3077757
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
The identification of near-duplicate keyframe (NDK) pairs is a useful task for a variety of applications such as news story threading and content-based video search. In this paper, we propose a novel approach for the discovery and tracking of NDK pairs and threads in the broadcast domain. The detection of NDKs in a large data set is a challenging task due to the fact that when the data set increases linearly, the computational cost increases in a quadratic speed, and so does the number of false alarms. This paper explores the symmetric and transitive nature of near-duplicate for the effective detection and fast tracking of NDK pairs based upon the matching of local keypoints in frames. In the detection phase, we propose a robust measure, namely pattern entropy (PE), to measure the coherency of symmetric keypoint matching across the space of two keyframes. This measure is shown to be effective in discovering the NDK identity of a frame. In the tracking phase, the NDK pairs and threads are rapidly propagated and linked with sitivity without the need of detection. This step ends up a significant boost in speed efficiency. We evaluate proposed approach against a month of the 2004 broadcast videos. The experimental results indicate our approach outperforms other techniques in terms of recall and precision with a large margin. In addition, by considering the transitivity and the underlying distribution of NDK pairs along time span, a speed up of 3 to 5 times is achieved when keeping the performance close enough to the optimal one obtained by exhaustive evaluation.
15612889
Keyframe Retrieval by Keypoints: Can Point-to-Point Matching Help?
Bag-of-words representation with visual keypoints has recently emerged as an attractive approach for video search. In this paper, we study the degree of improvement when point-to-point (P2P) constraint is imposed on the bag-of-words. We conduct investigation on two tasks: near-duplicate keyframe (NDK) retrieval, and high-level concept classification, covering parts of TRECVID 2003 and 2005 datasets. In P2P matching, we propose a one-to-one symmetric keypoint matching strategy to diminish the noise effect during keyframe comparison. In addition, a new multi-dimensional index structure is proposed to speed up the matching process with keypoint filtering. Through experiments, we demonstrate that P2P constraint can significantly boost the performance of NDK retrieval, while showing competitive accuracy in concept classification of broadcast domain.
62556261
An enhanced appearance model for ultrasound image segmentation
Active appearance model (AAM) had been popular on object segmentation for medical images. However, its performance is not good on ultrasound (US) images. In this paper, we propose an enhanced appearance model which represents the texture by edge structure and reduces the view-dependent feature of US images by a novel data normalization process. In our experiments on general US data, the proposed model shows considerable improvements compared to the original one.
26210485
Robust Registration of 3-D Ultrasound Images Based on Gabor Filter and Mean-Shift Method
A novel robust method is presented for the registration of 3-D ultrasound images. The proposed method improves the performance of the voxel property-based affine registration in two aspects. First, a set of wavelet-like Gabor filters is used to extract the texture and edge features of the voxels. By using these features, the smoothness of the similarity function in large scale can be improved. Furthermore, adopting edge information can improve the registration accuracy. Second, a robust maximization method based on the mean-shift algorithm and Powell’s direction set method is proposed. The implicitly embedded smoothing process of the mean-shift algorithm can effectively remove the local fluctuation of the similarity function and significantly improve the robustness of optimization. Experimental results demonstrate the robust and accurate performance of the proposed method in the registration of 3-D ultrasound fetal head images.
119964309
Geodesic closest point constrained inter-subject non-rigid registration
In this paper, we propose an inter-subject brain registration method by combining the intensity and the geodesic closest point based similarity metric. Each of the brain hemispheres can be topologically equalized into a sphere. The inter subject variance can be better reduced by using cortical surface based analysis method. A one to one mapping of the points on spherical surfaces of two subjects can be achieved by using this technique. We find the geodesic correspondence between subjects by using spherical registration first, then the correspondence on the cortical surface between subjects are used to guide the volumetric inter-subject registration. By adding these anatomical constraints of the cortical surface, the inter-subject registration result is more anatomically meaningful and accurate. The cortical surface correspondence between subjects can be combined with the general non-rigid registration. In our experiments, the proposed method performs better than the method of Hartkens et al.
2341708
Segmentation of kidney from ultrasound images based on texture and shape priors
This work presents a novel texture and shape priors based method for kidney segmentation in ultrasound (US) images. Texture features are extracted by applying a bank of Gabor filters on test images through a two-sided convolution strategy. The texture model is constructed via estimating the parameters of a set of mixtures of half-planed Gaussians using the expectation-maximization method. Through this texture model, the texture similarities of areas around the segmenting curve are measured in the inside and outside regions, respectively. We also present an iterative segmentation framework to combine the texture measures into the parametric shape model proposed by Leventon and Faugeras. Segmentation is implemented by calculating the parameters of the shape model to minimize a novel energy function. The goal of this energy function is to partition the test image into two regions, the inside one with high texture similarity and low texture variance, and the outside one with high texture variance. The effectiveness of this method is demonstrated through experimental results on both natural images and US data compared with other image segmentation methods and manual segmentation.
35554419
An Adaptive Enhancement Method for Ultrasound Images
In this paper, we present a novel adaptive method for ultrasound (US) image enhancement. It is based on a new measure of perceptual saliency and the view-dependent feature on US images. By computing this measure on an US image, speckle noise is reduced and perceptual salient boundaries of organs are enhanced. Because of the curvature gradient based saliency measure, this method can enhance more types of salient structures than the well-known saliency network method. Meanwhile, the proposed method does not depend on the closure measure. This makes it more appropriate to enhance real images than other existing methods. Moreover, the local analysis of speckle patterns leads a good performance in speckle reduction for US images. Experimental results show the proposed enhancement approach can provide a good assistant for US image segmentation and image-guided diagnosis.