Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task...Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.展开更多
Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches,leveraging the relational structure between image regions to improve accuracy.This paper presents an enhan...Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches,leveraging the relational structure between image regions to improve accuracy.This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network(CNN)features with graph convolutional network(GCN)learning,leveraging superpixel-based image representations.The proposed framework initiates the process by segmenting input images into significant superpixels,reducing computational complexity while preserving essential spatial structures.A pre-trained CNN backbone extracts both global and local features from these superpixels,capturing critical texture and shape information.These features are structured into a graph,and the framework presents a graph classification model that learns and propagates relationships between nodes,improving global contextual understanding.By combining the strengths of CNN-based feature extraction and graph-based relational learning,the method achieves higher accuracy,faster training speeds,and greater robustness in image classification tasks.Experimental evaluations on four agricultural datasets demonstrate the proposed model’s superior performance,achieving accuracy rates of 96.57%,99.63%,95.19%,and 90.00%on Tomato Leaf Disease,Dragon Fruit,Tomato Ripeness,and Dragon Fruit and Leaf datasets,respectively.The model consistently outperforms conventional CNN(89.27%–94.23%accuracy),VIT(89.45%–99.77%accuracy),VGG16(93.97%–99.52%accuracy),and ResNet50(86.67%–99.26%accuracy)methods across all datasets,with particularly significant improvements on challenging datasets such as Tomato Ripeness(95.19%vs.86.67%–94.44%)and Dragon Fruit and Leaf(90.00%vs.82.22%–83.97%).The compact superpixel representation and efficient feature propagation mechanism further accelerate learning compared to traditional CNN and graph-based approaches.展开更多
The region completeness of object detection is very crucial to video surveillance,such as the pedestrian and vehicle identifications.However,many conventional object detection approaches cannot guarantee the object re...The region completeness of object detection is very crucial to video surveillance,such as the pedestrian and vehicle identifications.However,many conventional object detection approaches cannot guarantee the object region completeness because the object detection can be influenced by the illumination variations and clustering backgrounds.In order to overcome this problem,we propose the iterative superpixels grouping(ISPG)method to extract the precise object boundary and generate the object region with high completeness after the object detection.First,by extending the superpixel segmentation method,the proposed ISPG method can improve the inaccurate segmentation problem and guarantee the region completeness on the object regions.Second,the multi-resolution superpixel-based region completeness enhancement method is proposed to extract the object region with high precision and completeness.The simulation results show that the proposed method outperforms the conventional object detection methods in terms of object completeness evaluation.展开更多
Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is prop...Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.展开更多
This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accu...This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.展开更多
Photoacoustic(PA) imaging has drawn tremendous research interest for various applications in biomedicine and experienced exponential growth over the past decade. Since the scattering effect of biological tissue on ult...Photoacoustic(PA) imaging has drawn tremendous research interest for various applications in biomedicine and experienced exponential growth over the past decade. Since the scattering effect of biological tissue on ultrasound is two-to three-orders magnitude weaker than that of light, photoacoustic imaging can effectively improve the imaging depth.However, as the depth of imaging further increases, the incident light is seriously affected by scattering that the generated photoacoustic signal is very weak and the signal-to-noise ratio(SNR) is quite low. Low SNR signals can reduce imaging quality and even cause imaging failure. In this paper, we proposed a new wavefront shaping and imaging method of low SNR photoacoustic signal using digital micromirror device(DMD) based superpixel method. We combined the superpixel method with DMD to modulate the phase and amplitude of the incident light, and the genetic algorithm(GA) was used as the wavefront shaping algorithm. The enhancement of the photoacoustic signal reached 10.46. Then we performed scanning imaging by moving the absorber with the translation stage. A clear image with contrast of 8.57 was obtained while imaging with original photoacoustic signals could not be achieved. The proposed method opens new perspectives for imaging with weak photoacoustic signals.展开更多
