they have: low lighting, pixel saturation, noise, shades, different shades in the lemon leaves and intrinsic parameters of photographic cameras. there are several investigations in this regard, which have demonstrated how important it is the optimum choice of color model to be used for each specific area. In the present investigation, the color model HSL (Hue, Saturation, Lightness) was considered, which was used in the software MatLab (R) for the tests that were carried out.

关键词： computer vision Segmentation Classification Crop pests Citrus miner

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YOLO v3-Tiny: Object Detection and Recognition using one stage improved model 6

YOLO v3-Tiny: Object Detection and Recognition using one sta...

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6th International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Adarsh, Pranav Rathi, Pratibha Kumar, Manoj Delhi Technol Univ Dept Comp Sci & Engn Delhi India

ISBN: (纸本)9781728151977;9781728151960

Object detection has seen many changes in algorithms to improve performance both on speed and accuracy. By the continuous effort of so many researchers, deep learning algorithms are growing rapidly with an improved object detection performance. Various popular applications like pedestrian detection, medical imaging, robotics, self-driving cars, face detection, etc. reduces the efforts of humans in many areas. Due to the vast field and various state-of-the-art algorithms, it is a tedious task to cover all at once. this paper presents the fundamental overview of object detection methods by including two classes of object detectors. In two stage detector covered algorithms are RCNN, Fast RCNN, and Faster RCNN, whereas in one stage detector YOLO v1, v2, v3, and SSD are covered. Two stage detectors focus more on accuracy, whereas the primary concern of one stage detectors is speed. We will explain an improved YOLO version called YOLO v3-Tiny, and then its comparison with previous methods for detection and recognition of object is described graphically.

关键词： computer vision YOLO v3 Faster RCNN Deep learning YOLO v3-Tiny Object detection image processing Convolutional Neural Networks

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Cauliflower Leaf Disease Detection Using computerized Techniques

Cauliflower Leaf Disease Detection Using Computerized Techni...

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International conference on Computing, Communication & Automation (ICCCA)

作者： Taslima Yesmin Orin Mayen Uddin Mojumdar Shah Md. Tanvir Siddiquee Narayan Ranjan Chakraborty Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9781665447539

Crop cultivation is an important role in the agriculture industry. Presently, food loss is primarily caused by sick crops, which affects growth rate and increase. High yield depends a lot on its growth. However, now crop leaf disease has become a significant problem in agriculture as a result of which the quality and growth rate of yield in agriculture is declining day by day. through this paper, we have tried to diagnose the leaf disease of a particular crop (cauliflower). In this study, we collected data from each cauliflower leaf and created a dataset, and we divided our study was split into two sections: leaf disease detection and machine learning techniques. To detect leaf disease, we have used Matlab and various algorithms of classic machine learning such as Nav Bayes (NB), Decision Tree (TT), Random Forest (RF), Support Vector Machine (SVM), Secular Minimal Optimization (SMO). For each, we determined the accuracy, retraction, F-measurement, accuracy, and ROC values classification. this paper describes a simple but effective technique for reviewing leaf disease detection evidence and applying it to image processing and computer vision.

关键词： Support vector machines Training computer vision Shape Crops Agriculture Classification algorithms

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