14 results on '"Sang, Nong"'
Search Results
2. Joint image restoration and matching method based on distance-weighted sparse representation prior.
- Author
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Shao, Yuanjie, Sang, Nong, Li, Yacheng, Li, Wenhao, and Gao, Changxin
- Subjects
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IMAGE reconstruction , *IMAGE registration , *IMAGE processing , *PATTERN matching - Abstract
• We propose a joint image restoration and matching method to deal with the problem of blurred image matching. • The approach utilizes distance-weighted sparse representation prior to exploit the correlation between restoration and matching. • The approach applies a coarse to fine strategy to improve the matching accuracy and reduce the search time. Image matching is a classic problem in the field of image processing, aims to locate the unique position of the real-time images and has been widely used in visual-based navigation systems. However, most of the works on image matching simply assume the ideal inputs without considering the degradation of the real world, such as image blur. In the presence of such a situation, the traditional matching methods usually first resort to image restoration and then perform image matching with the restored image. However, by treating the restoration and matching separately, the accuracy of blurred image matching will be reduced by the defective output of the image restoration. In this paper, we propose a joint blurred image restoration and matching method based on distance-weighted sparse representation (JRM-DSR), which utilizes the sparse representation prior to exploit the correlation between restoration and matching. This prior assumes that the blurred image, if correctly restored, can be well represented as a sparse linear combination of the dictionary constructed by the reference image. In order to achieve more accurate matching results to help image restoration, we consider both local and sparse information as well as adopt distance-weighted sparse representation to obtain better representation coefficients. By iterative restoring the input image in pursuit of the sparest representation, our approach can achieve restoration and match simultaneous, and these two tasks can benefit greatly from each other. Moreover, a coarse-to-fine matching strategy is proposed to further improve the matching accuracy and search efficiency. Experiments demonstrate the effectiveness of our method compared with conventional methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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3. A Method of Adjusting Visible-light Camera Oriented to Embedded Structures
- Author
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Sang Nong and Xu Yue
- Subjects
Brightness ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,Grayscale ,Entropy (information theory) ,Computer vision ,Artificial intelligence ,business ,Field-programmable gate array ,Digital signal processing ,Visible spectrum ,System software - Abstract
In order to achieve real-time adjustment of the camera and produce satisfactory images, an embedded system oriented approach, which involves DSP and FPGA is proposed. The new method, combining traditional methods of gray scale mean value and information entropy of images has been empirically proved to maximize the information carried by the image, and ensures a proper brightness of outputs, it also quantifies pre-processing with FPGA, and maintains real-time productivity of algorithm with the help of system software in DSP. As is indicated in the experiment with the target system, images under this method are obviously distinct.
- Published
- 2013
4. Attention-based hierarchical fusion of visible and infrared images.
- Author
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Chen, Yanfei and Sang, Nong
- Subjects
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IMAGE fusion , *INFRARED absorption , *IMAGE processing , *OPTICAL processors , *SPECTRUM analysis - Abstract
This paper presents a two-level hierarchical multi-resolution image fusion method based on attention, which focus on combining multi-sensor images from visible and infrared spectrums into an image while maintaining the salient information necessary for human visual. Attention-based one-level fusion employs adaptive weighting fusion rules to obtain the initial fused image which may being the boundary of saliency regions, then two-level fusion based on nonsubsampled contourlet transform (NSCT) is used to hide possible imprints of the initial fused image. Firstly, for the given visible and infrared images from the same scene, salient regions are detected by visual attention model. Secondly, salient sizes are acquired by calculating maximum entropy. Thirdly, one-level fusion is achieved by the adoptive weighing fusion rules. Various weight values are adopted according to the visual saliency order from high to low. The initial fused image contains not only the salient information of visible image, but also the salient thermal target information of infrared image. Finally, two-level fusion based on NSCT is employed to fuse the visible source image, the infrared source image and the initial fused image, and the final fused image is obtained. Two different data sets from TNO, Human Factors are employed and the experimental results show that the proposed approach is efficient and also performed better than some existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
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5. Spatial multi-scale gradient orientation consistency for place instance and Scene category recognition.
