Showing papers by "Yap-Peng Tan published in 2007"

Adaptive Filtering for Color Filter Array Demosaicking

Abstract: Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and 2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.

People counting using iterative mean-shift fitting with symmetry measure

Abstract: This paper presents a new method for counting the number of persons from video images. Conventional people counting methods can be classified into the learning-tracking based techniques. They either require elaborated human model learned using AdaBoost or sophisticated tracking algorithms by particle filtering. The proposed algorithm performs people counting by segmenting group of people in a cluttered scene into individuals. By assuming human body is bilaterally symmetric, the proposed method first determines a probability map of symmetry using a local energy function. Taking the highest symmetry pixel as the initial position, our method then employs the mean-shift technique to fit each person in the foreground. The procedure of mean-shift fitting is repeated until all the foreground regions are exhausted. The advantages of the proposed method lie in its simplicity and capability of counting multiple human targets without stringent learning and tracking constraints. Experimental results show that the proposed method is robust and efficient in handling real-life video data of different scenarios.

Evolution and maintenance of frequent pattern space when transactions are removed

Abstract: This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.

Retinal Vessel Detection using Self-Matched Filtering

Abstract: Automated analysis of retinal images usually requires estimating the positions of blood vessels, which contain important features for image alignment and abnormality detection. Matched filtering can produce the best results but is difficult to implement because the vessel orientations and widths are unknown beforehand. Many researchers use Hessian filtering, which provides an estimate for vessel orientation through the use of three orientation templates. We propose a novel filtering approach, called self-matched filtering, which is based on the 180deg rotated version of the noisy vessel signal in the local neighborhood. We show that even though the proposed filter achieves half the signal-to-noise ratio of a matched filter, it does not require the estimation of the vessel scale and orientation, and can outperform Hessian filtering by up to a factor of two in terms of miss detection error.

Video adaptation for heterogeneous environments

Abstract: The explosive growth of compressed video streams and repositories accessible worldwide, the recent addition of new video-related standards such as H.264/AVC, MPEG-7, and MPEG-21, and the ever-increasing prevalence of heterogeneous, video-enabled terminals such as computer, TV, mobile phones, and personal digital assistants have escalated the need for efficient and effective techniques for adapting compressed videos to better suit the different capabilities, constraints, and requirements of various transmission networks, applications, and end users. For instance, Universal Multimedia Access (UMA) advocates the provision and adaptation of the same multimedia content for different networks, terminals, and user preferences. Video adaptation is an emerging field that offers a rich body of knowledge and techniques for handling the huge variation of resource constraints (e.g., bandwidth, display capability, processing speed, and power consumption) and the large diversity of user tasks in pervasive media applications. Considerable amounts of research and development activities in industry and academia have been devoted to answering the many challenges in making better use of video content across systems and applications of various kinds. Video adaptation may apply to individual or multiple video streams and may call for different means depending on the objectives and requirements of adaptation. Transcoding, transmoding (cross-modality transcoding), scalable content representation, content abstraction and summarization are popular means for video adaptation. In addition, video content analysis and understanding, including low-level feature analysis and high-level semantics understanding, play an important role in video adaptation as essential video content can be better preserved. The aim of this special issue is to present state-of-theart developments in this flourishing and important research field. Contributions in theoretical study, architecture design, performance analysis, complexity reduction, and real-world applications are all welcome. Topics of interest include (but are not limited to):

Error Inhomogeneity in Wavelet-Based Compression

Abstract: Despite the popularity of wavelet-based image compression, its shortcoming of having error inhomogeneity (EI), namely the error that is different for even and odd pixel location, has not been previously analyzed and formally addressed. The difference can be substantial, up to 3.4-dB peak signal-to-noise ratio (PSNR) for some images and compression ratios. In this paper, we show that the EI is caused by asymmetrical filtering of quantization errors after the upsampling step in wavelet synthesis process. Nonuniformity and correlation of quantization errors can also contribute to this EI, albeit to a smaller degree. In addition to explaining the source of EI, the model we developed in this paper also allows predicting its amount for a given wavelet. Furthermore, we show how to redesign wavelet filters to reduce this EI at a cost of a small reduction in the overall PSNR performance. For applications that are sensitive to PSNR degradation, we also show how to design wavelet filters that can gradually tradeoff PSNR performance for reduced EI.

Detection and Removal of Rainboweffect Artifacts

Abstract: Due to the imperfect separation of luminance and chroma signals in receiver's demodulation, composite video signals suffer from rainbow effect artifacts, which present themselves as interlaced color stripes in regions of high luminance frequency and high luminance intensity. In this paper, we derive the formulas governing the rainbow effects. Based on these formulas, we propose a novel method to detect and remove these annoying artifacts. Experimental results on both captured video frames and simulated frames show that our method can remove the rainbow-effect artifacts effectively and improve the image quality notably.

An Efficient and Effective Color Filter Array Demosaicking Method

Abstract: To reduce the cost and size, most digital still cameras (DSCs) capture only one color value at each pixel, and the results - color filter array samples - are then interpolated by a demosaicking method to construct a full-color image. Many advanced demosaicking methods have been proposed recently. However, the high complexity of these methods could prevent them from being used in DSCs. In this paper we propose an efficient and effective demosaicking method, which substitutes high-frequency component of color values in the spatial rather than frequency domain. We also propose a simple ternary, anisotropic interpolation scheme to obtain an initial full-color image required in the spatial-domain high-frequency substitution. Experimental results show that the proposed method can outperform recent state-of-the-art methods in terms of both PSNR performance and perceptual results, at the same time reducing the computational cost substantially.

Robust color object tracking with application to people monitoring

Abstract: We present an automated and complete camera-based monitoring system that makes use of low-level color features to perform detection, tracking and recognition of multiple people in video sequence. Specifically, the system employs a novel coverage check-up method to segment detected foreground regions into isolated people and then localize each of them. During tracking, the appearances of people are modeled by their color histograms so that the system can keep aware of their identities and recognize them after occlusions by maximizing the joint likelihood. To make the recognition more robust against shadows or changes of background illumination, the system also incorporates a shadow removal scheme to suppress shadow effects and hence improve the quality of color histogram. The proposed system has been used to identify people who re-enter the field of view of a monitoring camera in a closed-environment. Experimental results of real video data demonstrate the efficacy of the proposed people monitoring system.