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RISS 인기검색어
- 검색키워드
- 저자 : Cuong Cao Pham (검색결과 5 건)
- 무료
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- 유료
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Cuong Cao Pham,Thuy Tuong Nguyen,Jae Wook Jeon 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
Automatic long-term measuring wastewater velocity is an important and challenging task in hydraulic systems. This paper proposed a vision-based wastewater velocity measurement method using Bilateral filter that is a discontinuitypreserving smoothing as a prior-processing step. Experimental results showed that using Bilateral filter can improve estimation accuracy over existing methods. An effective background creation algorithm and simple floating waste tracking algorithm based on binary blob properties are also discussed in this paper. Furthermore, by implementing the proposed method on massively parallel GPU (graphics processing units) using the CUDA (compute unified device architecture) programming model, we can achieve a satisfactory acceleration to apply in real-time applications. Memory usage optimization methods are discussed and analyzed for effective implementation in graphics hardware.
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Efficient image sharpening and denoising using adaptive guided image filtering
Cuong Cao Pham,Jae Wook Jeon IET 2015 IET image processing Vol.9 No.1
<P>Enhancing the sharpness and reducing the noise of blurred, noisy images are crucial functions of image processing. Widely used unsharp masking filter-based approaches suffer from halo-artefacts and/or noise amplification, while noise- and halo-free adaptive bilateral filtering (ABF) is computationally intractable. In this study, the authors present an efficient sharpening algorithm inspired by guided image filtering (GF). The author's proposed adaptive GF (AGF) integrates the shift-variant technique, a part of ABF, into a guided filter to render crisp and sharpened outputs. Experiments showed the superiority of their proposed algorithm to existing algorithms. The proposed AGF sharply enhances edges and textures without causing halo-artefacts or noise amplification, and it is efficiently implemented using a fast linear-time algorithm.</P>
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Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching
Cuong Cao Pham,Jae Wook Jeon IEEE 2013 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDE Vol.23 No.7
<P>Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem of these algorithms since the computational complexity is proportionally related to the support window size. In this paper, we present a novel cost aggregation method inspired by domain transformation, a recently proposed dimensionality reduction technique. This transformation enables the aggregation of 2-D cost data to be performed using a sequence of 1-D filters, which lowers computation and memory costs compared to conventional 2-D filters. Experiments show that the proposed method outperforms the state-of-the-art local methods in terms of computational performance, since its computational complexity is independent of the input parameters. Furthermore, according to the experimental results with the Middlebury dataset and real-world images, our algorithm is currently one of the most accurate and efficient local algorithms.</P>
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Robust Adaptive Normalized Cross-Correlation for Stereo Matching Cost Computation
Dinh, Vinh Quang,Pham, Cuong Cao,Jeon, Jae Wook Institute of Electrical and Electronics Engineers 2017 IEEE transactions on circuits and systems for vide Vol.27 No.7
<P>Stereo matching is a challenging task because stereo images are affected by many factors, such as radiometric distortion, sun and rain flare, flying snow, occlusion, textureless and noisy image regions, and object boundaries. However, most of the existing methods for stereo matching aim to solve only one specific problem. As a result, their performance is degraded significantly when operating with stereo images captured under a variety of scenes and conditions. In this paper, we propose a novel matching cost function based on adaptive normalized cross-correlation (ANCC). We demonstrate several weaknesses of ANCC and propose techniques to resolve them. In addition, we employ available information, such as intensity mean, intensity variance, and support window radius, to estimate the parameters of the proposed matching cost function. Compared with ANCC, the proposed matching cost function reduces the error rates from 24.1% to 17.8% in the Middlebury data set and from 64.1% to 26.4% in the KITTI data set. In addition, for noisy stereo pairs, the proposed function reduces the error rate from 73.6% to 37.3%. The qualitative and quantitative experimental results based on stereo images in different data sets under various conditions show that our proposed matching cost function outperforms state-of-the-art matching cost functions in indoor and outdoor stereo images having various radiometric distortions.</P>
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Matching cost function using robust soft rank transformations
Dinh, Vinh Quang,Pham, Cuong Cao,Jeon, Jae Wook IET 2016 IET image processing Vol.10 No.7
<P>Stereo correspondence is a challenging task because stereo images are affected by many factors such as radiometric distortion, sun and rain flares, flying snow, occlusions and object boundaries. However, most of the existing stereo correspondence methods use simple matching cost functions. As a result, their performance is degraded significantly when operating with real-world stereo images whose intensities of corresponding pixels can be arbitrarily transformed. In this study, the authors propose a novel matching cost function based on the order relations between pixel pairs that can operate accurately under various conditions of transformed intensities between stereo images. The proposed matching cost function is an improvement of the soft rank transform (SRT) and can tolerate local, monotonically non-linear changes in intensities between the left and right images. The proposed function significantly reduces the error rate from 24.7 to 12.7% in the Middlebury dataset, and from 19.8 to 7.1% in the KITTI dataset as compared with the SRT. The qualitative and quantitative experimental results obtained using stereo images in different datasets under various conditions show that the proposed matching cost function outperforms the state-of-the-art matching cost functions in indoor and outdoor stereo images.</P>
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