- Author
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Gao, Changxin, Sang, Nong, and Huang, Rui
- Subjects
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PATTERN recognition systems , *IMAGE processing , *FEATURE selection , *IMAGE representation , *MULTISCALE modeling - Abstract
This paper presents a novel image feature called Gradient Orientation Consistency (GOC) and an image representation based on the spatial multi-scale GOC histogram for place instance and scene category recognition. Gradient orientations have proven to be a more robust descriptor of the image information than intensities, in the presence of illumination variances. The GOC feature is obtained by imposing a Local Binary Pattern (LBP) type of non-parametric operator to the gradient orientation codes, hence captures the local structures in an image. The histogram of GOC further encodes the global structure of the entire image. The multi-scale analysis and spatial pyramid techniques are also used to enrich the longer-range structure information, resulting in a comprehensive image representation. We apply this representation method for place instance and scene category recognition on five different datasets and show that it outperforms the state of the art. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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6. Extraction of Salient Contours Via Excitatory-Inhibitory Interactions in the Visual Cortex.
- Author
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Narayanan, P. J., Nayar, Shree K., Shum, Heung-Yeung, Tang, Qiling, Sang, Nong, and Zhang, Tianxu
- Abstract
In this paper we mimic a biological visual strategy to extract salient contours from complex scenes. Psychophysical and physiological studies show that the response to the stimulus within the receptive field is affected by the presence of surrounding stimuli— the response is suppressed significantly by similarly oriented stimuli in the surround while this suppression is converted to strong facilitation with the addition of collinear stimuli in the surround. According to this property of visual perception, we enhance salient contours and at the same time reduce the interference of the extraneous elements. Our results show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2006
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7. Face recognition with Riesz binary pattern.
- Author
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Li, Jing, Sang, Nong, and Gao, Changxin
- Subjects
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HUMAN facial recognition software , *BINARY number system , *MONOGENIC functions , *SIGNAL processing , *IMAGE processing , *COMPUTER vision - Abstract
First-order Riesz transform based monogenic signal representation has been widely used in image processing and computer vision, however it only characterizes image intrinsic one-dimensional structure, and is incapable of describing intrinsic two-dimensional structure. To this end, a novel feature extraction approach, named Riesz Binary Pattern (RBP), is proposed for face recognition based on image multi-scale analysis and multi-order Riesz transform. RBP consists of two complementary components, i.e., local Riesz binary pattern (LRBP) and global Riesz binary pattern (GRBP). LRBP is obtained by performing local binary coding operator on each Riesz transform response to extract image intrinsic two-dimensional structure features. While GRBP is the global binary coding of joint information of image pixel multi-scale analysis and multi-order Riesz transform. Histogram of LRBP and GRBP are concatenated to form face image RBP description. Experimental results on three databases demonstrate that our proposed RBP descriptor is more discriminant in extracting image information and can provide a higher classification rate compared to some state-of-the-art image representation methods. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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8. Biologically Inspired Scene Context for Object Detection Using a Single Instance.
- Author
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Gao, Changxin, Sang, Nong, and Huang, Rui
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BIOSENSORS , *LIFE sciences , *COMPUTATIONAL biology , *SIGNAL processing , *IMAGE processing , *BIOTECHNOLOGY - Abstract
This paper presents a novel object detection method using a single instance from the object category. Our method uses biologically inspired global scene context criteria to check whether every individual location of the image can be naturally replaced by the query instance, which indicates whether there is a similar object at this location. Different from the traditional detection methods that only look at individual locations for the desired objects, our method evaluates the consistency of the entire scene. It is therefore robust to large intra-class variations, occlusions, a minor variety of poses, low-revolution conditions, background clutter etc., and there is no off-line training. The experimental results on four datasets and two video sequences clearly show the superior robustness of the proposed method, suggesting that global scene context is important for visual detection/localization. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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9. An improved LBP transfer learning for remote sensing object recognition.
