Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognitio...Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognition. In this paper, we propose a method of moving shadow detection based on edge information, which can effectively detect the cast shadow of a moving vehicle in a traffic scene. Having confirmed shadows existing in a figure, we execute the shadow removal algorithm proposed in this paper to segment the shadow from the foreground. The shadow eliminating algorithm removes the boundary of the cast shadow and preserves object edges firstly; secondly, it reconstructs coarse object shapes based on the edge information of objects; and finally, it extracts the cast shadow by subtracting the moving object from the change detection mask and performs further processing. The proposed method has been further tested on images taken under different shadow orientations, vehicle colors and vehicle sizes, and the results have revealed that shadows can be successfully eliminated and thus good video segmentation can be obtained.展开更多
Recently,virtual reality technology that can interact with various data is used for urban design and analysis.Reality,one of the most important elements in virtual reality technology,means visual expression so that a ...Recently,virtual reality technology that can interact with various data is used for urban design and analysis.Reality,one of the most important elements in virtual reality technology,means visual expression so that a person can experience three-dimensional space like reality.To obtain this realism,real-world data are used in the various fields.For example,in order to increase the realism of 3D modeled building textures real aerial images are utilized in 3D modelling.However,the aerial image captured during the day can be shadowed by the sun and it can cause the distortion or deterioration of image.To resolve this problem,researches on detecting and removing shadows have been conducted,but the detecting and removing shadow is still considered as a challenging problem.In this paper,we propose a novel method for detecting and removing shadows using deep learning.For this work,we first a build a new dataset of photo-realistic synthetic urban data based on the virtual environment using 3D spatial information provided by VWORLD.For detecting and removing shadow from the dataset,firstly,the 1-channel shadow mask image is inferred from the 3-channel shadow image through the CNN.Then,to generate a shadow-free image,a 3-channel shadow image and a detected 1-channel shadow mask into the GAN is executed.From the experiments,we can prove that the proposed method outperforms the existing methods in detecting and removing shadow.展开更多
Height extraction for buildings is a fundamental step of 3D scene reconstruction in many virtual reality applications. In this paper, we propose an automatic method to extract the height of buildings in high resolutio...Height extraction for buildings is a fundamental step of 3D scene reconstruction in many virtual reality applications. In this paper, we propose an automatic method to extract the height of buildings in high resolution satellite imagery based on the length of shadow. Taking into account the limitation of traditional algorithms, we make use of the boundary information of a building to facilitate detecting and matching the shadow regions with higher accuracy. Then, we introduce a shadow-cast model to correct the shadow location in our system. The experimental result shows that when extracting the height of buildings from complex urban regions, our method has better accuracy.展开更多
An adaptive narrowband two-phase Chan-Vese (ANBCV) model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborho...An adaptive narrowband two-phase Chan-Vese (ANBCV) model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF (Markov Random Field, MRF) is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.展开更多
In YCbCr colorspace, a method is proposed to reconstruct the background and extract moving objects based on the Gaussian model of chroma components. Background model is updated according to changes of chroma component...In YCbCr colorspace, a method is proposed to reconstruct the background and extract moving objects based on the Gaussian model of chroma components. Background model is updated according to changes of chroma components. In order to eliminate the disturbance of shadow, a shadow detecting principle is proposed in YCbCr colorspace. A Kalman filter is introduced to estimate objects' positions in the image and then the pedestrian is tracked according to its information of shape. Experiments show that the background reconstruction and updating are successful, object extraction and shadow suppression are satisfactory, and real-time and reliable tracking is realized.展开更多
Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images sti...Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine.展开更多
基金The work was supported by the National Natural Science Foundation of PRC (No.60574033)the National Key Fundamental Research & Development Programs(973)of PRC (No.2001CB309403)
文摘Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognition. In this paper, we propose a method of moving shadow detection based on edge information, which can effectively detect the cast shadow of a moving vehicle in a traffic scene. Having confirmed shadows existing in a figure, we execute the shadow removal algorithm proposed in this paper to segment the shadow from the foreground. The shadow eliminating algorithm removes the boundary of the cast shadow and preserves object edges firstly; secondly, it reconstructs coarse object shapes based on the edge information of objects; and finally, it extracts the cast shadow by subtracting the moving object from the change detection mask and performs further processing. The proposed method has been further tested on images taken under different shadow orientations, vehicle colors and vehicle sizes, and the results have revealed that shadows can be successfully eliminated and thus good video segmentation can be obtained.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2018R1D1A1B07048819)。
文摘Recently,virtual reality technology that can interact with various data is used for urban design and analysis.Reality,one of the most important elements in virtual reality technology,means visual expression so that a person can experience three-dimensional space like reality.To obtain this realism,real-world data are used in the various fields.For example,in order to increase the realism of 3D modeled building textures real aerial images are utilized in 3D modelling.However,the aerial image captured during the day can be shadowed by the sun and it can cause the distortion or deterioration of image.To resolve this problem,researches on detecting and removing shadows have been conducted,but the detecting and removing shadow is still considered as a challenging problem.In this paper,we propose a novel method for detecting and removing shadows using deep learning.For this work,we first a build a new dataset of photo-realistic synthetic urban data based on the virtual environment using 3D spatial information provided by VWORLD.For detecting and removing shadow from the dataset,firstly,the 1-channel shadow mask image is inferred from the 3-channel shadow image through the CNN.Then,to generate a shadow-free image,a 3-channel shadow image and a detected 1-channel shadow mask into the GAN is executed.From the experiments,we can prove that the proposed method outperforms the existing methods in detecting and removing shadow.
基金Supported by National Natural Science Foundation of China(61232014,61421062,61472010)the National Key Technology R&D Program of China(2015BAK01B06)
文摘Height extraction for buildings is a fundamental step of 3D scene reconstruction in many virtual reality applications. In this paper, we propose an automatic method to extract the height of buildings in high resolution satellite imagery based on the length of shadow. Taking into account the limitation of traditional algorithms, we make use of the boundary information of a building to facilitate detecting and matching the shadow regions with higher accuracy. Then, we introduce a shadow-cast model to correct the shadow location in our system. The experimental result shows that when extracting the height of buildings from complex urban regions, our method has better accuracy.
基金supported by the National Natural Science Foundation of China(41306086)Technology Innovation Talent Special Foundation of Harbin(2014RFQXJ105)the Fundamental Research Funds for the Central Universities(HEUCF100606)
文摘An adaptive narrowband two-phase Chan-Vese (ANBCV) model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF (Markov Random Field, MRF) is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.
基金This project is supported by Program for New Century Excellent Talents in University, China(No.NCET-04-0545)National Defense Science Foundation of China(No.51416070104JW0404)
文摘In YCbCr colorspace, a method is proposed to reconstruct the background and extract moving objects based on the Gaussian model of chroma components. Background model is updated according to changes of chroma components. In order to eliminate the disturbance of shadow, a shadow detecting principle is proposed in YCbCr colorspace. A Kalman filter is introduced to estimate objects' positions in the image and then the pedestrian is tracked according to its information of shape. Experiments show that the background reconstruction and updating are successful, object extraction and shadow suppression are satisfactory, and real-time and reliable tracking is realized.
文摘Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine.