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Computing, Robotics and system Sciences (ICRSS), International Conference on

作者： Yansha Zhang Feng Pan Jie Wang Lin Wang College of Date Science and Information Engineering GuiZhou Minzu University Guiyang China Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guiyang China

Data augmentation technology can effectively solve the problem of few-shot image classification, but many approaches based on data augmentation generated sample have poor discriminability, which negatively affects the classifier and classification accuracy needs to be improved. In this regard, based on the TFFCN, a mixture loss function-based classification network for few-shot learning (MLFCN) is proposed. First of all, for the prototype tensor features with a simple mean representation, ignoring the different contributions of the sample features of support set for per class to the class prototype tensor features, so that the generated prototype tensor features can not well represent sample features of per class, affecting the discriminability of the generated samples. Introduce the improved adaptively spatial feature fusion module, it uses the convolution, considers the support set of different sample features on the prototype tensor, adaptively assign weights, then weighted and summed to obtain a more representative prototype tensor feature; secondly, aiming at the blurred classification boundary of similar categories in the generated sample features, a mixture loss function is proposed. The loss function is to add the inter-class mean squared error term to the mean square error loss, and stretches the inter-class distance of the generated tensor feature, so that the classification boundary is clearer, so as to train a better classifier. Experimental results show that the improved adaptively spatial feature fusion and the mixing loss function can augment the discriminability of the generated tensor features and further improve the classification accuracy.

关键词： Training Tensors Convolution Prototypes Mean square error methods Feature extraction Generators

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Tight reservoirs classification using random forest: A case study of he 8 member in eastern yan'an gas field 3

Tight reservoirs classification using random forest: A case ...

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3rd International Conference on intelligent Control, Measurement and Signal Processing and intelligent Oil Field, ICMSP 2021

作者： Yan, Wang Ruogu, Wang Shengyi, Yang Jianping, Liu College of Petroleum Engineering Xi'An Shiyou University Xi'an China Co. Ltd. Xi'an China Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guiyang China Changqing Oilfield Company Exploration Department of PetroChina Xi'an China

ISBN: (纸本)9781665437158

Tight sandstone reservoir is very important in oil and gas exploration in China. Tight reservoirs classification and evaluation are a frontier research field. There are many indexes involved in reservoirs classification, and it is necessary to judge the reservoir type according to personal experience, which consumes lots of time and manpower. Therefore, a new classification method of tight reservoirs using random forest is proposed. Firstly, the high pressure mercury injection curves of tight sandstone reservoirs of He 8 member of Lower Shihezi Formation in eastern Yan'an Gas Field are selected as the research data. Four characteristics for classification are obtained by principal component analysis. Secondly, the random forest using CART is used to classify and obtain the results of reservoir classification. Finally, classification results are verified and parameters of the random forest are optimized. Experimental results show that the proposed reservoirs classification method has high accuracy and low calculation cost. It can effectively reduce time loss and save manpower, and has good generalization. © 2021 IEEE.

关键词： Principal component analysis

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Smoke Image Segmentation Based on Color Model

Smoke Image Segmentation Based on Color Model

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第11届创新与管理国际学术会议（ICIM2014）

作者： Deng Xing Yu Zhongming Wang Lin Li Jinlan School of Science Guizhou Minzu University Guizhou provincial key laboratory of pattern recognition and intelligent system Liupanshui Normal University

Smoke is the most significant feature in the process of fire,so it's possible to rely on smoke detection to detect *** the smoke image segmentation is the most difficult and also indispensable step in the analysis of smoke image *** order to improve its accuracy and effectively exclude the disturbances of non-smoke image,and lower the false alarm rate,it puts forward a kind of smoke image segmentation based on color *** uses K-means clustering in Lab color space and threshold segmentation in HSV color space,then merges the two ***,it uses the method of shen filter and regional mark to denoise,Experimental results on segmentation of smoke image show that the proposed method is able to segment smoke from the background.

关键词： Image segmentation K-means algorithm Color space LAB HSV

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Video matting based on local-global features fusion 4

Video matting based on local-global features fusion

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Dong, Niuniu Liang, Yihui Zou, Kun Li, Wensheng Feng, Fujian School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, these methods suffer from performance degradation when dealing with complex scenes or natural background videos. To address this challenge, we propose a video matting method called Video Matting Based on Local-Global Features Fusion (VMBLGFF) which can extract both comprehensive global correlation features and local subtle features. VMBLGFF contains two closely connected networks: a transformer network that utilizes window and global attention mechanisms to obtain global correlation features within and cross windows, and a fusion network that integrates local subtle features into the global correlation features to supplement the local detail information which may be overlooked by the attention mechanisms. VMBLGFF alleviates the issue of limiting global correlation features and has been benchmarked on both synthetic and real datasets, and the results demonstrate that VMBLGFF improves the quality of video matting and exhibits good generalization performance. © 2024 SPIE.