We develop a new video-based motion analysis algorithn to determine whether two persons have any interaction in their meet- ing. The interaction between two persons can be very general, such as shaking hands, exchangi...We develop a new video-based motion analysis algorithn to determine whether two persons have any interaction in their meet- ing. The interaction between two persons can be very general, such as shaking hands, exchanging objects, and so on. To make the motio~ analysis robust to image noise, we segment each video flame into a set of superpixels and then derive a motion feature and a motion pattern for each superpixel by averaging the optical flow within the superpixe Specifically, we use the lattice cut to construct the superpixels, which are spatially and temporally consistent across frames. Based on the motion feature and the motion pattern of the superpixels, we develop an algorithm to divide an input video sequence into three consecutive periods： 1） two persons walking toward each other, 2） two persons meeting each other, and 3） two persons walking away fi＇om each other. The experiment show that the proposed algorithm can accurately dis- tinguish the videos with and without human interactions.展开更多
The goal of salient object detection is to estimate the regions which are most likely to attract human's visual attention. As an important image preprocessing procedure to reduce the computational complexity, sali...The goal of salient object detection is to estimate the regions which are most likely to attract human's visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.展开更多
The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current al...The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current algorithms are designed for natural images with little noise corrupted.In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise,we propose a noiseresistant superpixel segmentation(NRSS)algorithm in this paper.In the proposed NRSS,the spectral signatures are first transformed into frequency domain to enhance the noise robustness;then the two widely spectral similarity measures-spectral angle mapper(SAM)and spectral information divergence(SID)are combined to enhance the discriminability of the spectral similarity;finally,the superpixels are generated with the proposed frequency-based spectral similarity.Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels.Moreover,the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering(SLIC),where the comparison results prove the superiority of the proposed superpixel segmentation algorithm.展开更多
Superpixel segmentation is the oversegmentation of an image into a set of homogeneous regions. Superpixel has many specific properties and has been commonly used as supporting regions for primitives to reduce computat...Superpixel segmentation is the oversegmentation of an image into a set of homogeneous regions. Superpixel has many specific properties and has been commonly used as supporting regions for primitives to reduce computations in various computer vision tasks. One property of superpixels is compactness, which is preferred in some applications. In this paper, we give an review on image superpixel segmentation algorithms proposed in recent years. Superpixel segmentation approaches are classified based on the compactness constraint and their main idea are introduced. We also compare these algorithms in visual and evaluate them with five common measurements.展开更多
Superpixels generation is becoming increasingly popular as a preprocessing in many computer vision applications. A superpixel is an image patch which has uniform pixels intensity and is aligned with intensity edges. S...Superpixels generation is becoming increasingly popular as a preprocessing in many computer vision applications. A superpixel is an image patch which has uniform pixels intensity and is aligned with intensity edges. Superpixels provide a convenient primitive from which local image features can be computed. So far, there are many methods to generate superpixels. Several main superpixels generation algorithms are summarized in this paper and the advantages and disadvantages of them are analyzed simply. In the end, some applications of superpixels are listed.展开更多
With the decrease of agricultural labors and the increase in production costs,harvesting robots have become a research hotspot in recent years.To guide harvesting robots to pick mature citrus more precisely under vari...With the decrease of agricultural labors and the increase in production costs,harvesting robots have become a research hotspot in recent years.To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions,an image segmentation algorithm based on superpixel was proposed.Efficient simple linear iterative clustering(SLIC)algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels.The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks(BPNN)classifier in the next step.The adjacency information of superpixels was considered by calculating the global-local binary pattern(LBP)in R component images when extracting texture features.To accelerate the classification process,the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels.These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77%and pixel-level accuracy of 94.96%,while the average time to segment one image was 0.4778 s.Therefore,the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently.展开更多
