Search

SUMMARY The aim of this randomized controlled clinical trial was to compare the clinical effectiveness of different polishing systems and self-etch adhesives in class V composite resin restorations. A total of 164 noncarious cervical lesions (NCCLs) from 35 patients were randomly allocated to one of four experimental groups, each of which used a combination of polishing systems and adhesives. The two polishing systems used were Sof-Lex XT (Sof), a multistep abrasive disc, and Enhance/Pogo (EP), a simplified abrasive-impregnated rubber instrument. The adhesive systems were Clearfil SE bond (CS), a two-step self-etch adhesive, and Xeno V (XE), a one-step self-etch adhesive. All NCCLs were restored with light-cured microhybrid resin composites (Z250). Restorations were evaluated at baseline and at 6, 12, 18, and 24 months by two blinded independent examiners using modified FDI criteria. The Fisher exact test and generalized estimating equation analysis considering repeated measurements were performed to compare the outcomes between the polishing systems and adhesives. Three restorations were dislodged: two in CS/Sof and one in CS/EP. None of the restorations required any repair or retreatment except those showing retention loss. Sof was superior to EP with regard to surface luster, staining, and marginal adaptation (p0.05). Sof is clinically superior to EP for polishing performance in class V composite resin restoration. XE demonstrates clinically equivalent bonding performance to CS.

In this paper, we propose a new pattern model, called the structural binary pattern (SBP) model, for object tracking. For the proposed SBP model, we introduce an alternate thresholding scheme to generate a set of multiple SBPs. The SBP encodes not only the binary pattern consisting of binarized differences between the average intensities of subregions within the target region, but also the spatial configuration of the subregions. With the proposed SBP model, we define a metric for similarity between the SBP models from the target and candidate for target localization, which is based on an isotropic kernel weighted Hamming distance. To further improve the tracking performance, we employ a color-based tracking method along with the SBP-based tracking method. The experimental results show that the proposed algorithm exhibits the better performance even when the object being tracked confronts drastic illumination changes, partial occlusion, a similar colored background, or low illumination as compared with conventional tracking methods.

Background The association between obesity and response to neoadjuvant chemotherapy in breast cancer patients is not clear. We evaluated the impact of obesity on response to neoadjuvant chemotherapy in patients with operable breast cancer. Methods From May 2008 to December 2010, 104 patients were diagnosed with invasive breast cancer at Korea University Anam Hospital and received neoadjuvant chemotherapy before surgery. Patients were classified into those of normal (BMI of 18.5 to Results Median age was 45 years. Mean BMI was 24.8 kg/m2; 53.8% had a normal BMI, 35.6% overweight, and 10.6% of patients was obese. BMI did not show a significant association with ER status, PR status, HER-2 status, lymph node involvement and neoadjuvant chemotherapy regimen. In univariate analysis, overweight and obese patients were significantly less likely to have a pCR compared with normal weight patients (odds ratio [OR] = 0.300; 95% CI, 0.115 to 0.784; p = 0.010). In multivariate analysis, ER negativity was significantly associated with a pCR and pPR to neoadjuvant chemotherapy (OR = 2.987; 95% CI, 1.128 to 7.907; p = 0.028), And there was significant difference in pCR for overweight and obese compared with normal weight patients (OR = 0.304;95% CI, 0.115 to 0.803; p = 0.016). Conclusion This study suggests that higher BMI should be considered to be a factor of worse response to neoadjuvant chemotherapy in patients with operable breast cancer. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-22.

In this paper, we propose a new color histogram model for object tracking. The proposed model incorporates the color arrangement of the target that encodes the relative spatial distribution of the colors inside the object. Using the color arrangement, we can determine which color bin is more reliable for tracking. Based on the proposed color histogram model, we derive a mean shift framework using a modified Bhattacharyya distance. In addition, we present a method of updating an object scale and a target model to cope with changes in the target appearance. Unlike conventional mean shift based methods, our algorithm produces satisfactory results even when the object being tracked shares similar colors with the background.

This paper presents a new image stitching algorithm for mobile multimedia devices. In general, the stitched image obtained by optimal seam finding can avoid the ghost effects but sometimes experience the unconformity of the image structure and color differences near the transition region between images. To solve these problems, the proposed algorithm adopts the seam carving/inserting operator and exploits an adaptive color blending algorithm. Experimental results show that the proposed algorithm can produce a stitched image without visible artifacts including the ghost effects, the unconformity of the structure, and the visible color differences. With respect to the visual quality of the stitched images, the proposed algorithm significantly outperforms simple conventional stitching algorithms and is comparable to the computationally demanding Poisson image editing algorithm1.

In this paper, we introduce an algorithm for object tracking in video sequences. In order to represent the object to be tracked, we propose a new spatial color histogram model which encodes both the color distribution and spatial information. Using this spatial color histogram model, a voting method based on the generalized Hough transform is employed to estimate the object location from frame to frame. The proposed voting based method, called the center voting method, requests every pixel near the previous object center to cast a vote for locating the new object center in the new frame. Once the location of the object is obtained, the back projection method is used to segment the object from the background. Experiment results show successful tracking of the object even when the object being tracked changes in size and shares similar color with the background.

The scalable video coding (SVC) standard adopts the inter-layer residual prediction (ILRP) algorithm to encode the residual signal of the enhancement layer (EL). The ILRP reduces the number of bits required for encoding the residual signal but incurs excessive encoding time. In this paper, we propose a fast encoding algorithm for SVC-based video streaming. In this algorithm, the ILRP is selectively applied to the coding modes depending on their Lagrangian rate-distortion costs. Experimental results show that the proposed algorithm can reduce computational complexity with negligible video quality degradation and bitrate increments.

In this paper, we propose a new post-processing method, based on the theory of the projection onto convex sets (POCS) to reduce the in digital high definition television (HDTV) decoded images. We propose a new smoothness constraint set (SCS) and its projection operator in the wavelet transform (WT) domain to remove unnecessary high-frequency components caused by blocking artifacts. We also propose a new method to find and preserve the original high frequency components of the image edge. Experimental results show that the proposed method can not only achieve a significantly enhanced subjective quality, but also exhibit the PSNR improvement in the output image.

A driver assistance system typically adopts the wide-angle camera to obtain a wide-view image. However, the wide-angle camera often produces radial distortion. Since the conventional training-based pedestrian detection method uses distortion-free training samples, it is not suitable for distorted images. In this paper, we propose an effective pedestrian detection method that divides pedestrian training samples into several classes according to the amount of radial distortion, and trains each class separately. Likewise, a test image is divided into sub-regions and detection is performed for each sub-region separately. Experimental results show that our approach provides better performance compared to the conventional method.

In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos., {"references":["A. Yilmaz, O. Javed, and M. Shah, \"Object Tracking: A survey,\" ACM\nComputing Surveys, vol. 38, no. 4, pp. 13, 2006.","M. Isard and A. Blake, \"CONDENSATION - Conditional Density Propagation\nfor Visual Tracking,\" International Journal of Computer Vision,\nvol. 29, no. 1, pp. 5-28, August 1998.","Y. Shi and W. C. Karl, \"Real-Time Tracking Using Level Sets,\" Proc.\nIEEE Computer Vision and Pattern Recognition, vol. 2, pp. 34-41, June\n2005.","J. Shi and C. Tomasi, \"Good Features to Track,\" Proc. IEEE Computer\nVision and Pattern Recognition, pp. 593-600, 1994.","C. Tomasi and T. Kanade, \"Detection and Tracking of Point Features,\"\nTechnical Report CMU-CS-91132, Pittsburgh:Carnegie Mellon University\nSchool of Computer Science, April 1991.","D. G. Lowe, \"Distinctive Image Features from Scale-Invariant Keypoints,\"\nInternational Journal of Computer Vision, vol. 60, 1999, pp. 91-110,\nNovember 2004.","H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, \"SURF: Speeded Up\nRobust Features,\" Computer Vision and Image Understanding, vol. 110,\nno. 3, pp. 346-359, June 2008.","G.R. Bradski, \"Real Time Face and Object Tracking as A Component of\nA Perceptual User Interface,\" Applications of Computer Vision, pp. 214\n- 219, 1998.","D. Comaniciu, V. Ramesh, and P. Meer, \"Kernel-based Object Tracking,\"\nIEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no.\n5, May 2003.\n[10] S. T. Birchfield and S. Rangarajan, \"Spatiograms versus Histograms\nfor Region-Based Tracking,\" Proc. IEEE Computer Vision and Pattern\nRecognition, vol. 2, pp. 1158-1163, June 2005.\n[11] Q. Zhao and H. Tao, \"Object Tracking Using Color Correlogram,\" Proc.\nIEEE Visual Surveillance and Performance Evaluation of Tracking and\nSurveillance, pp. 263-270, October 2005.\n[12] B.D. Lucas and T. Kanade, \"An Iterative Image Registration Technique\nwith an Application to Stereo Vision,\" Proc. of the 7th International Joint\nConference on Artificial Intelligence, Vancouver, pp. 674-679, 1981.\n[13] R. T. Collins, \"Mean-shift Blob Tracking through Scale Space,\" Proc.\nIEEE Computer Vision and Pattern Recognition, vol. 2, pp. 234-240,\n2003.\n[14] C. Stauffer and W.E.L. Grimson, \"Adaptive Background Mixture Models\nfor Real-Time Tracking,\" Proc. IEEE Computer Vision and Pattern\nRecognition, vol. 2, pp. 246-252, June 1999.\n[15] A. Elgammal, D. Harwood, and L.S. Davis, \"Non-parametric Model for\nBackground Subtraction,\" European Conference on Computer Vision, vol.\n2, pp. 751-767, 2000.\n[16] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, \"Real-Time\nForeground Background Segmentation Using Codebook Model,\" Real-\nTime Imaging, vol. 11, no. 3, pp. 172-185, June 2005."]}

In this paper, an efficient multi-scale level set method is proposed to track a non-rigid object contour in real time. The proposed algorithm consists of two cycles which are carried out in different scale domains. In the first cycle on the coarse scale, the algorithm evolves the contour depending on the observed data. In the other cycle on the fine scale, the smoothness regularization is imposed on the curve. In order to maintain the curve data during the scale transition, we propose a simple pattern to interpolate empty grids effectively. In addition, we propose a position prediction scheme so as to accelerate the convergence rate of the algorithm. Experimental results show that dramatic speedup is achieved on video tracking experiments without the performance degradation.

In this paper, we propose an efficient encoding scheme in scalable video coding (SVC) for the video transmission over various networks. In order to improve the encoding efficiency, we adopt the spatio-temporal motion vector (MV) prediction method in the motion compensated temporal filtering (MCTF). Experimental results show that the proposed method outperforms the conventional method in terms of compression ratio.

This paper presents a improved POCS-based deblocking technique, based on the theory of the projection onto convex sets (POCS) to reduce the blocking artifacts in decoded images. We propose a new smoothness constraint set (SCS) and its projection operator in the wave-let transform (WT) domain to remove unnecessary high-frequency components caused by blocking artifacts. In order to eliminate the blocking artifacts component while preserving the original edge component, we also propose a significant coefficient decision method (SCDM)for fast and efficient performance. Experimental results show that the proposed method can not only achieve a significantly enhanced subjective quality but also increase the PSNR improvement in the reconstructed image.

This paper presents an improved motion vector (MV) prediction scheme in scalable video coding (SVC) to increase coding efficiency. In order to improve the accuracy of prediction, we adopt the spatio-temporal prediction. Experimental results show that the proposed method outperforms the conventional prediction methods.

We introduce a new deblocking algorithm that can remove the blockiness without blurring image details. After modeling the blocking artifacts as well as the real edges, the proposed algorithm controls the suppression of the blocking artifact based on the human visual system. Through simulations, we show that our algorithm can successfully reduces the blocking artifacts without excessive blurring.