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共 4722 条记录，以下是511-520 订阅

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Enhancing image Classification with Bayesian Neural Networks: Improved Training Techniques and Decision Strategies for Reliable AI Support in Uncertainty-Sensitive Tasks

Enhancing Image Classification with Bayesian Neural Networks...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International conference on

作者： Yan.Cheng. Liu Hua.Jun Huang Yi.Wen Wang School of Information Technology and Management Hunan University of Finance and Economics Changsha China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Bayesian theory is a probabilistic statistical framework for dealing with uncertainty and incomplete information, while neural networks are widely applied in deep learning fields such as image processing. Bayesian Neural Networks (BNNs) combine Bayesian theory and neural networks to provide more comprehensive predictive results. Although BNNs have significant advantages in expressing model uncertainty, their training methods and network structure design are still to be explored. Effectively training BNNs and exploring their network structure design to enhance predictive performance and computational efficiency are the core of this research. The study aims to improve the performance of BNNs in image classification tasks, specifically including the quantification and utilization of uncertainty to enhance model reliability and decision support capabilities. We use efficient training methods such as variational inference to redesign Bayesian neural networks and conduct deep image recognition training on the CIFAR-10 dataset. The improved training method allows for the rapid and accurate estimation of model weight uncertainty, while the proposed decision strategy utilizes uncertainty information to make reliable decisions. This study demonstrates that BNNs have tremendous potential in addressing real-world uncertainty issues, with the results expected to provide safer AI decision support for risk-sensitive tasks and promote the reliability and practicality of deep learning technology in various critical applications.

关键词： Training Deep learning Uncertainty Computer network reliability Neural networks Bayes methods Reliability

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Smartphone-Based Imaging System and Method for Particle Size and Shape Measuring in Baijiu Brewing Process

Smartphone-Based Imaging System and Method for Particle Size...

引用

IEEE International conference on Imaging Systems and Techniques (IST)

作者： Yang, Shanlin Lin, Yang Xu, Defu Zhang, Suyi Peng, Lihui Tsinghua Univ Dept Automat Beijing Peoples R China LuZhou LaoJiao Co Ltd Luzhou Peoples R China Tsinghua Univ LuZhou LaoJiao Joint Res Ctr Intell Luzhou Peoples R China Tsinghua Univ Tsinghua Univ LuZhou LaoJiao Joint Res Ctr Intell Beijing Peoples R China

ISBN: (纸本)9781728173719

Digital particle sizing is desirable in liquor brewing industry. This paper presents the design of a prototype imaging system for on-site particle size and shape measurement by utilizing the easily accessible smartphones. With a portable sampling box as auxiliary device, smartphones can take photographs at stable scale. A server is used to process images and establish a database to store the data for further statistical analysis. To facilitate the operation of workers, an APP is designed to communicate with the server and display the results. A sequence of image processing methods to segment particles and analyze the size distribution is introduced in some detail. Experiments performed on several different materials have demonstrated that the algorithm has strong adaptability with manually set upper and lower limits of the pixel area. The uncertainty of vision-based measurement is also discussed in brief.

关键词： particle size distribution images processing digitalization portable device

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New Born Face Feature Extraction Recognition using image processing Techniques 1

New Born Face Feature Extraction Recognition using Image Pro...

引用

1st IEEE International Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Shashikala, H.K. Sudhamsu, Gadug Bangalore India

ISBN: (纸本)9781665456876

Biometric techniques such as fingerprints, palm prints, irises, faces, and retinas are used to identify the individuals responsible for the forgery. The importance of facial recognition has grown as a direct result of the rise in the number of fakes. The information that makes up the repository for newborn babies comes from a wide variety of sources, and it features babies in a variety of poses and lighting conditions throughout many distinct galleries. A number of different facial recognition strategies and classification algorithms currently used in commercial systems are also examined as part of a standard benchmark test. The data set is obtained from the face of a newborn baby, then trained and tested with classification algorithms, and then compared based on a variety of performance indicators. The extractable features from a picture serve as the basis for the classification process, and some of these extracted features are also used for the purposes of training and testing in conjunction with the classification process. Different feature extraction strategies are investigated in this study, including local binary patterns (LBP), principal component analysis (PCA), and gray level co-occurrence matrix (GLCM). LBPs are described by their local binary patterns. Eigenfaces and Eigenvectors are produced using Principal Component Analysis, and second-order statistical features are constructed with Gray Level Co-occurrence Matrix. Photographs of newborn babies displaying a variety of facial expressions use these techniques. The extracted features are provided as input to the support vector machine for classification. In comparison to the other feature extraction methods, the principal component analysis method had an accuracy of 91%, a better recognition rate, and a shorter computation time. Experiments have also shown that this method is superior to other. © 2022 IEEE.

关键词： Face recognition

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SAR Change Detection Based on Keypoint Graph Structure and Cosegmentation-Based Refinement

SAR Change Detection Based on Keypoint Graph Structure and C...