An automated superpixels identification/mosaicking method is presented for the analysis of cone photoreceptor cells with the use of adaptive optics scanning laser ophthalmoscope(AO-SLO) images. This is an image overse...An automated superpixels identification/mosaicking method is presented for the analysis of cone photoreceptor cells with the use of adaptive optics scanning laser ophthalmoscope(AO-SLO) images. This is an image oversegmentation method used for the identification and mosaicking of cone photoreceptor cells in AO-SLO images.It includes image denoising, estimation of the cone photoreceptor cell number, superpixels segmentation, merging of superpixels, and final identification and mosaicking processing steps. The effectiveness of the presented method was confirmed based on its comparison with a manual method in terms of precision, recall, and F1-score of 77.3%, 95.2%, and 85.3%, respectively.展开更多
Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multisp...Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance bottleneck.For this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC results.Supace can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective way.To better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for LCC.Extensive experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.展开更多
Background:Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing.Traditionally,size uniformity is one of the significant features of superpixels.However,in medical image...Background:Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing.Traditionally,size uniformity is one of the significant features of superpixels.However,in medical images,in which subjects scale varies greatly and background areas are often flat,size uniformity rarely conforms to the varying content.To obtain the fewest superpixels with retaining important details,the size of superpixel should be chosen carefully.Methods:We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images,especially pathological images.A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content,that is smaller(larger)superpixels in color-riching areas(flat areas).Results:The proposed superpixel algorithm can generate superpixels with boundary adherence,insensitive to noise,and with extremely big sizes and extremely small sizes on one image.The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images.Conclusion:With the proposed algorithm,the choice of superpixel size is automatic,which frees the user from the predicament of setting suitable superpixel size for a given application.The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.展开更多
文摘Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.
文摘Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches,leveraging the relational structure between image regions to improve accuracy.This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network(CNN)features with graph convolutional network(GCN)learning,leveraging superpixel-based image representations.The proposed framework initiates the process by segmenting input images into significant superpixels,reducing computational complexity while preserving essential spatial structures.A pre-trained CNN backbone extracts both global and local features from these superpixels,capturing critical texture and shape information.These features are structured into a graph,and the framework presents a graph classification model that learns and propagates relationships between nodes,improving global contextual understanding.By combining the strengths of CNN-based feature extraction and graph-based relational learning,the method achieves higher accuracy,faster training speeds,and greater robustness in image classification tasks.Experimental evaluations on four agricultural datasets demonstrate the proposed model’s superior performance,achieving accuracy rates of 96.57%,99.63%,95.19%,and 90.00%on Tomato Leaf Disease,Dragon Fruit,Tomato Ripeness,and Dragon Fruit and Leaf datasets,respectively.The model consistently outperforms conventional CNN(89.27%–94.23%accuracy),VIT(89.45%–99.77%accuracy),VGG16(93.97%–99.52%accuracy),and ResNet50(86.67%–99.26%accuracy)methods across all datasets,with particularly significant improvements on challenging datasets such as Tomato Ripeness(95.19%vs.86.67%–94.44%)and Dragon Fruit and Leaf(90.00%vs.82.22%–83.97%).The compact superpixel representation and efficient feature propagation mechanism further accelerate learning compared to traditional CNN and graph-based approaches.
基金supported in part by the“MOST”under Grant No.103-2221-E-216-012
文摘The region completeness of object detection is very crucial to video surveillance,such as the pedestrian and vehicle identifications.However,many conventional object detection approaches cannot guarantee the object region completeness because the object detection can be influenced by the illumination variations and clustering backgrounds.In order to overcome this problem,we propose the iterative superpixels grouping(ISPG)method to extract the precise object boundary and generate the object region with high completeness after the object detection.First,by extending the superpixel segmentation method,the proposed ISPG method can improve the inaccurate segmentation problem and guarantee the region completeness on the object regions.Second,the multi-resolution superpixel-based region completeness enhancement method is proposed to extract the object region with high precision and completeness.The simulation results show that the proposed method outperforms the conventional object detection methods in terms of object completeness evaluation.
