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Multi-object extraction technology for complex background based on faster regions-CNN algorithm in the context of artificial intelligence

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2025年第1期19卷 15-27页

作者： Peng, Shunsheng Hunan Coll Informat Dept Software Engn Changsha 410200 Peoples R China

With the rapid expansion of artificial intelligence technology, the trajectory of future battlefield operations is increasingly guided by intelligence. The modern combat environment is growing in complexity. An improved Fast Regions-Convolutional Neural Network algorithm is proposed for extracting multiple objects in intricate backgrounds to enhance the precision of striking enemy objects. Furthermore, to reduce the missed detection rate of objects, this research aims to integrate the backbone feature extraction network of the Residual Network with the Feature Pyramid Network. This integration seeks to optimize candidate regions and extract more detailed features of the object, thereby improving overall detection accuracy. This study notably enhances object detection algorithms in complex scenarios, particularly in military and security-sensitive fields, by integrating residual and feature pyramid networks into the Fast Regions-Convolutional Neural Network framework. The primary contribution is improving the algorithm's detection capability for small and multi-scale objects while bolstering its robustness against challenges like poor image quality, low contrast, and occlusion scenarios. Additionally, the study enhances the algorithm's generalization through data augmentation technology, ensuring high accuracy in diverse environments. These advancements contribute significantly to object detection techniques and hold practical value for applications like intelligent monitoring and autonomous driving. The data confirm that the enhanced Faster Regions-Convolutional Neural Network model achieves the highest accuracy at 98.21%, surpassing the original model by 13.47%. Following pruning, the optimized model demonstrates a remarkable 33.33% increase in the detection rate, coupled with a 1.58 times acceleration effect. The research on multi-object extraction technology in complex backgrounds, based on the Faster Regions-Convolutional Neural Network algorithm within artifici

关键词： Artificial intelligence FR-CNN algorithm object extraction FPN ResNet Deep learning Multi-object detection Machine vision

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object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2004年第3-4期58卷 129-151页

作者： Baltsavias, EP ETH Honggerberg Swiss Fed Inst Technol Inst Geodesy & Photogrammetry CH-8093 Zurich Switzerland

The paper focuses mainly on extraction of important topographic objects, like buildings and roads, that have received much attention the last decade. As main input data, aerial imagery is considered, although other data, like from laser scanner, SAR and high-resolution satellite imagery, can be also used. After a short review of recent image analysis trends, and strategy and overall system aspects of knowledge-based image analysis, the paper focuses on aspects of knowledge that can be used for object extraction: types of knowledge, problems in using existing knowledge, knowledge representation and management, current and possible use of knowledge, upgrading and augmenting of knowledge. Finally, an overview on commercial systems regarding automated object extraction and use of a priori knowledge is given. In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown. (C) 2003 Elsevier B.V. All rights reserved.

关键词： geospatial data image analysis knowledge-based multi-cue integration operational systems commercial software systems semi-automation mapping object extraction object revision roads buildings

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object extraction from T2 weighted brain MR image using histogram based gradient calculation

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PATTERN RECOGNITION LETTERS 2013年第12期34卷 1356-1363页

作者： Gilanie, Ghulam Attique, Muhammad Hafeez-Ullah Naweed, Shahid Ahmed, Ejaz Ikram, Masroor Islamia Univ Bahawalpur Dept Comp Sci & Informat Technol Bahawalpur Pakistan Pakistan Inst Engn & Appl Sci Dept Phys & Appl Math Islamabad 45650 Pakistan Bahauddin Zakariya Univ Dept Phys Multan Pakistan

Several segmentation methods have been reported with their own pros and cons. Here we proposed a method for object extraction from T2 weighted (T2) brain magnetic resonance (MR) images. The proposed method is purely based on histogram processing for gradient calculation. The proposed method utilizes the histogram filtering technique as a pre-processing. The primary brain areas;gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are extracted out efficiently from 2D and 3D images. The method has been successfully implemented on human brain MR images obtained in clinical environment. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Magnetic resonance imaging object extraction Cerebrospinal fluid Gray matter White matter

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object extraction Using a Stochastic Birth-and-Death Dynamics in Continuum

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2009年第3期33卷 347-359页

作者： Descombes, Xavier Minlos, Robert Zhizhina, Elena INRIA I3S Ariana Res Grp F-06902 Sophia Antipolis France Lab Dobrushin IITP Moscow 127994 Russia

We define a new birth and death dynamics dealing with configurations of disks in the plane. We prove the convergence of the continuous process and propose a discrete scheme converging to the continuous case. This framework is developed to address image processing problems consisting in detecting a configuration of objects from a digital image. The derived algorithm is applied for tree crown extraction and bird detection from aerial images. The performance of this approach is shown on real data.

关键词： object extraction Stochastic modeling Birth and death dynamics

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object extraction from a moving background using velocity estimation and optimal filter in the MixeD

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2001年第12期E84A卷 3082-3089页

作者： Wu, SL Shinmura, H Hamada, N Keio Univ Fac Sci & Technol Yokohama Kanagawa 2238522 Japan

This paper addresses the problem to extract moving object from the moving background in the mixed domain (MixeD), which makes it possible to carry the filtering in one dimension, Since the velocities of moving object and background are necessary for moving object extraction, we first estimate the velocities based on the appropriate spatial frequency point selection method in the MixeD. Then an optimal filter used for 1-D signal filtering is designed. By filtering 1-D signals over all spatial frequencies, signals with certain velocity vector are extracted, while the extracted image is obtained by applying the 2-D IDFT to the filtered signals. The simulation results,;how that the method can extract moving object based both on the correctly estimated velocity and the proposed optimal 1-D filter.

