다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

Traditional and recent approaches in background modeling for foreground detection: An overview

Compared with traditional mono-view video, three-dimensional video (3DV) provides user interactive functionalities and stereoscopic perception, which makes people more interested in pop-out regions or the regions with small depth value. Thus, traditional visual attention model for mono-view video can hardly be directly applied to stereoscopic visual attention (SVA) analysis for 3DV. In this paper, we propose a bottom-up SVA model to simulate human visual system with stereoscopic vision more accurately. The proposed model is based on multiple perceptual stimuli including depth information, luminance, color, orientation and motion contrast. Then, a depth based dynamic fusion is proposed to integrate these features. The experimental results on multi-view video test sequences show that the proposed model maintains high robustness and is able to efficiently simulate SVA of human eyes.

Stereoscopic visual attention model for 3d video

The recent years have witnessed three-dimensional (3-D) video technology to become increasingly popular, as it can provide high-quality and immersive experience to end users, where view rendering with depth-image-based rendering (DIBR) technique is employed to generate the virtual views. Distortions in depth map may induce geometry changes in the virtual views, and distortions in texture video may be propagated to the virtual views. Thus, effective compression of both texture videos and depth maps is important for 3-D video system. From the perspective of bit allocation, asymmetric coding of the texture videos and depth maps is an effective way to get the optimal solution of 3-D video compression and view rendering problems. In this paper, a novel asymmetric coding method of multi-view video plus depth (MVD) based 3-D video is proposed on purpose of providing high-quality view rendering. In the proposed method, two models are proposed to characterize view rendering distortion and binocular suppression in 3-D video. Then, an asymmetric coding method of MVD-based 3-D video is proposed by combining two models in encoding framework. Finally, a chrominance reconstruction algorithm is presented to achieve accurate reconstruction. Experimental results show that compared with other methods, the proposed method can obtain higher performance of view rendering under the total bitrate constraint. Moreover, the perceptual visual quality of 3-D video is almost unaffected with the proposed method.

Asymmetric Coding of Multi-View Video Plus Depth Based 3-D Video for View Rendering

Quality assessment of 3D images encounters more challenges than its 2D counterparts. Directly applying 2D image quality metrics is not the solution. In this paper, we propose a new full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be more in line with human visual perception. To be more specific, in the training phase, we learn a multiscale dictionary from the training database, so that the latent structure of images can be represented as a set of basis vectors. In the quality estimation phase, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their phase difference and amplitude difference, and compute global luminance similarity index by considering luminance changes. The final quality score is obtained by incorporating binocular combination based on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency with subjective assessment.

Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties

In three-dimensional (3D) video coding, distortion in texture video and depth maps can all affect the quality of the synthesized virtual views. Therefore, under the total bitrate constraint, effective bit allocation between texture and depth information is very important for 3D video coding. In this paper, the major technical contribution is to formulate view synthesis quality for optimal resource allocation in 3D video coding, since such quality is what that matters most to the ultimate user (i.e., the viewer) of the system; to be more specific, a new joint bit allocation and rate control method for multi-view video plus depth (MVD) based 3D video coding is proposed accordingly. We firstly derive a view synthesis distortion model to characterize the effect of coding distortion of texture video and depth maps on the synthesized virtual views. Based on this model, we derive a rate-distortion model to characterize the relationship between the bitrate and the view synthesis distortion, and the optimal bitrate ratio between texture and depth is established adaptively by solving the associated optimization problem. Finally, the rate control algorithm is performed on view level, texture/depth level and frame level. Experimental results show that compared with other methods, the proposed bit allocation method obtains higher performance of view synthesis. Moreover, the proposed rate control method can accurately control the bitrate to satisfy the total bitrate constraint.

Joint Bit Allocation and Rate Control for Coding Multi-View Video Plus Depth Based 3D Video

Research on stereoscopic image and video processing has been the new trend in the recent years. Measurement of visual quality is of fundamental importance for numerous stereoscopic image and video processing applications. The goal of quality assessment is to automatically assess the quality of images or videos in agreement with human quality judgments and optimize the stereoscopic image or video systems. Unfortunately, human are lack of the knowledge for the perception quality of stereoscopic images. In this paper, we present experimental results of an extensive subjective quality assessment experiment for stereoscopic images in which a total 400 distorted stereoscopic images were evaluated by about twenty human subjects. The stereoscopic images quality data obtained from 8,000 individual human quality judgments is used to build a database that can be exploited for understanding perception of stereoscopic images and provide data for objective assessment metrics designing. The experimental results indicated that the quality perception of the distorted stereoscopic images is content and distortion types dependent.

Mei Yu

Papers

Stereoscopic visual attention model for 3d video

Asymmetric Coding of Multi-View Video Plus Depth Based 3-D Video for View Rendering

Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties

Joint Bit Allocation and Rate Control for Coding Multi-View Video Plus Depth Based 3D Video

Research on subjective stereoscopic image quality assessment