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Research on Improved MobileViT Image Tamper Localization Model
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作者 Jingtao Sun Fengling Zhang +1 位作者 Huanqi Liu Wenyan Hou 《Computers, Materials & Continua》 SCIE EI 2024年第8期3173-3192,共20页
As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social stability.In the realm of image tampering localization,accurately l... As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social stability.In the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of challenges.These issues impede the model’s universality and generalization capability and detrimentally affect its performance.To tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization.Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream integration.Meanwhile,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization performance.To comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature fusion.This facilitates a more precise localization of tampered regions of various sizes.Furthermore,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered images.This strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy loss.As a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is enhanced.Experimental evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validate the effectiveness of our proposed model.The meticulously calibrated FL-MobileViT model consistently outperforms numerous existing general models regarding localization accuracy across diverse datasets,demonstrating superior adaptability. 展开更多
关键词 image tampering localization focused linear attention mechanism MobileViT contrastive loss
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Two-Dimensional Perovskite Single Crystals for High-Performance X-ray Imaging and Exploring MeV X-ray Detection
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作者 Xieming Xu Yiheng Wu +5 位作者 Yi Zhang Xiaohui Li Fang Wang Xiaoming Jiang Shaofan Wu Shuaihua Wang 《Energy & Environmental Materials》 SCIE EI CAS CSCD 2024年第1期139-146,共8页
Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,bu... Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,but they are mostly used in low-energy(≤130 keV)regions.Direct detection of MeV X-rays,which ensure thorough penetration of the thick shell walls of containers,trucks,and aircraft,is also highly desired in practical industrial applications.Unfortunately,scintillation semiconductors for high-energy X-ray detection are currently scarce.Here,This paper reports a 2D(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single crystal with outstanding sensitivity and stability toward X-ray radiation that provides an ultra-wide detectable X-ray range of between 8.20 nGy_(air)s^(-1)(50 keV)and 15.24 mGy_(air)s^(-1)(9 MeV).The(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single-crystal detector with a vertical structure is used for high-performance X-ray imaging,delivering a good spatial resolution of 4.3 Ip mm^(-1)in a plane-scan imaging system.Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X-ray radiation at high bias voltages,leading to a sensitivity of 46.90μC Gy_(air)-1 cm^(-2)(-1.16 Vμm^(-1))with 9 MeV X-ray radiation,demonstrating that 2D perovskites have enormous potential for high-energy industrial applications. 展开更多
关键词 MeV X-ray detection single-crystal X-ray detectors two-dimensional perovskites X-ray imaging
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An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan-Vese Model
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作者 Shupeng Qiu Chujin Lin Wei Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期1119-1134,共16页
In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussi... In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation. 展开更多
关键词 image segmentation Chan–Vese model local radial basis functionmethod Gaussian kernel Runge–Kuttamethod
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Relational graph location network for multi-view image localization
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作者 YANG Yukun LIU Xiangdong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第2期460-468,共9页
In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relationa... In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy. 展开更多
关键词 multi-view image localization graph construction heterogeneous graph graph neural network
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An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images
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作者 Syed Ayaz Ali Shah Aamir Shahzad +4 位作者 Musaed Alhussein Chuan Meng Goh Khursheed Aurangzeb Tong Boon Tang Muhammad Awais 《Computers, Materials & Continua》 SCIE EI 2024年第5期2565-2583,共19页
Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when deal... Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field. 展开更多
关键词 Line detector vessel detection localization mathematical morphology image processing
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A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm
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作者 苏静明 方士辉 +1 位作者 洪炎 温言 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第7期233-243,共11页
A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are con... A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc. 展开更多
关键词 color image encryption discrete cosine transform two-dimensional(2D)coupled chaotic system diagonal scrambling
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Image Retrieval with Text Manipulation by Local Feature Modification 被引量:2
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作者 查剑宏 燕彩蓉 +1 位作者 张艳婷 王俊 《Journal of Donghua University(English Edition)》 CAS 2023年第4期404-409,共6页
The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the qu... The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1). 展开更多
关键词 image retrieval text manipulation ATTENTION local feature modification
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Generalization ability of a CNNγ-ray localization model for radiation imaging
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作者 Wei Lu Hai‑Wei Zhang +3 位作者 Ming‑Zhe Liu Hao‑Xuan Li Xian‑Guo Tuo Lei Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第12期53-65,共13页
Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generali... Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generalization capabilities of a CNN localization model with respect to theγ-ray energy and thickness of the crystal.The model maintained a high positional linearity(PL)and spatial resolution for ray energies between 59 and 1460 keV.The PL at the incident surface of the detector was 0.99,and the resolution of the central incident point source ranged between 0.52 and 1.19 mm.In modified uniform redundant array(MURA)imaging systems using a thick crystal,the CNNγ-ray localization model significantly improved the useful field-of-view(UFOV)from 60.32 to 93.44%compared to the classical centroid localization methods.Additionally,the signal-to-noise ratio of the reconstructed images increased from 0.95 to 5.63. 展开更多
关键词 γ-Ray imaging γ-Ray localization model Convolutional neural network Spatial resolution
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Convolutional Neural Networks Based Indoor Wi-Fi Localization with a Novel Kind of CSI Images 被引量:8
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作者 Haihan Li Xiangsheng Zeng +2 位作者 Yunzhou Li Shidong Zhou Jing Wang 《China Communications》 SCIE CSCD 2019年第9期250-260,共11页
Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices.In this paper,a new method of constructing the channel s... Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices.In this paper,a new method of constructing the channel state information(CSI)image is proposed to improve the localization accuracy.Compared with previous methods of constructing the CSI image,the new kind of CSI image proposed is able to contain more channel information such as the angle of arrival(AoA),the time of arrival(TOA)and the amplitude.We construct three gray images by using phase differences of different antennas and amplitudes of different subcarriers of one antenna,and then merge them to form one RGB image.The localization method has off-line stage and on-line stage.In the off-line stage,the composed three-channel RGB images at training locations are used to train a convolutional neural network(CNN)which has been proved to be efficient in image recognition.In the on-line stage,images at test locations are fed to the well-trained CNN model and the localization result is the weighted mean value with highest output values.The performance of the proposed method is verified with extensive experiments in the representative indoor environment. 展开更多
关键词 convolutional NEURAL network INDOOR WI-FI localization channel state information CSI image
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New two-dimensional fuzzy C-means clustering algorithm for image segmentation 被引量:3
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作者 周鲜成 申群太 刘利枚 《Journal of Central South University of Technology》 EI 2008年第6期882-887,共6页
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this... To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation. 展开更多
关键词 image segmentation fuzzy C-means clustering particle swarm optimization two-dimensional histogram
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SEMI-FRAGILE WATERMARKING ALGORITHM FOR IMAGE TAMPERS LOCALIZATION AND RECOVERY 被引量:2
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作者 Jiang Xuemei Liu Quan 《Journal of Electronics(China)》 2008年第3期343-351,共9页
Two watermarks are embedded into the original image. One is the authentication watermark generated by secret key, which is embedded into the sub-LSB (Least Significant Bit) of the original image for tamper localizat... Two watermarks are embedded into the original image. One is the authentication watermark generated by secret key, which is embedded into the sub-LSB (Least Significant Bit) of the original image for tamper localization; the other is the recovery watermark for tamper recovering. The original image is divided into 8 x 8 blocks and each block is transformed by Discrete Cosine Transform (DCT). For each block, some lower frequency DCT coefficients are chosen to be quantized and binary encoded so as to gain the recovery watermark of each block, and the recovery watermark is embedded into the LSB of another block by chaos encryption and authentication chain technology. After the two watermarks being detected, the location of any minute changes in image can be detected, and the tampered image data can be recovered effectively. In the paper, the number of coefficients and their bit lengths are carefully chosen in order to satisfy with the payload of each block and gain the capability of self-recovering. The proposed algorithm can well resist against possible forged attacks. Experimental results show that the watermark generated by the proposed algorithm is sensitive to tiny changes in images, and it has higher accuracy of tamper localization and good capability of the tamper recovery. 展开更多
关键词 image authentication Tamper localization image recovery Semi-fragile watermark
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Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm 被引量:2
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作者 LI Wei HU Xiao-hui WANG Hong-chuang 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第1期42-49,共8页
In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.Whe... In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm. 展开更多
关键词 two-dimensional cross entropy biogeography-based optimization(BBO)algorithm multi-threshold image segmentation
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Splicing Image and Its Localization:A Survey 被引量:2
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作者 Jinwei Wang Yangyang Li 《Journal of Information Hiding and Privacy Protection》 2019年第2期77-86,共10页
With the rapid development of information technology,digital images have become an important medium for information transmission.However,manipulating images is becoming a common task with the powerful image editing to... With the rapid development of information technology,digital images have become an important medium for information transmission.However,manipulating images is becoming a common task with the powerful image editing tools and software,and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal.Images are easily spliced and distributed,and the situation will be a great threat to social security.The survey covers splicing image and its localization.The present status of splicing image localization approaches is discussed along with a recommendation for future research. 展开更多
关键词 Social security image splicing image splicing localization
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Image encryption technique based on new two-dimensional fractional-order discrete chaotic map and Menezes–Vanstone elliptic curve cryptosystem 被引量:1
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作者 Zeyu Liu Tiecheng Xia Jinbo Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第3期161-176,共16页
