Quan Miao

Tsinghua University, Peping, Beijing, China

Are you Quan Miao?

Claim your profile

Publications (7)1.83 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.
    IEICE Transactions on Information and Systems 01/2013; E96.D(1):159-162. DOI:10.1587/transinf.E96.D.159 · 0.19 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Object tracking is a major technique in image processing and computer vision. Tracking speed will directly determine the quality of applications. This paper presents a parallel implementation for a recently proposed scale- and rotation-invariant on-line object tracking system. The algorithm is based on NVIDIA's Graphics Processing Units (GPU) using Compute Unified Device Architecture (CUDA), following the model of single instruction multiple threads. Specifically, we analyze the original algorithm and propose the CPU-based parallel design. Emphasis is placed on exploiting the data parallelism and memory usage. In addition, we apply optimization technique to maximize the utilization of NVIDIA's GPU and reduce the data transfer time. Experimental results show that our GPGPU-based method running on a GTX480 graphics card could achieve up to 12X speed-up compared with the efficiency equivalence on an Intel E8400 3.0 GHz CPU, including I/O time.
    IEICE Transactions on Information and Systems 12/2011; 94-D(12):2549-2552. DOI:10.1587/transinf.E94.D.2549 · 0.19 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We present a new object tracking scheme by employing adaptive classifiers to match the corresponding keypoints between consecutive frames. The detection of interest points is a critical step in obtaining robust local descriptions. This paper proposes an efficient feature detector based on SURF, by incrementally predicting the search space, to enhance the repeatability of the tracked interest points. Instead of computing the SURF descriptor, we construct a classifier-based descriptor using on-line boosting. With on-line learning ability based on our sample weighting mechanism, the classifier maintains its discriminative power to establish robust feature description and reliable points matching for subsequent tracking. In addition, matching candidates are validated using improved RANSAC to ensure correct updates and accurate tracking. All of these ingredients contribute measurably to improving overall tracking performance. Experimental results demonstrate the robustness and accuracy of our proposed technique.
    Pattern Recognition Letters 10/2011; 32(13):1564-1571. DOI:10.1016/j.patrec.2011.05.017 · 1.06 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Robust local image features have become crucial components of many state-of-the-art computer vision algorithms. Due to limited hardware resources, computing local features on embedded system is not an easy task. In this paper, we propose an efficient parallel computing framework for speeded-up robust features with an orientation towards multi-DSP based embedded system. We optimize modules in SURF to better utilize the capability of DSP chips. We also design a compact data layout to adapt to the limited memory resource and to increase data access bandwidth. A data-driven barrier and workload balance schemes are presented to synchronize parallel working chips and reduce overall cost. The experiment shows our implementation achieves competitive time efficiency compared with related works.
    IEICE Transactions on Information and Systems 04/2011; 94-D(4):930-933. DOI:10.1587/transinf.E94.D.930 · 0.19 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper introduces a topology based affine invariant descriptor for maximally stable extremal regions (MSERs). The popular SIFT descriptor computes the texture information on a grey-scale patch. Instead our descriptor use only the topology and geometric information among MSERs so that features can be rapidly matched regardless of the texture in the image patch. Based on the ellipses fitting for the detected MSERs, geometric affine invariants between ellipses pair are extracted as the descriptors. Finally topology based voting selector is designed to achieve the best correspondences. Experiment shows that our descriptor is not only computational faster than SIFT descriptor, but also has better performance on wide angle of view and nonlinear illumination change. In addition, our descriptor shows a good result on multi sensor images registration.
    Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
  • [Show abstract] [Hide abstract]
    ABSTRACT: Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.
    IEICE Transactions on Information and Systems 05/2010; 93-D(5):1317-1320. DOI:10.1587/transinf.E93.D.1317 · 0.19 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Object tracking is a major technique in image processing and computer vision. In this paper, we propose a new robust feature-based tracking scheme by employing adaptive classifiers to match the detected keypoints in consecutive frames. The novelty of this paper is that the design of online boosting is combined with the invariance of local features so that the classifier-based descriptions are formed in association with the scale and rotation information. Furthermore, we introduce a sample weighting mechanism in the on-line classifier updating, for the subsequent tracking. Experimental results demonstrate the robustness and accuracy of our proposed technique.
    Proceedings of the International Conference on Image Processing, ICIP 2010, September 26-29, Hong Kong, China; 01/2010