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Geology, Mapping and Remote Sensing (ICGMRS), International conference on

作者： Jingxing Zhu Rui Liu Niangang Jiao Feng Wang Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350365719

ISBN: (纸本)9798350365726

Change detection between high-resolution Synthetic Aperture Radar (SAR) images remains a challenging task. Due to the effectively integration of structure and intensity information, graph-based methods have made some progress in SAR change detection. But it still faces the issues of robustness of graph construction and incomplete change detection outcomes. To tackle these challenges, we propose an improved keypoint-based graph structure change detection approach. First, we propose a keypoint extraction strategy guided by a coarse change map to better describe potential change areas. Second, we construct stable graph structures on these keypoints with employing a robust distance measurement that considers the statistical properties of SAR images. Then, the keypoint difference image (KDI) is derived through measuring the consistency of graph structures between pre-and post-time SAR images. Next, keypoint change map (KCM) is generated through clustering KDI by k-means. Finally, we use co-segmentation superpixel mask to refine the KCM and obtain accurate and complete binary change map. We validate the effective of proposed framework on GF-3 dataset. Experimental results demonstrate the superior performance of our method compared to alternative method.

关键词： image segmentation Geology Noise Focusing Speckle Radar polarimetry Robustness

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An Approach to Classify the Shot Selection by Batsmen in Cricket Matches Using Deep Neural Network on image Data 25

An Approach to Classify the Shot Selection by Batsmen in Cri...

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25th International conference on Computer and Information Technology, ICCIT 2022

作者： Khan, Afsana Nabila, Fariha Haque Mohiuddin, Masud Mollah, Mahadi Alam, Ashraful Tanzim Reza, Md Brac University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350346022

In recent times, technological advancement has brought a tremendous change in the field o f c ricket, which is a popular sport in many countries. Technology is being utilized to figure out projected score prediction, wicket prediction, winning probability, run rate, and many other parameters. In this research, our primary goal is to use Machine learning in the field of Cricket, where we aim to classify the shot played by the batsman, which can help in applications such as automated broadcasting systems or statistical data generation systems. For implementing our proposed model, we have generated our own dataset of cricket shot images by taking real-time photos from various cricket matches. We collected 1000 images of 10 different types of shots being played. For the classification task, we trained VGG-19 and Inception v3 model architecture and we got a better result by using VGG19. Before classification, the images had to go through several pre-processing methods such as background removal through Mask R-CNN, batsman segmentation through YOLO v3, etc. Then we used 83% of the total images to train the models and 17% to test the models. Finally, we achieved desired accuracy of 84.71% from VGG-19 and 82.35% from Inception-V3. © 2022 IEEE.

关键词： Deep neural networks

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Field Seismic Information Representation Location Technology

Field Seismic Information Representation Location Technology

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Informatics, Electronics, and Radio Engineering (PIERE), International conference Problems of

作者： Evgeniy V. Rabinovich Automation and Computer Engineering Novosibirsk state technical university Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

An original seismic location conception for constructing high resolution and accuracy seismic images is proposed. The paper shows the certain representation of field seismic information with high resolution and precision in the form of reflected waves' hodograph series. Seismic image quality is defined by precise estimation of distance to the reflected seismic signal source, time and velocity of wave propagation in a certain medium layer, reflecting boundary inclination, etc. The application of radio-locating super-resolving methods for hodograph constructing of waves reflected from seismic section boundaries is shown. For seismic super-resolution is used a locating relief that is rendered according to a design of suggested technology. The relief allows to estimate parameters of data required with a high precision. To calculate one are used close (local) statistical signal interconnections, received by different elements of digital antenna array in narrow seismogram areas that associated with wave hodograph. High-resolution seismic location algoritms application allows to render a field information with a given accuracy; to construct hodograph series of reflected waves; to divide closely located or interferring wavefronts. The technology provides automatic processing of both wave reflected from the plane boundary hodograph construction routine task and special tasks concerned with boundary configuration fluctuation or irregularity.

关键词： image quality Accuracy Propagation Superresolution Receiving antennas Information representation Reflector antennas Informatics Signal resolution Antenna arrays

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Improving German image Captions Using Machine Translation and Transfer Learning 9th

Improving German Image Captions Using Machine Translation an...

