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
After a brief review of existing methods for fabric wetting and wicking measurement,a new numerical approach based on dynamic image acquisition and analysis was proposed to study the liquid wetting and wicking propert...After a brief review of existing methods for fabric wetting and wicking measurement,a new numerical approach based on dynamic image acquisition and analysis was proposed to study the liquid wetting and wicking properties of woven fabrics.A measuring system was first developed to record on-site the images of liquid ascending in fabrics for a certain period of time.The hardware and software platforms and the experimental methods were described,and the image processing and analysis as well as other related algorithms were discussed in detail.The liquid front curves and rising rates in wetting and wicking were eventually obtained towards different fabrics.From liquid wicking curves,relationship between liquid ascending height and liquid ascending time agrees well with the Washburn theory.The data comparison between the numerical measurement and the traditional test proves the reliability of the numerical results.展开更多
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
In order to achieve high-speed, real-time and accurate, an image acquisition method based on digital signal processor (DSP) TMS320DM642 is proposed for the paper currency image acquisition [1]. System will be high spe...In order to achieve high-speed, real-time and accurate, an image acquisition method based on digital signal processor (DSP) TMS320DM642 is proposed for the paper currency image acquisition [1]. System will be high speed digital signal processing (DSP) technology and complex programmable logic device (CPLD) and CIS acquisition module combination, the structure of acquisition system is given and the time series analysis, during the process of collecting this kind of design has the advantages of simple implementation, high recognition rate [2].展开更多
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
This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image...This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.展开更多
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
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
In this paper the method of approximate expansion is used to analyse a perfect planar surround sound system, resulting in an order of new and upgrade systems. First reproductinn signals of the perfect system and the c...In this paper the method of approximate expansion is used to analyse a perfect planar surround sound system, resulting in an order of new and upgrade systems. First reproductinn signals of the perfect system and the characteristics of different orders systems are analysed. The independent transmission signals and decoding (reproduction) equation of the systexns are given. The compatibility among different orders systems and the problem of simplifying output channels are discussed. The problem of signal picking up, recording,transmitting and the possibility of putting the systems into practical use are studied. A sound hoage localization experiment for the systems is carried out in order to study haage localization in relaion to the numbers of transmission signals and output channels. The experimental result is consistemt with the theoretical result. This work lay down a base for practical use.展开更多
Context-aware computing is a new mode originated from ubiquitous computing.Its emergence brings a substantial change to traditional computing and related service.Image is a pervasive tool for context awareness.A large...Context-aware computing is a new mode originated from ubiquitous computing.Its emergence brings a substantial change to traditional computing and related service.Image is a pervasive tool for context awareness.A large number of applications are developed based on images analysis.In this paper,an image acquisition system is presented for agricultural context-aware computing.The potential use of the system includes production evaluation,precise management and assistant control.The system includes four modules:the camera system,the control system,mechanism,and communication.The system can be easily installed in target crop fields.The camera system is composed of a binocular stereo camera and a color camera.Two cubic images and a corresponding texture image are collected for each plant in the process of data acquisition.An accessorial software system is developed to control and manage the capture system.Experiments show that the presented system is effective for image acquisition of agricultural context-aware computing.展开更多
The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to...The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.展开更多
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
Acquiring deep-space images with high spatial resolution and sensitivity is important for space-debris surveillance and early warning. We propose a novel computational imaging (CI) method for high-sensitivity image ...Acquiring deep-space images with high spatial resolution and sensitivity is important for space-debris surveillance and early warning. We propose a novel computational imaging (CI) method for high-sensitivity image acquisition in this letter. The proposed approach introduces CI into image formation. The proposed capturing process conducts minor modifications for cameras to encode more desirable information during capture, which is practical for hardware implementation. The latent image is reconstructed by formulating a recovery problem into an optimization problem, which is solved with iteratively reweighted least square technique. The experimental results clearly show the effectiveness of the proposed method.展开更多
Agricultural crop production is a major contributing element to any country’s economy.To maintain the economic growth of any country plants disease detection is a leading factor in agriculture.The contribution of the...Agricultural crop production is a major contributing element to any country’s economy.To maintain the economic growth of any country plants disease detection is a leading factor in agriculture.The contribution of the proposed algorithm is to optimize the extracted infor-mation from the available resources for the betterment of the result without any additional complexity.The proposed technique basically localizes the leaf region prior to the image classification into healthy and diseased.The novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected outcome.The leaf colors are analyzed using color transformation for the seed region identification.The mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral range.The neighboring pixels-based leaf region growing is applied on the initial seeds.In order to refine the leaf boundary and the disease-affected areas,we employed a random sample consensus(RANSAC)for suitable curve fitting.The feature sets using bag of visual words,Fisher vectors,and handcrafted features are extracted followed by classification using logistic regression,multilayer perceptron model,and support vector machine.The performance of the proposal is analyzed through PlantVillage datasets of apple,bell pepper,cherry,corn,grape,potato,and tomato.The simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of arts.The proposed approach provides average accuracy and area under the curve of 0.932 and 0.903,respectively.展开更多
Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for...Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.展开更多
The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific...The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473.展开更多
文摘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.