关键词： Computer vision

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A Cellular Ant Colony Algorithm for Path Planning Using Bayesian Posterior Probability

A Cellular Ant Colony Algorithm for Path Planning Using Baye...

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2019 2nd International Conference on Informatics, Control and Automation (ICA 2019)

作者： Xiu-fen WANG Sheng-yi YANG School of data science and Information Engineering Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University

In order to solve the problem of slow convergence rate in traditional ant colony algorithm for UAV path planning,a new cellular ant colony algorithm is ***,we construct a sector prediction area in grid environment *** we build heuristic and obstacle repulsion functions of target nodes in the prediction *** these functions,we can get the Bayesian conditional probability and posterior estimation of target *** the end,we select the node with the largest posterior estimation as the next path *** simulation results show that the new algorithm has better global search ***,using the sector prediction area makes the planned path more consistent with the UAV flight *** the designed functions in the sector prediction area speeds up the path search process.

关键词： Bayesian ant colony algorithm Sector prediction region Bayesian posteriori probability

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An Image Segmentation Algorithm Based on Gray Bit Plane for Flame

An Image Segmentation Algorithm Based on Gray Bit Plane for ...

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2016 4th International Conference on Electrical&Electronics Engineering and Computer Science(ICEEECS 2016)

作者： Xing Deng Jinlan Li Fujian Feng Lin Wang Zhongming Yu School of physics and electronic science Guizhou Normal University Guizhou provincial key laboratory of pattern recognition and intelligent system Liupanshui Normal University

ISBN: (纸本)9781510835429

Flame region judgment is an important part of fire detection system;meanwhile the flame of intelligent monitoring system also has important practical significance to national fire control safety. Therefore, this article puts forward the flame image segmentation method based on gray level bit plane. Firstly, by studying the distribution of flame image in OHTA color space, we found that it can accurately segment the flame area by using the method of the gray level bit plane for component image in OHTA color space. The experiments results show that the algorithm in different scenarios for segmenting flame has a good effectiveness and robustness.

关键词： Fire detect OHTA color space gray bit plane image segmentation

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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Natural image matting based on image inpainting

Natural image matting based on image inpainting

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Computer Graphics, Image and Virtualization (ICCGIV), International Conference on

作者： Yuan Zhang Mian Tan Zhulian Zhou Yuan Yang Yihui Liang Fujian Feng Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Computer Science University of Electronic Science and Technology China Zhongshan China

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions.

关键词： Degradation Deep learning Computer vision Image processing Refining Neural networks Production

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Cd-Vae: An Unsupervised Disentangled Representation Learning Framework for Visual Data

SSRN

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SSRN 2023年

作者： Huang, Chengquan Cai, Jianghai Luo, Senyan Wang, Shunxia Yang, Guiyan Lei, Huan Zhou, Lihua Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China

Disentangled representation learning is a crucial research problem in the field of artificial intelligence, and learning meaningful representation of visual data is useful for improving the generalizability and interpretability of machine learning models. To discover and learn the interpretable factors behind the visual data, previous approaches typically use extra regularization terms in learning disentangled representations of the generated images, which usually lead to poor results between disentanglement and generative quality. For the generative model variational auto-encoder (VAE), the traversal of the generated images along different factor directions shows meaningful and interpretable variations in the latent space. Building upon this observation, we argue that it is important to learn disentangled representations of the generated images by exploiting the scalability and training stability of the generative model VAE, and focusing on meaningful traversal directions in the latent space. Therefore, we propose contrast disentanglement based on VAE (CD-VAE), a deep unsupervised disentangled representation learning framework for visual data, that can discover the latent factors in image data. CD-VAE jointly learns disentangled representations by traversing image variations in the latent space, and constructing contrast disentanglement in the variation space. Experimental results demonstrate that CD-VAE can discover interpretable directions and achieve better disentangled representation learning for visual data. Source codes and pre-training data will be publicly available at GitHub website. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Learning bronchiole-sensitive airway segmentation cnns by feature recalibration and attention distillation 23rd

Learning bronchiole-sensitive airway segmentation cnns by fe...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China UdL INSA Lyon CREATIS CNRS UMR 5220 INSERM U1206 Lyon France

ISBN: (纸本)9783030597092

Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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