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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Xiong Zhihao Pan, Hang Zhao, Yongjia Chen, Jinlong Yang, Minghao Chinese Acad Sci CASIA Res Ctr Brain Inspired Intelligence BII Inst Automat Beijing Peoples R China Jiangxi Univ Finance & Econ JUFE Modern Ind Sch Virtual Real Inst Automat Nanchang Jiangxi Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Jiangxi Res Inst Beijing 330096 Jiangxi Peoples R China Guilin Univ Elect Sci & Technol Guilin Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In the process of automated apple picking, when depth estimation is performed based on the center position of the target detection candidate frame, errors may occur due to the influence of complex environments such as leaf occlusion and lighting changes. Therefore, this work proposes an image segmentation method with joint loss optimization for apple picking. This method automatically labels the collected apple data set to obtain accurate apple label files. Then, the BCEDice is used as the joint loss optimization function to balance the problem of sparse training gradients, and the processed apple data set is input into the autoencoding model, combining ResNet and UNet for training. Finally, experiments on the apple segmentation dataset showed that the model performed well on evaluation indicators, with the Dice coefficient and precision reaching 89.7% and 89.6%, respectively. This validation result shows that the model also exhibits good generalization ability and segmentation accuracy on the diverse apple dataset collected.

关键词： automated apple harvesting image segmentation method object detection joint loss optimization

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Active Contour image segmentation method for Training Talents of Computer Graphics and image Processing Technology

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IEEE ACCESS 2021年 9卷 19187-19194页

作者： Ye, Xinghuo Wang, Qianyi Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Beijing Chengshi Wanglin Informat Technol Co Ltd AI Lab Artificial Intelligence Lab Beijing 100001 Peoples R China

image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively.

关键词： image segmentation image edge detection Active contours Training Computer graphics Computational modeling Computer graphics and image processing technology talent training active contour model image segmentation method

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Light image Enhancement Application in Visual Communication Art Based on Wireless Network Sensing and image segmentation method

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MOBILE NETWORKS & APPLICATIONS 2024年

作者： Zhao, Hongsen Yi, Zhonghua Hunan Womens Univ Sch Art & Design Changsha 410004 Peoples R China

With the rapid development of Internet of Things technology, optical image enhancement, as a technology to improve the quality of visual information, has shown great potential in art communication and display. This study aims to explore the effect of combining wireless network-based sensing technology and image segmentation method in light image enhancement, analyze its practical application in sensory communication art, and improve the visual expression and information transmission efficiency of artistic works. Wireless sensor network is used to collect light, temperature and other related data in the environment, and the data is processed and transmitted by mobile terminal. Combined with advanced image segmentation algorithm, the acquired images are processed to achieve dynamic enhancement under different lighting conditions. In the experiment, a variety of art works were selected for comparative analysis, and the effect was evaluated by combining subjective evaluation with objective indicators. The research results show that, based on the data support of wireless network sensors, image segmentation technology significantly improves the visual effect of art works in different environments. Under low light conditions, the enhanced image details are richer, the overall expressive force of art works is improved, and the audience's aesthetic experience is also improved.

关键词： Wireless Network Sensing image segmentation method Light image Enhancement Visual Communication Art

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Non-parametric mixture model with TV spatial regularisation and its dual expectation maximisation algorithm

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IET image PROCESSING 2018年第9期12卷 1655-1662页

作者： Yan, Shi Yu, Zihao Liu, Jun Beijing Normal Univ Sch Math Sci Lab Math & Complex Syst Minist Educ China Beijing 100875 Peoples R China

An image segmentation method based on a non-parametric mixture model together with total variation (TV) regularisation is proposed. The authors use a kernel density estimator as a basic mixture model, which can better separate the non-central distributed data. To enforce its robustness, they integrate the well-known TV regularisation into the statistical method. They use the dual method to efficiently solve the TV-related energy and get a new dual expectation maximisation algorithm. Experiments on both synthetic images and real images show that the proposed algorithm can achieve good segmentation results. Compared with the parametric models and hidden Markov random field-based method, the proposed method can produce better result in some cases.

关键词： medical image processing Markov processes expectation-maximisation algorithm image segmentation statistical analysis nonparametric mixture model TV spatial regularisation dual expectation maximisation algorithm image segmentation method total variation regularisation kernel density estimator basic mixture model noncentral distributed data well-known TV regularisation statistical method dual method TV-related energy parametric models

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Colon cancer prediction using 2DReCA segmentation and hybrid features on histopathology images

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IET image PROCESSING 2020年第16期14卷 4144-4157页

作者： Babu, Tina Singh, Tripty Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru Karnataka India

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer-free colon tissue, epithelial cells generally have an elliptical shape;their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi-level thresholding based on Renyi's two-dimensional entropy with a cultural algorithm (2DR(e)CA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey-level co-occurrence matrix and block-wise elliptical local binary pattern) are calculated based on pre-processed grey-scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques.