基金the Research Funds for the Central Universities,China(No.1302-1)The Project Sponsored SRF for ROCS,SEM,China
文摘After a brief review of existing methods for fabric wetting and wicking measurement,a new numerical approach based on dynamic image acquisition and analysis was proposed to study the liquid wetting and wicking properties of woven fabrics.A measuring system was first developed to record on-site the images of liquid ascending in fabrics for a certain period of time.The hardware and software platforms and the experimental methods were described,and the image processing and analysis as well as other related algorithms were discussed in detail.The liquid front curves and rising rates in wetting and wicking were eventually obtained towards different fabrics.From liquid wicking curves,relationship between liquid ascending height and liquid ascending time agrees well with the Washburn theory.The data comparison between the numerical measurement and the traditional test proves the reliability of the numerical results.
基金This work was supported by the National Key R&D Program of China,Grant No.2018YFA0306703Chengdu Innovation and Technology Project,No.2021-YF05-02413-GX.
文摘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.
文摘In order to achieve high-speed, real-time and accurate, an image acquisition method based on digital signal processor (DSP) TMS320DM642 is proposed for the paper currency image acquisition [1]. System will be high speed digital signal processing (DSP) technology and complex programmable logic device (CPLD) and CIS acquisition module combination, the structure of acquisition system is given and the time series analysis, during the process of collecting this kind of design has the advantages of simple implementation, high recognition rate [2].
基金This study was funded by the Science and Technology Project in Xi’an(No.22GXFW0123)this work was supported by the Special Fund Construction Project of Key Disciplines in Ordinary Colleges and Universities in Shaanxi Province,the authors would like to thank the anonymous reviewers for their helpful comments and suggestions.
文摘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.
文摘This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.
文摘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.
基金sponsored by Guangdong Basic and Applied Basic Research Foundation under Grant No.2021A1515110680Guangzhou Basic and Applied Basic Research under Grant No.202102020340.
文摘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.
文摘In this paper the method of approximate expansion is used to analyse a perfect planar surround sound system, resulting in an order of new and upgrade systems. First reproductinn signals of the perfect system and the characteristics of different orders systems are analysed. The independent transmission signals and decoding (reproduction) equation of the systexns are given. The compatibility among different orders systems and the problem of simplifying output channels are discussed. The problem of signal picking up, recording,transmitting and the possibility of putting the systems into practical use are studied. A sound hoage localization experiment for the systems is carried out in order to study haage localization in relaion to the numbers of transmission signals and output channels. The experimental result is consistemt with the theoretical result. This work lay down a base for practical use.
基金National High Technology R&D Program(“863”Program)of China(Grant No.2013AA102404-02)National Natural Science Foundation of China(Grant No.31171454,61300079)+1 种基金Beijing Municipal Natural Science Foundation(Grant No.4132028)Special Fund for S&T Innovation of Beijing Academy of Agriculture,and Forestry Sciences Grant(No.KJCX201204007).
文摘Context-aware computing is a new mode originated from ubiquitous computing.Its emergence brings a substantial change to traditional computing and related service.Image is a pervasive tool for context awareness.A large number of applications are developed based on images analysis.In this paper,an image acquisition system is presented for agricultural context-aware computing.The potential use of the system includes production evaluation,precise management and assistant control.The system includes four modules:the camera system,the control system,mechanism,and communication.The system can be easily installed in target crop fields.The camera system is composed of a binocular stereo camera and a color camera.Two cubic images and a corresponding texture image are collected for each plant in the process of data acquisition.An accessorial software system is developed to control and manage the capture system.Experiments show that the presented system is effective for image acquisition of agricultural context-aware computing.
基金Auhui Provincial Key Research and Development Project(No.202004a07020050)National Natural Science Foundation of China Youth Program(No.61901006)。
文摘The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.
基金Foundation items:Shanghai Sailing Program,China (No. 21YF1401300)Shanghai Science and Technology Innovation Action Plan,China (No.19511101802)Fundamental Research Funds for the Central Universities,China (No.2232021D-25)。
文摘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).
基金supported by the National Natural Science Foundation Key Project(No.61231016)theNational Natural Science Foundation of China Youth Science Fund Project(No.61301193)
文摘Acquiring deep-space images with high spatial resolution and sensitivity is important for space-debris surveillance and early warning. We propose a novel computational imaging (CI) method for high-sensitivity image acquisition in this letter. The proposed approach introduces CI into image formation. The proposed capturing process conducts minor modifications for cameras to encode more desirable information during capture, which is practical for hardware implementation. The latent image is reconstructed by formulating a recovery problem into an optimization problem, which is solved with iteratively reweighted least square technique. The experimental results clearly show the effectiveness of the proposed method.
文摘Agricultural crop production is a major contributing element to any country’s economy.To maintain the economic growth of any country plants disease detection is a leading factor in agriculture.The contribution of the proposed algorithm is to optimize the extracted infor-mation from the available resources for the betterment of the result without any additional complexity.The proposed technique basically localizes the leaf region prior to the image classification into healthy and diseased.The novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected outcome.The leaf colors are analyzed using color transformation for the seed region identification.The mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral range.The neighboring pixels-based leaf region growing is applied on the initial seeds.In order to refine the leaf boundary and the disease-affected areas,we employed a random sample consensus(RANSAC)for suitable curve fitting.The feature sets using bag of visual words,Fisher vectors,and handcrafted features are extracted followed by classification using logistic regression,multilayer perceptron model,and support vector machine.The performance of the proposal is analyzed through PlantVillage datasets of apple,bell pepper,cherry,corn,grape,potato,and tomato.The simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of arts.The proposed approach provides average accuracy and area under the curve of 0.932 and 0.903,respectively.
文摘Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.
文摘The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473.