Key Laboratory of Image Understanding and Computer Vision

Shenyang, China

Key Laboratory of Image Understanding and Computer Vision

Shenyang, China
SEARCH FILTERS
Time filter
Source Type

Zhang L.,CAS Shenyang Institute of Automation | Zhang L.,University of Chinese Academy of Sciences | Zhu F.,CAS Shenyang Institute of Automation | Zhu F.,Chinese Academy of Sciences | And 4 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

In order to enable the non-cooperative rendezvous, capture, and removal of large space debris, robust and fast tracking of the non-cooperative target is needed. This paper proposes an improved algorithm of real-time visual tracking for space non-cooperative target based on three-dimensional model, and it does not require any artificial markers. The non-cooperative target is assumed to be a 3D model known and constantly in the field of view of the camera mounted on the chaser. Space non-cooperative targets are regarded as less textured manmade objects, and the design documents of 3D model are available. Space appears to be black, so we can assume the object is in empty space and only the object is visible, and the background of the image is dark. Due to edge features offer a good invariance to illumination changes or image noise, our method relies on monocular vision and uses 3D-2D correspondences between the 3D model and its corresponding 2D edges in the image. The paper proposes to remove the sample points that are susceptible to false matches based on geometrical distance due to perspective projection of the 3D model. To allow a better robustness, we compare the local region similarity to get better matches between sample points and edge points. Our algorithm is proved to be efficient and shows improved accuracy without significant computational burden. The results show potential tracking performance with mean errors of < 3 degrees and < 1.5% of range. © 2016 SPIE.


Zhang L.,CAS Shenyang Institute of Automation | Zhang L.,University of Chinese Academy of Sciences | Zhu F.,CAS Shenyang Institute of Automation | Zhu F.,Chinese Academy of Sciences | And 6 more authors.
Applied Optics | Year: 2017

Pose estimation for spacecraft is widely recognized as an important technology for space applications. Many space missions require accurate relative pose between the chaser and the target spacecraft. Stereo-vision is a usual mean to estimate the pose of non-cooperative targets during proximity operations. However, the uncertainty of stereovision measurement is still an outstanding issue that needs to be solved. With binocular structure and the geometric structure of the object, we present a robust pose estimation method for non-cooperative spacecraft. Because the solar panel can provide strict geometry constraints, our approach takes the corner points of which as features. After stereo matching, an optimization-based method is proposed to estimate the relative pose between the two spacecraft. Simulation results show that our method improves the precision and robustness of pose estimation. Our system improves the performance with maximum 3D localization error less than 5% and relative rotation angle error less than 1°. Our laboratory experiments further validate the method. © 2017 Optical Society of America.


Zhang J.,CAS Shenyang Institute of Automation | Zhang J.,University of Chinese Academy of Sciences | Zhang J.,Chinese Academy of Sciences | Luo H.,CAS Shenyang Institute of Automation | And 10 more authors.
Optics Express | Year: 2017

Division of focal plane (DoFP) polarimeters are composed of interlaced linear polarizers overlaid upon a focal plane array sensor. The interpolation is essential to reconstruct polarization information. However, current interpolation methods are based on the unrealistic assumption of noise-free images. Thus, it is advantageous to carry out denoising before interpolation. In this paper, we propose a principle component analysis (PCA) based denoising method, which works directly on DoFP images. Both simulated and real DoFP images are used to evaluate the denoising performance. Experimental results show that the proposed method can effectively suppress noise while preserving edges. © 2017 Optical Society of America.


Zhang J.,CAS Shenyang Institute of Automation | Zhang J.,University of Chinese Academy of Sciences | Luo H.,CAS Shenyang Institute of Automation | Luo H.,Chinese Academy of Sciences | And 7 more authors.
Applied Optics | Year: 2016

Division of focal plane polarimeters are composed of nanometer polarization elements overlaid upon a focal plane array (FPA) sensor. The manufacturing flaws of the polarization grating and each detector in the FPA having a different photo response can introduce non-uniformity errors when reconstructing the polarization image without correction. A new calibration method is proposed to mitigate non-uniformity errors in the visible waveband. We correct non-uniformity in the form of a vector. The correction matrix and offset vector are calculated for the following correction. The performance of the proposed method is compared with state-of-the-art techniques by employing simulated data and real scenes. The experimental results showed that the proposed method can effectively mitigate non-uniformity errors and achieve better visual results. © 2016 Optical Society of America.


Zhang J.,CAS Shenyang Institute of Automation | Zhang J.,University of Chinese Academy of Sciences | Luo H.,CAS Shenyang Institute of Automation | Luo H.,Chinese Academy of Sciences | And 7 more authors.
Optics Express | Year: 2016

Division of focal plane (DoFP) polarimeters operate by integrating micro-polarizer elements with a focal plane. These polarization imaging sensors reduce spatial resolution output and each pixel has a varying instantaneous field of view (IFoV). These drawbacks can be mitigated by applying proper interpolation methods. In this paper, we present a new interpolation method for DoFP polarimeters by using intensity correlation. We employ the correlation of intensity measurements in different orientations to detect edges and then implement interpolation along edges. The performance of the proposed method is compared with several previous methods by using root mean square error (RMSE) comparison and visual comparison. Experimental results showed that our proposed method can achieve better visual effects and a lower RMSE than other methods. © 2016 Optical Society of America.


