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Machine learning meets distinctness in variety testing 18

Machine learning meets distinctness in variety testing

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18th ieee/CVF International conference on Computer Vision (ICCV)

作者： Couasnet, Geoffroy El Abidine, Mouad Zine Laurens, Francois Dutagaci, Helin Rousseau, David INRAe UMR 1345 IRHS Angers France Univ Angers Angers France Eskisehir Osmangazi Univ Eskisehir Turkey

ISBN: (纸本)9781665401913

Distinctness is a binary trait used in variety testing to determine if a new plant variety can be considered distinct or not from a set of already existing varieties. Currently distinctness is mostly based on human visual perception. This communication considers distinctness with a machine learning perspective where distinctness is evaluated through an identification process based on information extraction from machine vision. Illustrations are provided on apple variety testing to perform distinctness based on color. An automated pipeline of image acquisition, processing and supervised learning is proposed. A feature space based on the 3D color histogram of a set of apples is built. This feature space is built using optimal transport, fractal dimension, mutual entropy and fractional anisotropy and it provides results in accordance with human expertise when applied to a set of varieties highly contrasted in color and another one with low color contrast. These results open new research directions for achieving higher-throughput, higher reproducibility and higher statistical confidence in variety testing.

关键词： Three-dimensional displays image color analysis Machine vision Supervised learning Pipelines Machine learning Information retrieval

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Fine-Grained Blind Face Inpainting with 3D Face Component Disentanglement

Fine-Grained Blind Face Inpainting with 3D Face Component Di...

引用

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

作者： Yu Bai Ruian He Weimin Tan Bo Yan Yangle Lin School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Inpainting is a task to restore occlusion or other corruption on images. However, previous works require mask of the occluded area to restore the occluded image, which is inconvenient for application. Blind face inpainting aims to automatically restore the occluded face without position information of the corrupt region. In this paper, we propose a novel fine-grained blind face inpainting framework, combining 3D face components disentanglement with generative network. Canonical face texture and shape disentangled by unsupervised 3D face model is restored separately to get occlusion-free rendered result. Finally, the pixel-to-pixel generative module utilize the occlusion image and the coarse de-occlusion face to get refined inpainted result. We also build up a new dataset called CelebO-3D which consists of occluded face images synthesized with 3D occlusion and rendered by 3D face model. Extensive experiments show that the proposed method is effective and robust in face blind inpainting both in synthesized and real images. Extensive evaluations and comparison with previous methods also show our superior effectiveness and lightweight architecture.

关键词： Solid modeling Three-dimensional displays Shape Speech enhancement Signal processing Acoustics image restoration

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Sparse noise minimization in image classification using Genetic Algorithm and DenseNet 5

Sparse noise minimization in image classification using Gene...

引用

5th ieee Annual International conference on Information communications Technology and Society (ICTAS)

作者： Mienye, Ibomoiye Domor Ainah, Priye Kenneth Emmanuel, Ikiomoye Douglas Esenogho, Ebenezer Univ Johannesburg Dept Elect & Elect Engn Johannesburg South Africa Niger Delta Univ Dept Elect & Elect Engn Wilberforce Isl Nigeria Univ Salford Sch Comp Sci & Engn Salford Lancs England Univ Johannesburg Ctr Telecommun Dept Elect & Elect Engn Johannesburg South Africa

ISBN: (纸本)9781728180816

Noise handling is a critical aspect of image processing, which can significantly affect the accuracy of classification and recognition algorithms. In this paper, we propose a technique for improved noise handling in sparse input feature maps where the noise signal is also sparse. The signal-noise relationship is formulated as an optimization problem which is solved by a genetic algorithm. The genetic algorithm is applied to optimize the setting of a non-convexity parameter which yields a more accurate image sparse matrix. The resulting feature map is then classified using a densely connected convolutional network (DenseNet). Lung computed tomography images were used for the experiments. The proposed approach achieves better performance when the classification results are compared with a case in which the input signal has not been denoised using the proposed approach.