关键词： visual databases image texture cancer entropy image segmentation image classification feature extraction biological tissues medical image processing colon cancer prediction hybrid features histopathology images histopathological images cancer-free colon tissue elliptical shape structure alters malignant tissue colon biopsy image segmentation multiple databases image segmentation method multilevel thresholding Renyi's two-dimensional entropy elliptical epithelial cells segmented background successful segmentation shape descriptors texture features grey-level co-occurrence matrix block-wise elliptical local binary pattern pre-processed grey-scale colon images hybrid feature vector data sets multiple performance measures

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Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images

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IET COMPUTER VISION 2015年第5期9卷 629-638页

作者： Rahmani, Masoumeh Akbarizadeh, Gholamreza Shahid Chamran Univ Ahvaz SCU Dept Elect Engn Fac Engn Ahvaz *** Iran

Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral clustering is an image segmentation method making it possible to combine features and cues. This study presents a new spectral clustering method using unsupervised feature learning (UFL). In this method, the SAR image is primarily processed by the non-negative matrix factorisation (NMF) algorithm and then non-negative features containing spatial structure information are extracted. Afterwards, the extracted features are learned using a sparse coding algorithm to increase the discrimination power of the features. Sparse coding is an unsupervised learning algorithm which finds the patterns or high-level semantics of the data. Ultimately, the SAR image segmentation operation is performed by applying spectral clustering on learned features. In this method, sparse coding learns features and simultaneously creates the similarity function required in spectral clustering through the production of sparse coefficients. Therefore this method avoids the Gaussian similarity function, which has a problem with scale parameter adjustment that is one of the drawbacks of spectral clustering methods. The results demonstrate that, compared with wavelet and GLCM features, NMF features manage to obtain more meaningful information and provide a better SAR image segmentation result. The results have also demonstrated that SAR image segmentation using learned features is significantly improved compared with segmentation by unlearned features. The experimental results indicate the effect of UFL on SAR image segmentation.

关键词： synthetic aperture radar radar imaging image coding image segmentation feature extraction learning (artificial intelligence) matrix decomposition radar computing unsupervised feature learning sparse coding synthetic aperture radar images segmentation SAR image feature representation image segmentation method spectral clustering method nonnegative matrix factorisation algorithm spatial structure information feature extraction sparse coding algorithm SAR image segmentation operation spectral clustering Gaussian similarity function SAR image segmentation

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segmentation method for face detection in complex background

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ELECTRONICS LETTERS 2000年第3期36卷 213-214页

作者： Wang, YJ Yuan, BZ No Jiaotong Univ Inst Sci Informat Beijing 100044 Peoples R China

A nevi method for segmenting candidate regions of human faces with a complex background is proposed. A number of evolutionary agents are distributed in the 2-D colour image environment in order to find the face-like p... 详细信息

关键词： complex background image segmentation method face-like pixels image colour analysis image segmentation face detection image recognition Pattern recognition robust face recognition human faces evolutionary agents 2D colour image environment Computer vision and image processing techniques image processing and restoration face-like region

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SAR image segmentation based on analysing similarity with clutter spatial patterns

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ELECTRONICS LETTERS 2016年第21期52卷 1807-1809页

作者： Zhao, Xiaohui Jiang, Yicheng Wang, Wen-Qin Harbin Inst Technol Res Inst Elect Engn Technol Harbin 150001 Heilongjiang Peoples R China Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Peoples R China

An image segmentation method is proposed based on exploiting the spatial patterns in synthetic aperture radar (SAR) images. First, a similarity map is constructed for evaluating the similarity between the spatial pattern of the SAR image and the reference clutter images. Then, a segmentation threshold is selected based on the similarity map. Finally, image segmentation is achieved by preserving pixels that have larger similarity values. The superiority of the proposed method is verified by the experimental results for high-resolution SAR images.

关键词： synthetic aperture radar radar imaging radar clutter image segmentation image resolution clutter spatial patterns synthetic aperture radar spatial patterns similarity map reference clutter images segmentation threshold similarity values high-resolution SAR images image segmentation method

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Automatic image edge extraction and segmentation by random seed region search algorithm

Automatic image edge extraction and segmentation by random s...

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International Conference on Neural Networks and Signal Processing

作者： Ji, WH Yu, HM Liu, YY Zhejiang Univ Informat Sci & Elect Engn Dept Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)0780377028

This paper proposes a new automatic image edge extraction and segmentation method. Firstly, We extract the image's edge automatically by random seed regions search algorithm. Obviously, we can choose the initial seed randomly and automatically. Moreover, we can adjust segmentation threshold automatically depending on contrast of image. Then, we conclude that our algorithm is effective comparing with the other's popular image edge extraction and segmentation methods.

关键词： edge detection feature extraction image segmentation search problems automatic image edge extraction image segmentation method random seed region search algorithm segmentation threshold adjustment image contrast

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