基金supported by NSFC Joint Fund with Guangdong under Key Project(U1201258)National Natural Science foundation of China(61402261+3 种基金6130308861572286)the scientific research foundation of Shandong Province of Outstanding Young Scientist Award(BS2013DX048)Shandong Ji’nan Science and Technology Development Project(201202015)
文摘Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.
文摘This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.
基金Project supported by the National Key Research and Development Program of China(Grant No.2017YFB1104500)the Beijing Natural Science Foundation,China(Grant No.7182091)+1 种基金the National Natural Science Foundation of China(Grant No.21627813)the Research Projects on Biomedical Transformation of China–Japan Friendship Hospital(Grant No.PYBZ1801)。
文摘Photoacoustic(PA) imaging has drawn tremendous research interest for various applications in biomedicine and experienced exponential growth over the past decade. Since the scattering effect of biological tissue on ultrasound is two-to three-orders magnitude weaker than that of light, photoacoustic imaging can effectively improve the imaging depth.However, as the depth of imaging further increases, the incident light is seriously affected by scattering that the generated photoacoustic signal is very weak and the signal-to-noise ratio(SNR) is quite low. Low SNR signals can reduce imaging quality and even cause imaging failure. In this paper, we proposed a new wavefront shaping and imaging method of low SNR photoacoustic signal using digital micromirror device(DMD) based superpixel method. We combined the superpixel method with DMD to modulate the phase and amplitude of the incident light, and the genetic algorithm(GA) was used as the wavefront shaping algorithm. The enhancement of the photoacoustic signal reached 10.46. Then we performed scanning imaging by moving the absorber with the translation stage. A clear image with contrast of 8.57 was obtained while imaging with original photoacoustic signals could not be achieved. The proposed method opens new perspectives for imaging with weak photoacoustic signals.
基金Supported by the National Natural Science Foundation of China(61272453)
文摘We develop a new video-based motion analysis algorithn to determine whether two persons have any interaction in their meet- ing. The interaction between two persons can be very general, such as shaking hands, exchanging objects, and so on. To make the motio~ analysis robust to image noise, we segment each video flame into a set of superpixels and then derive a motion feature and a motion pattern for each superpixel by averaging the optical flow within the superpixe Specifically, we use the lattice cut to construct the superpixels, which are spatially and temporally consistent across frames. Based on the motion feature and the motion pattern of the superpixels, we develop an algorithm to divide an input video sequence into three consecutive periods： 1） two persons walking toward each other, 2） two persons meeting each other, and 3） two persons walking away fi＇om each other. The experiment show that the proposed algorithm can accurately dis- tinguish the videos with and without human interactions.
基金the Natural Science Foundation of China(Nos.61602349,61375053,and 61273225)the China Scholarship Council(No.201508420248)Hubei Chengguang Talented Youth Development Foundation(No.2015B22)
文摘The goal of salient object detection is to estimate the regions which are most likely to attract human's visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61801222 and No.61501522in part by the Project of Shandong Province Higher Educational Science and Technology Program under Grant No.KJ2018BAN047.
文摘The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current algorithms are designed for natural images with little noise corrupted.In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise,we propose a noiseresistant superpixel segmentation(NRSS)algorithm in this paper.In the proposed NRSS,the spectral signatures are first transformed into frequency domain to enhance the noise robustness;then the two widely spectral similarity measures-spectral angle mapper(SAM)and spectral information divergence(SID)are combined to enhance the discriminability of the spectral similarity;finally,the superpixels are generated with the proposed frequency-based spectral similarity.Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels.Moreover,the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering(SLIC),where the comparison results prove the superiority of the proposed superpixel segmentation algorithm.
基金Supported by the National Science Foundation of China(61373078,61572292)NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project(U1609218)
文摘Superpixel segmentation is the oversegmentation of an image into a set of homogeneous regions. Superpixel has many specific properties and has been commonly used as supporting regions for primitives to reduce computations in various computer vision tasks. One property of superpixels is compactness, which is preferred in some applications. In this paper, we give an review on image superpixel segmentation algorithms proposed in recent years. Superpixel segmentation approaches are classified based on the compactness constraint and their main idea are introduced. We also compare these algorithms in visual and evaluate them with five common measurements.