关键词： ADMM DenseNet genetic algorithm lung cancer sparse noise

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Early Detection of Heart Disease Using Deep Learning Model 8

Early Detection of Heart Disease Using Deep Learning Model

引用

8th International conference on Smart Structures and Systems, ICSSS 2022

作者： Deepika, R. Srikaanth, P. Balaji Pitchai, R. Srm Institute of Science and Technology Kattankulathur Technology School of Computing Tamilnadu India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9781665497619

Identifying and detecting the early signs of diseases is difficult due to asymptomatic conditions. Detecting the signs early gives the patient a higher chance of surviving. There are several technologies to identify the heart diseases for timely treatment. So far, different techniques of machine learning is used to predict heart abnormalities. Machine learning models trained by large amounts of data in the healthcare field contain a lot of medical images that enable the medical professionals to identify even minimal changes with a high level of accuracy. This paper presents the deep learning technique used to predict the heart abnormalities. Deep learning technique helps in processing the image data through various tests which include electrocardiogram, echocardiogram etc., that detects the abnormalities in the heart. © 2022 ieee.

关键词： Heart Deep learning Medical services Feature extraction Data models Intelligent structures Medical diagnostic imaging

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VAEPose: 6D Pose Estimation with visual Appearance Enhancement for Low-Light Conditions

VAEPose: 6D Pose Estimation with Visual Appearance Enhanceme...

引用

ieee International conference on Industrial Technology (ICIT)

作者： Yan Gao Lu Chen Jingyang Liu Insti. of Big Data Science & Industry School of Computer & Information Tech. Shanxi University Taiyuan China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

6D pose estimation is a key technology in the field of computer vision, and has great application potential in the fields of virtual reality, augmented reality, robot operation, and intelligent driving. When using deep neural network technology to accomplish this advanced vision task, the quality of the data is demanding. Currently, the task achieves excellent results in normal lighting conditions, but the task presents serious challenges due to the significant degradation of data quality in low-light conditions. The results are not satisfactory when the object pose is directly estimated using a one-stage processing mode in low-light conditions. Therefore, in view of this observation, we choose a two-stage processing mode to solve the challenges. In this work, we propose a Low-light Pose Estimation Network based on the visual Appearance Enhancement module (VAEPose). Among them, the Low-light Appearance Enhancement Module (LAEM) is designed to improve data quality and show clearer details, helping the network model to be more adaptable to light and less dependent on data. In the LAEM module, an attention mechanism is introduced to achieve the fusion of channel and spatial attention of the extracted RGB features, resulting in finer color and texture information. The Virtual Low-light Dataset (VLD) used in the experiment is created by us in virtual environment. Experiments verify the effectiveness of the proposed method.

关键词： visualization Solid modeling image color analysis Data integrity Pose estimation Virtual environments Feature extraction

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Implementation and Acceleration of image Recognition Algorithm under Limited Resources 13

Implementation and Acceleration of Image Recognition Algorit...

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Jiang, Bin Xu, Jiaxin Zhang, Jiaqi Xiao, Jian College of Electronic and Optical Engineering College of Microelectronics Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665407854

In recent years, with the development of artificial intelligence and Internet of Things technology, more and more neural network models need to run on the mobile terminals. In order to better run the neural network model on the embedded chip with limited resources, we propose a method for efficiently transplanting and implementing MobileNet model, and utilize C66xDSP for model inference acceleration. We decompose the neural network model into multiple layer groups, which are loaded and computed by different C66xDSP modules in parallel. Experiments have proved that our method effectively reduces the scale of the MobileNet parameters, and improves the recognition speed of MobileNet on the embedded platform under the premise of taking into account the computational cost and the accuracy of image recognition. Our code is open source in https://***/njupt-b523/oph-robot. © 2021 ieee.

关键词： Neural network models

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A Comprehensive Analysis on Face Recognition using various Deep Learning Techniques

A Comprehensive Analysis on Face Recognition using various D...

引用

2022 International conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Jayabharathi, P. Suresh, A. Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9781665479950

Identifying human faces are difficult issue whereas the occurrence of huge diversity in facial pose, lighting, and poor picture quality. Some of the most significant issues with face recognition is object recognition and computer vision. Face recognition still has a lot of issues that need to be worked out and can be resolved using more powerful methods to a larger extent. The application of face recognition in a real-life situation still remains, a challenging task. The most important processes in face recognition, Pre-processing, Face Detection, Feature Extraction, and Classifications remains that should be followed when identifying faces in the wild. The purpose of this paper to assess a variety of deep learning methods;how its work with face recognition for identifying face images in a large database and also explained about various datasets that are used for image classification. To comprehend the present state-of-the-art, of this paper delivers a full review of various DL approaches. © 2022 ieee.