关键词： linear trajectory signal velocity estimation object extraction optimal filter dynamic image processing

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object extraction scheme and protocol for energy efficient image communication over wireless sensor networks

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COMPUTER NETWORKS 2013年第15期57卷 2949-2960页

作者： Duc Minh Pham Aziz, Syed Mahfuzul Univ S Australia Sch Engn Mawson Lakes SA 5095 Australia

To date, wireless sensor networks lack the most powerful human sense - vision. This is largely due to two main problems: (1) available wireless sensor nodes lack the processing capability and energy resource required to efficiently process and communicate large volume of image data and (2) the available protocols do not provide the queue control and error detection capabilities required to reduce packet error rate and retransmissions to a level suitable for wireless sensor networks. This paper presents an innovative architecture for object extraction and a robust application-layer protocol for energy efficient image communication over wireless sensor networks. The protocol incorporates packet queue control mechanism with built-in CRC to reduce packet error rate and thereby increase data throughput. Unlike other image transmission protocols, the proposed protocol offers flexibility to adjust the image packet size based on link conditions. The proposed processing architecture achieves high speed object extraction with minimum hardware requirement and low power consumption. The system was successfully designed and implemented on FPGA. Experimental results obtained from a network of sensor nodes utilizing the proposed architecture and the application-layer protocol reveal that this novel approach is suitable for effectively communicating multimedia data over wireless sensor networks. (C) 2013 The Author. Published by Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Image communication Energy efficiency object extraction Field Programmable Gate Array (FPGA)

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object extraction FROM LASER-RADAR IMAGERY

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PATTERN RECOGNITION 1991年第6期24卷 587-600页

作者： CHEN, SW JAIN, AK NATL TAIWAN UNIV DEPT INFORMAT & COMP EDUCTAIPEITAIWAN MICHIGAN STATE UNIV DEPT COMP SCIE LANSINGMI 48824

A procedure for extracting objects present in imagery taken by the fieldable laser radar transceiver (FLRT) is reported. The laser radar imagery of a scene is composed of a registered pair of range and intensity images. Range images are first normalized in order to recover shape distortion due to the inherent effect of the ellipse-like coordinate system of the FLRT sensor. The normalized images are then smoothed using a median operator followed by a histogram-based filter to further remove outliers of noise. Afterwards, objects can be extracted from the images by removing the backgrounds of scenes. Extracted objects are then separated according to their range values into individual objects. Finally, by referring to the registered intensity images, some non-target objects can be discovered and be removed a priori.

关键词： FIELDABLE LASER RADAR TRANSCEIVER RANGE NORMALIZATION BACKGROUND REMOVAL object extraction

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object extraction Using an Edge-Based Feature for Query-by-Sketch Image Retrieval

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2015年第1期E98D卷 214-217页

作者： Takasu, Takuya Kumagai, Yoshiki Ohashi, Gosuke Shizuoka Univ Dept Elect & Elect Engn Hamamatsu Shizuoka 4328561 Japan

We previously proposed a query-by-sketch image retrieval system that uses an edge relation histogram (ERH). However, it is difficult for this method to retrieve partial objects from an image, because the ERH is a feature of the entire image, not of each object. Therefore, we propose an object-extraction method that uses edge-based features in order to enable the query-by-sketch system to retrieve partial images. This method is applied to 20,000 images from the Corel Photo Gallery. We confirm that retrieval accuracy is improved by using the edge-based features for extracting objects, enabling the query-by-sketch system to retrieve partial images.

关键词： query-by-sketch image retrieval object extraction partial image retrieval edge-based feature

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object extraction Based on Evolutionary Morphological Processing

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Geo-Spatial Information Science 2004年第3期7卷 193-197,230页

作者： LIBin PANLi HubeiProvinceBureauofSurveyingandMapping Wuhan430070China 不详

This paper introduces a novel technique for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structure element, which is derived by genetic algorithms. The population of morphological filters is iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring element using the evolution principles of genetic search. Experimental results of road extraction from high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.

关键词： object extraction genetic algorithms morphological processing high resolution satellite images

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object extraction is not subject to Child Relativized Minimality

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LINGUA 2010年第6期120卷 1516-1521页

作者： Goodluck, Helen Univ York Dept Language & Linguist Sci York YO10 5DD N Yorkshire England

Friedmann et al. (2009) propose that the grammar of three-five-year-old children imposes a stricter version of Relativized Minimality that the adult grammar does. This allows them to explain several experimental findings, including the difficulty children have in comprehending object relative clauses when the subject of the relative is a lexical NP, and in comprehending object questions when the object is D(iscourse)-linked. The purpose of this paper is to challenge their analysis, in terms of both its empirical coverage and its implications for a theory of acquisition that assumes continuity. (C) 2009 Elsevier B.V. All rights reserved.

关键词： D(iscourse)-linking Relativized Minimality object extraction Child language Continuity

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