We propose a new fractional two-dimensional triangle function combination discrete chaotic map(2D-TFCDM)with the discrete fractional difference.Moreover,the chaos behaviors of the proposed map are observed and the bif... We propose a new fractional two-dimensional triangle function combination discrete chaotic map(2D-TFCDM)with the discrete fractional difference.Moreover,the chaos behaviors of the proposed map are observed and the bifurcation diagrams,the largest Lyapunov exponent plot,and the phase portraits are derived,respectively.Finally,with the secret keys generated by Menezes-Vanstone elliptic curve cryptosystem,we apply the discrete fractional map into color image encryption.After that,the image encryption algorithm is analyzed in four aspects and the result indicates that the proposed algorithm is more superior than the other algorithms. 展开更多
关键词 CHAOS fractional two-dimensional triangle function combination discrete chaotic map image encryption Menezes-Vanstone elliptic curve cryptosystem
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A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques 被引量:1
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作者 Weijin Tan Yunqing Wu +1 位作者 Peng Wu Beijing Chen 《Journal of New Media》 2019年第1期11-25,共15页
Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the mo... Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed. 展开更多
关键词 image forgery copy-move forgery localization passive techniques
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High-precision two-dimensional atom localization via probe absorption in an M-scheme atomic system
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作者 吴建春 刘正东 郑军 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第4期264-267,共4页
In the present paper, we investigate the behavior of two-dimensional atom localization in a five-level M-scheme atomic system driven by two orthogonal standing-wave fields. We find that the precision and resolution of... In the present paper, we investigate the behavior of two-dimensional atom localization in a five-level M-scheme atomic system driven by two orthogonal standing-wave fields. We find that the precision and resolution of the atom localization depends on the probe field detuning significantly. And because of the effect of the microwave field, an atom can be located at a particular position via adjusting the system parameters. 展开更多
关键词 two-dimensional atom localization probe absorption
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An Image Localization System Based on Single Photon
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作者 Yanyi Wu Xiaoyu Li +3 位作者 Qinsheng Zhu Xiaolei Liu Hao Wu Shan Yang 《Computers, Materials & Continua》 SCIE EI 2022年第12期6139-6149,共11页
As an essential part of artificial intelligence,many works focus on image processing which is the branch of computer vision.Nevertheless,image localization faces complex challenges in image processing with image data ... As an essential part of artificial intelligence,many works focus on image processing which is the branch of computer vision.Nevertheless,image localization faces complex challenges in image processing with image data increases.At the same time,quantum computing has the unique advantages of improving computing power and reducing energy consumption.So,combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms.At present,many quantum image localization algorithms have been proposed,and their efficiency is theoretically higher than the corresponding classical algorithms.But,in quantum computing experiments,quantum gates in quantum computing hardware need to work at very low temperatures,which brings great challenges to experiments.This paper proposes a single-photon-based quantum image localization algorithm based on the fundamental theory of single-photon image classification.This scheme realizes the operation of the mixed national institute of standards and technology database(MNIST)quantum image localization by a learned transformation for non-noise condition,noisy condition,and environmental attack condition,respectively.Compared with the regular use of entanglement between multi-qubits and low-temperature noise reduction conditions for image localization,the advantage of this method is that it does not deliberately require low temperature and entanglement resources,and it improves the lower bound of the localization success rate.This method paves a way to study quantum computer vision. 展开更多
关键词 MNIST data SINGLE-PHOTON quantum computing image localization
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Two-dimensional atom localization via probe absorption in a four-level atomic system
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作者 王志平 葛强 +1 位作者 阮于华 俞本立 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第7期202-207,共6页
We have investigated the two-dimensional (2D) atom localization via probe absorption in a coherently driven four-level atomic system by means of a radio-frequency field driving a hyperfine transition. It is found th... We have investigated the two-dimensional (2D) atom localization via probe absorption in a coherently driven four-level atomic system by means of a radio-frequency field driving a hyperfine transition. It is found that the detecting probability and precision of 2D atom localization can be significantly improved via adjusting the system parameters. As a result, our scheme may be helpful in laser cooling or the atom nano-lithography via atom localization. 展开更多
关键词 two-dimensional (2D) atom localization probe absorption four-level atomic system
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Image Thresholding Using Two-Dimensional Tsallis Cross Entropy Based on Either Chaotic Particle Swarm Optimization or Decomposition
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作者 吴一全 张晓杰 吴诗婳 《China Communications》 SCIE CSCD 2011年第7期111-121,共11页
The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The e... The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly. 展开更多
关键词 signal and information processing image segmentation threshold selection two-dimensional Tsallis cross entropy chaotic particle swarm optimization DECOMPOSITION
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Efficient Object Localization Scheme Based on Vanishing Line in Road Image for Autonomous Vehicles
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作者 Bongkyo Moon Jiwon Choi +1 位作者 Juehyun Lee Minyoung Lee 《Journal of Computer and Communications》 2021年第9期85-97,共13页
This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the t... This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n<sup>2</sup>) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained. 展开更多
关键词 Object localization image Preprocessing Sliding Window Vanishing Line Autonomous Vehicle
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