- Author
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Dan, Zhiping, Sang, Nong, He, Yonggang, and Sun, Shuifa
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OBJECT recognition (Computer vision) , *RELIABILITY in engineering , *REMOTE-sensing images , *MACHINE learning , *PARAMETERS (Statistics) , *DIGITAL image processing - Abstract
Abstract: In the object recognition process of the remote sensing image, as the object data is influenced by the imaging scale, intensity and the shape of the object, the distribution of the object data is very different with that of known training data which leads to the low reliability of the object recognition. Aimed at the problem, an object recognition method based on transfer learning framework for the remote sensing image is proposed in this paper. The feature vectors of the object data are extracted by an improved LBP firstly, and then the transfer learning is used to find the common parameters among the feature spaces of the object data with the different distributions. The transfer learning method can transfer knowledge from the old object data to the new object data and improve the performance of the object recognition. According to the experiments in the satellite remote sensing images, it shows that the accuracy of object recognition has been greatly improved by our proposed method compared with the other classical methods. [Copyright &y& Elsevier]
- Published
- 2014
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10. Multi-ring local binary patterns for rotation invariant texture classification.
- Author
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He, Yonggang and Sang, Nong
- Subjects
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PATTERN recognition systems , *BINARY number system , *TEXTURE mapping , *ROBUST control , *ALGORITHMS , *IMAGE processing , *FEATURE extraction - Abstract
The local binary pattern (LBP) approach has been widely used in texture description. In this paper, we build a new framework to extract the binary patterns and propose a robust texture descriptor: multi-ring local binary pattern (MrLBP). The MrLBP algorithm creates patterns from several ringed areas and mainly contains two parts. One is the extra-ring local binary pattern operator that gets patterns from the mean values of different ringed areas. The other is the intra-ring local binary pattern operator that obtains patterns by counting the majority of binary values in every single ringed area. Moreover, the binary formation of each part of the MrLBP is obtained from two different aspects. The MrLBP method not only considers the binary relationship among pixels in a local region, but also focuses on the relationship between pixels in a local region and the whole image. This is a little different from the conventional LBP methods that only get the binary formation from the local gray scales differences. The experimental results on two public databases have validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
11. Image segmentation via coherent clustering in L∗a∗b∗ color space
- Author
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Huang, Rui, Sang, Nong, Luo, Dapeng, and Tang, Qiling
- Subjects
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DISCRIMINANT analysis , *IMAGE processing , *COMPUTER vision , *CLUSTER analysis (Statistics) , *PIXELS , *LATTICE theory , *ROBUST control , *ALGORITHMS - Abstract
Abstract: Automatic image segmentation is always a fundamental but challenging problem in computer vision. The simplest approach to image segmentation may be clustering feature vectors of pixels at first, then labeling each pixel with its corresponding cluster. This requires that the clustering on feature space must be robust. However, most of popular clustering algorithms could not obtain a robust clustering result yet, if the clusters in feature space have a complex distribution. Generally, for most of clustering-based segmentation methods, it still needs more constraints of positional relations between pixels in image lattice to be utilized during the procedure of clustering. Our works in this paper address the problem of image segmentation under the paradigm of pure clustering-then-labeling. A robust clustering algorithm which could maintain good coherence of data in feature space is proposed and utilized to do clustering on the L∗a∗b∗ color feature space of pixels. Image segmentation is straightforwardly obtained by setting each pixel with its corresponding cluster. Further, based on the theory of Minimum Description Length, an effective approach to automatic parameter selection for our segmentation method is also proposed. We test our segmentation method on Berkeley segmentation database, and the experimental results show that our method compares favorably against some state-of-the-art segmentation methods. [Copyright &y& Elsevier]
- Published
- 2011
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12. Knowledge-based adaptive thresholding segmentation of digital subtraction angiography images
- Author
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Sang, Nong, Li, Heng, Peng, Weixue, and Zhang, Tianxu
- Subjects
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DIGITAL subtraction angiography , *COMPUTER vision , *IMAGE processing , *ARTIFICIAL intelligence , *BLOOD vessels , *CONTRAST media - Abstract
Vessel segmentation is the base of three dimensional reconstruction on digital subtraction angiography (DSA) images. In this paper we propose two simple but efficient methods of vessel segmentation for DSA images. The original DSA image is divided into several appropriate subimages according to a prior knowledge of the diameter of vessels. We introduce the vessels existence measure to determine whether each subimage contains vessels and then choose an optimal threshold, respectively, for every subimage previously determined to contain vessels. Finally, an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments are implemented on cerebral and hepatic DSA images. The results demonstrate that our proposed methods yield better binary results than global thresholding methods and some other local thresholding methods do. [Copyright &y& Elsevier]
- Published
- 2007
- Full Text
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13. Contour detection based on contextual influences
- Author
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Tang, Qiling, Sang, Nong, and Zhang, Tianxu
- Subjects
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COMPUTER vision , *CONTEXTUAL analysis , *VISUAL cortex , *PATTERN recognition systems , *ARTIFICIAL intelligence , *IMAGE processing - Abstract
A contour detection model, inspired by the behavior of the primary visual cortex, is presented. The response of a central stimulus in the receptive field is affected by the presence of surrounding stimuli – for some stimulus conditions, the response is suppressed and for other conditions the response is enhanced. The visual mechanisms of contextual influences are utilized to extract “coherent” configurations. This is mainly due to the following two reasons: (1) on the one hand, a smooth contour can yield collinear excitation, which highlights smooth contours from irregularly textured surround; (2) on the other hand, similar orientation textures receive iso-orientation surround suppression and region boundary is subjected to the less inhibition, which makes boundary more salient for perceptual pop-out. Accordingly, smooth contours progressively stand out from their surround and at the same time textures are gradually suppressed by their surround through dynamic fine-tuning of contextual information. The proposed method which distinguishes between contours and texture edges is more effective for contour-based object recognition tasks. Initial experiments show that the model can be successfully applied to contour detection. Especially, when object contours are lumped together with unwantedly cluttered surround, the advantage of our approach is more prominent. This study provides a biological scheme for contour detection in computer vision. [Copyright &y& Elsevier]
- Published
- 2007
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14. Performance analysis on a watermarking method based on double random phase encoding technique.
- Author
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Sang, Jun, Zhang, Bing, Hong, Daxiang, Xiang, Hong, Xu, Hongsheng, and Sang, Nong
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PERFORMANCE evaluation , *DIGITAL watermarking , *EMBEDDED computer systems , *ITERATIVE methods (Mathematics) , *IMAGE processing , *COMPUTER simulation - Abstract
Abstract: When digital watermarking is used for piracy tracking, different watermarks are needed to be embedded into different distributions of a digital product. Based on double random phase encoding (DRPE) technique, cascaded-phases iterative algorithm and random-phase-shift algorithm, Chen et al. proposed a method to generate many different embedded watermarks from one reference watermark, while the embedded watermark can be recognized by testing the correlation between the recovered watermark and the original reference watermark. In this way, only the reference watermark instead of the embedded watermarks needs to be stored and managed. However, since the recovered watermark may be different from the embedded watermark, especially under any image processing, the correlation between the recovered watermark and the reference watermark may be different from the correlation between the embedded watermark and the reference watermark, which may result in wrong recognition. In this paper, the performance of Chen's method was analyzed with numerical simulations. The results indicated that, to correctly recognize the embedded watermark, the number of generated embedded watermarks with Chen's method is limited. [Copyright &y& Elsevier]
- Published
- 2013
- Full Text
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