引用

9th International conference on statistical Language and Speech processing, SLSP 2021

作者： Biswas, Rajarshi Barz, Michael Hartmann, Mareike Sonntag, Daniel Saarland Informatics Campus D3_2 Saarbruecken66123 Germany Applied Artificial Intelligence Oldenburg University Marie-Curie Str. 1 Oldenburg26129 Germany

ISBN: (纸本)9783030895785

image captioning is a complex artificial intelligence task that involves many fundamental questions of data representation, learning, and natural language processing. In addition, most of the work in this domain addresses the English language because of the high availability of annotated training data compared to other languages. Therefore, we investigate methods for image captioning in German that transfer knowledge from English training data. We explore four different methods for generating image captions in German, two baseline methods and two more advanced ones based on transfer learning. The baseline methods are based on a state-of-the-art model which we train using a translated version of the English MS COCO dataset and the smaller German Multi30K dataset, respectively. Both advanced methods are pre-trained using the translated MS COCO dataset and fine-tuned for German on the Multi30K dataset. One of these methods uses an alternative attention mechanism from the literature that showed a good performance in English image captioning. We compare the performance of all methods for the Multi30K test set in German using common automatic evaluation metrics. We show that our advanced method with the alternative attention mechanism presents a new baseline for German BLEU, ROUGE, CIDEr, and SPICE scores, and achieves a relative improvement of 21.2% in BLEU-4 score compared to the current state-of-the-art in German image captioning. © 2021, Springer Nature Switzerland AG.

关键词： image enhancement

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Supervised Classification of Multispectral images: A Comparative Study

Supervised Classification of Multispectral Images: A Compara...

引用

Advanced Technology in Electronic and Electrical Engineering (ICATEEE), International conference of

作者： Radja Kheddam Ahmed Tahraoui Aichouche Belhadj Aissa Image Processing and Radiation Laboratory University of Science and Technology Houari Boumediene (USTHB) Algiers Algeria

This work focuses on a satellite data supervised classification which is one of the most important topic in satellite image processing domain. A comparative study is carried out between statistical and commonly used classifiers in one hand, and relatively new adaptive classifiers that fall under the umbrella term of metaheuristic classifiers, on the other hand. The general purpose of these classifiers is to generate the most accurate land use and land cover mapping. The comparative study aims to state of the metaheuristic classifiers (bio-inspired algorithms seen as optimization and minimization algorithms) robustness over the statistical classifiers. The main drivers for investigating these methods are also addressed. The concerned classifiers are used for a supervised classification of a remotely sensed multiband image covering the East part of the capital city Algiers. Following the resulted classification maps and their assessment, it is concluded that 1) statistical classifiers are not as effective and reliable as metaheuristic classifiers, and 2) the immune classifier may offer a viable alternative to the genetic classifier in terms of precision and time consuming.

关键词： Support vector machines Deep learning Visualization Satellites Metaheuristics Urban areas Genetics

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Evaluating Precision and Context in image Captions 7

Evaluating Precision and Context in Image Captions

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7th International conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Chaudhari, Chaitrali Prasanna Devane, Satish Lokamanya Tilak College of Engineering Department of Computer Engineering Sector 4 Vikas Nagar Navi Mumbai Maharashtra Koparkhairane India Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering Gangapur Road Maharashtra Nashik India

ISBN: (纸本)9781665405201

Captioning images is the process of generating the image description in a textual format that has wide applications in various domains. The research in this area is ongoing and seeking increasing attention as it merges the fields of computer vision and natural language processing. In the presented work, an image caption generation model consisting of a probabilistic and neural framework is built following an encoder-decoder scheme. The image is inputted to the deep learning classifier called Convolutional Neural Network (CNN) and to generate a set of sentences, a Long-Short Term Memory (LSTM) model is used. The captions generated by the proposed captioning model are compared over the other conventional models with respect to statistical analysis using standard and popular similarity metrics called Cosine Similarity, Precision and Recall. The results of evaluation of the predicted image captions compared to the actual captions are presented and the applicability of evaluation methods in context of image captioning is discussed and justified. © 2021 IEEE.

关键词： Long short-term memory

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Attention Augmented Convolutional Neural Network for Fine-Grained Plant Disease Classification and Visualization Using stochastic Sample Transformations 21

Attention Augmented Convolutional Neural Network for Fine-Gr...

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Proceedings of the 5th International conference on Advances in image processing

作者： Getinet Yilma Zhiguang Qin Maregu Assefa Gedamu Alemu Melese Ayalew Software Engineering University of Electronic Science and Technology of China China Computer Science and Technology University of Electronic Science and Technology of China China

ISBN: (纸本)9781450385183

Deep convolutional neural network (DCNN) image analysis improved plant health management. DCNN methods require train samples with diverse feature distribution and discriminative features for the models to generalize well on unseen categories. The limited train sample coined with the locality nature of the convolution operator and commonly shared kernel weights led CNN models to a sub-optimal solution. To address these challenges, we trained a multi-head self-attention augmented CNN (AAC) and explored other attention augmented networks to jointly exploit global salient and local shared features for fine-grained plant disease classification and visualization. We further proposed stochastic train sample transformations to enlarge and improve train sample diversity and feature distribution. The experiment evaluations on the PlantVillage dataset confirmed that the attention augmentation methods demonstrated robust performance in disease classification and visualization. Specifically, the AAC network scored 99.56% with stochastic transformed train samples, 98.01% with simple geometric transformed samples, and 97.34% without transformation. Therefore, attention augmentation in general, AAC in particular, and stochastic sample transformation can address discriminative plant disease classification and visualization.

关键词： disease classification Attention augmentation stochastic transformation

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