关键词： Recurrent neural networks

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image/Video Summarization in Text/Speech for visually Impaired People 2

Image/Video Summarization in Text/Speech for Visually Impair...

引用

2nd ieee Mysore Sub Section International conference, MysuruCon 2022

作者： Chaitra, C. Chennamma Vethanayagi, R. Manoj Kumar, M.V. Prashanth, B.S. Snehah, H.R. Thomas, Likewin Shiva Darshan, S.L. NITTE-Meenakshi Department of Information Science & Engineering Karnataka Bangalore560064 India Manipal Institute of Technology Department of Information and Communication Technology Karnataka Bengaluru560 064 India Pesitm Department of Information Science & Engineering Karnataka Shimoga577204 India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

In the year 2022, an estimated 2.2 billion people around the globe will have a visual impairment. The problem may be hereditary or due to accidents. Nonetheless, technological advancements in helping visually impaired people have been going on for a long time. The admittance of technical concepts such as robotics, Machine Learning, and Artificial Intelligence for societal needs has proven worthwhile. The blind or visually impaired people learn about their surroundings through other senses, such as touch, hearing, and smell. Our proposed work aims to build an end-to-end solution for visually impaired people to help them grasp the environment by summarizing the images or video streams with the help of Machine Learning paradigms. The proposed work uses a pre-trained Caffe Object Detection model and requires less data for training and detection. We have developed a Client-Server model for our proposed idea wherein the significant computations happen on the server side, which is the Object detection model, and the client App is developed using Android. The app also has a text-to-signal processing feature that helps summarize the objects detected in the form of an audio catalog. © 2022 ieee.

关键词： Object detection

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Speckle Reduction in SAR images using CNN 3

Speckle Reduction in SAR Images using CNN

引用

3rd International conference on Signal processing and Communication (ICPSC)

作者： Santhi, V. Mohandass, Dheshan Jayanthi, J. Arulmozhivarman, P. Mehra, Raghav VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India ISRO Ctr Space Applicat Ahmadabad 380015 Gujarat India

ISBN: (纸本)9781665428644

The importance of remote sensing has ballooned in the recent decade, due to the increased need for its applications, from commercial and economic, to intelligence and military applications. Synthetic Aperture Radar (SAR), an important remote sensing instrument captures data that are inherently affected with speckle noise due to its coherent imaging, which makes working with SAR data complicated. Therefore, Synthetic Aperture Radar (SAR) images can benefit greatly from good speckle reduction or suppression techniques, making them easier to analyze and utilize their maximum potential. This paper gives a technical review of various speckle filtering approaches for Synthetic Aperture Radar images (SAR), discussing the drawbacks of the older conventional methods, and the improvements the newer convolutional Neural Networks (CNN) based solutions bring to the table.

关键词： Speckle Noise CNN Noise Suppression image Filtering SAR de-speckling

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Fusion with GCN and SE-ResNeXt Network for Aspect Based Multimodal Sentiment Analysis

Fusion with GCN and SE-ResNeXt Network for Aspect Based Mult...

引用

ieee Information Technology, Networking, Electronic and Automation Control conference

作者： Jun Zhao Fuping Yang College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

Aspect Based Multimodal Sentiment Analysis to determine the sentiment polarity of each aspect mentioned in multimodal posts or comments. However, many models fail to explain the related syntactic constraints and long-term word dependencies, as well as lack image feature extraction. In view of the above problems, we propose a Fusion with GCN and SE-ResNeXt Network(FGSN), which constructs a graph convolution network on the dependency tree of sentences to obtain the context representation and aspects words representation by using syntactic information and word dependency. Also, we use the pretrained network SE-ResNeXt101 to extract image features and generate visual representations. Moreover, the position attention and channel attention mechanism are used to obtain the image features. Finally, we fuse image features and text features to classify sentiment polarity. The experimental results show that our model performs well on the two datasets of TWITTER-2015 and TWITTER-2017.

关键词： visualization Sentiment analysis Convolution Mood Fuses Computational modeling Graphics processing units

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