基金Supported by Independent Innovation Foundation of Shandong University,IIFSDU(No.2012TB013)Scientific Research Foundation of Shandong Province of Outstanding Young Scientist Award(No.BS2013DX041,No.BS2013DX048)+1 种基金Shandong Province Natural Fund(zr2011FM031)Ji'nan Science and Technology Development Project(No.201202015)
文摘Superpixels generation is becoming increasingly popular as a preprocessing in many computer vision applications. A superpixel is an image patch which has uniform pixels intensity and is aligned with intensity edges. Superpixels provide a convenient primitive from which local image features can be computed. So far, there are many methods to generate superpixels. Several main superpixels generation algorithms are summarized in this paper and the advantages and disadvantages of them are analyzed simply. In the end, some applications of superpixels are listed.
基金This work was financially supported by Huzhou Har-bot Intelligent Technology Co.,Ltd.
文摘With the decrease of agricultural labors and the increase in production costs,harvesting robots have become a research hotspot in recent years.To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions,an image segmentation algorithm based on superpixel was proposed.Efficient simple linear iterative clustering(SLIC)algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels.The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks(BPNN)classifier in the next step.The adjacency information of superpixels was considered by calculating the global-local binary pattern(LBP)in R component images when extracting texture features.To accelerate the classification process,the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels.These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77%and pixel-level accuracy of 94.96%,while the average time to segment one image was 0.4778 s.Therefore,the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently.
基金supported by the Jiangsu Provincial Key R&D Program (Nos. BE2019682 and BE2018667)National Natural Science Foundation of China(Nos. 61605210,61675226,and 61378090)+3 种基金Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2019320)National Key R&D Program of China(Nos. 2016YFC0102500 and 2017YFB0403700)Frontier Science Research Project of the Chinese Academy of Sciences (No. QYZDB-SSW-JSC03)Strategic Priority Research Program of the Chinese Academy of Sciences(No. XDB02060000)
文摘An automated superpixels identification/mosaicking method is presented for the analysis of cone photoreceptor cells with the use of adaptive optics scanning laser ophthalmoscope(AO-SLO) images. This is an image oversegmentation method used for the identification and mosaicking of cone photoreceptor cells in AO-SLO images.It includes image denoising, estimation of the cone photoreceptor cell number, superpixels segmentation, merging of superpixels, and final identification and mosaicking processing steps. The effectiveness of the presented method was confirmed based on its comparison with a manual method in terms of precision, recall, and F1-score of 77.3%, 95.2%, and 85.3%, respectively.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62161160336, 42030111, and 62101045)the China Postdoctoral Science Foundation Funded Project (Grant No. 2021M690385)
文摘Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance bottleneck.For this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC results.Supace can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective way.To better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for LCC.Extensive experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.
基金The work was supported by the NSFC-Zhejiang Joint Fund of the Integration of Informatization and Industrialization(No.U1909210)the National Natural Science Foundation of China(No.61772312)the Fundamental Research Funds of Shandong University(No.2018JC030).
文摘Background:Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing.Traditionally,size uniformity is one of the significant features of superpixels.However,in medical images,in which subjects scale varies greatly and background areas are often flat,size uniformity rarely conforms to the varying content.To obtain the fewest superpixels with retaining important details,the size of superpixel should be chosen carefully.Methods:We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images,especially pathological images.A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content,that is smaller(larger)superpixels in color-riching areas(flat areas).Results:The proposed superpixel algorithm can generate superpixels with boundary adherence,insensitive to noise,and with extremely big sizes and extremely small sizes on one image.The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images.Conclusion:With the proposed algorithm,the choice of superpixel size is automatic,which frees the user from the predicament of setting suitable superpixel size for a given application.The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.