Liu Y.,CAS Shenyang Institute of Automation | Liu Y.,University of Chinese Academy of Sciences | Liu Y.,Chinese Academy of Sciences | Liu Y.,Key Laboratory of Image Understanding and Computer Vision | And 3 more authors.
Eurasip Journal on Advances in Signal Processing | Year: 2010

Region covariance descriptor recently proposed has been approved robust and elegant to describe a region of interest, which has been applied to visual tracking. We develop a geometric method for visual tracking, in which region covariance is used to model objects appearance; then tracking is led by implementing the particle filter with the constraint that the system state lies in a low dimensional manifold: affine Lie group. The sequential Bayesian updating consists of drawing state samples while moving on the manifold geodesics; the region covariance is updated using a novel approach in a Riemannian space. Our main contribution is developing a general particle filtering-based racking algorithm that explicitly take the geometry of affine Lie groups into consideration in deriving the state equation on Lie groups. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed tracking method. Copyright © 2010 Yunpeng Liu et al.


Dong S.,CAS Shenyang Institute of Automation | Dong S.,University of Chinese Academy of Sciences | Dong S.,Chinese Academy of Sciences | Dong S.,Key Laboratory of Image Understanding and Computer Vision | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

A high dynamic range infrared image of the sea surface scene includes the effects due to the sea clutter, mirror reflections from the wave facets, which decrease the visibility of the ship targets and their details. This paper provides an efficient adaptive enhancement technique for ship targets based on bilateral filtering and visual saliency detection. The 14- bit raw image is separated into a detail layer and a base layer by applying an adaptive bilateral filter. Then the two layers are processed separately and added afterward. Hereafter by employing visual saliency detection we can get the gain matrix to improve the contrast of ship targets. Finally, the image whose contrast is improved is quantized to the display range. The strength of our proposed method lies in its ability to inhibit the sea clutter and adaptability in different sea surface scene and shows a better performance in the contrast of the ship targets and the visibility of their details. © 2015 SPIE.


Chen H.,CAS Shenyang Institute of Automation | Chen H.,University of Chinese Academy of Sciences | Chen H.,Chinese Academy of Sciences | Chen H.,Key Laboratory of Image Understanding and Computer Vision | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

With the development of modern military, infrared imaging technology is widely used in this field. However, limited by the mechanism of infrared imaging and the detector, infrared images have the disadvantages of low contrast and blurry edge by comparison with the visible image. These shortcomings lead infrared image unsuitable to be observed by both human and computer. Thus image enhancement is required. Traditional image enhancement methods on the application of infrared image, without taking into account the human visual properties, is not convenient for the human observation. This article purposes a new method that combines the layering idea with the human visual properties to enhance the infrared image. The proposed method relies on bilateral filtering to separate a base component, which contains the large amplitude signal and must be compressed, from a detail component, which must be expanded because it contains the small signal variations related to fine texture. The base component is mapped into the proper range which is 8-bit using the human visual properties, and the detail component is applied the method of adaptive gain control. Finally, the two parts are recombined and quantized to 8-bit domain. Experimental results show that this algorithm exceeds most current image enhancement methods in solving the problems of low contrast and blurry detail. © 2015 SPIE.


Xu B.,CAS Shenyang Institute of Automation | Xu B.,University of Chinese Academy of Sciences | Xu B.,Chinese Academy of Sciences | Xu B.,Key Laboratory of Image Understanding and Computer Vision | And 7 more authors.
Guangxue Xuebao/Acta Optica Sinica | Year: 2011

Modulation transfer function (MTF) is one of significant parameters for designing and evaluating an electro-optical imaging system. By proving the linear relation between an edge spread function and an inverse cumulative histogram of edge, an MTF measurement method based on statistical histogram is presented. Simulation and experiment including optics and detectors are designed. Physical experimental platform including an integrating sphere, target, collimator, digital camera and other devices is developed. Taking the theoretical model and design parameters of electro-optical imaging system as a reference, the validity of our measurement method is verified by the simulation and experiment. Experimental results indicate that, comparing with the existing MTF measurement method, the proposed method can overcome the phase effects which is caused by under-sampling of imaging detector. It is more robust to angle of knife edge and noisy environment, and can measure response properties beyond the sampling frequency of an electro-optical imaging system.


Ma J.,CAS Shenyang Institute of Automation | Luo H.,University of Chinese Academy of Sciences | Chang Z.,Chinese Academy of Sciences | Hui B.,Key Laboratory of Image Understanding and Computer Vision
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via ℓ1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is effective and robust. © 2014 SPIE.

Loading Key Laboratory of Image Understanding and Computer Vision collaborators
Loading Key Laboratory of Image Understanding and Computer Vision collaborators