Erratum: Two-Dimensional Failure Modeling with Minimal Repair which appeared in this journal of April 2004, Volume 51:3 on pages 345–362

In this Erratum, we point out the reason for an error in the derivation of a result in our earlier paper, “Two-Dimensional Failure Modeling with Minimal Repair” [1], which appeared in the April 2004 issue of this journal, 51:3, on pages 345–362, and give the correct derivation.

Are echo techniques comparable for measurement of left ventricular dysynchrony? Comparison of real time 3D and tissue Doppler echocardiography in patients with ischaemic cardiomyopathy

Tissue Doppler (TD) assessment of dysynchrony (DYS) is established in evaluation for bi-ventricular pacing. Time to regional minimal volume by real-time 3D echo (3D) has been applied to DYS. 3D offers simultaneous assessment of all segments and may limit errors in localization of maximum delay due to off-axis images.We compared TD and 3D for assessment of DYS. 27 patients with ischaemic cardiomyopathy (aged 60±11 years, 85% male) underwent TD with generation of regional velocity curves. The interval between QRS onset and maximal systolic velocity (TTV) was measured in 6 basal and 6 mid-cavity segments. Onthe same day,3Dwas performed and data analysed offline with Q-Lab software (Philips, Andover, MA). Using 12 analogous regional time-volume curves time to minimal volume (T3D)was calculated. The standard deviation (S.D.) between segments in TTV and T3D was calculated as a measure ofDYS. In 7 patients itwas not possible to measureT3D due to poor images. In the remaining 20, LV diastolic volume, systolic volume and EF were 128±35 ml, 68±23 ml and 46±13%, respectively. Mean TTV was less than mean T3D (150±33ms versus 348±54 ms; p < 0.01). The intrapatient range was 20–210ms for TTV and 0–410ms for T3D. Of 9 patients (45%) with significantDYS (S.D. TTV > 32 ms), S.D. T3D was 69±37ms compared to 48±34ms in those without DYS (p = ns). In DYS patients there was concordance of the most delayed segment in 4 (44%) cases.Therefore, different techniques for assessing DYS are not directly comparable. Specific cut-offs for DYS are needed for each technique.

Towards effective indexing for very large video sequence database

With rapid advances in video processing technologies and ever fast increments in network bandwidth, the popularity of video content publishing and sharing has made similarity search an indispensable operation to retrieve videos of user interests. The video similarity is usually measured by the percentage of similar frames shared by two video sequences, and each frame is typically represented as a high-dimensional feature vector. Unfortunately, high complexity of video content has posed the following major challenges for fast retrieval: (a) effective and compact video representations, (b) efficient similarity measurements, and (c) efficient indexing on the compact representations. In this paper, we propose a number of methods to achieve fast similarity search for very large video database. First, each video sequence is summarized into a small number of clusters, each of which contains similar frames and is represented by a novel compact model called Video Triplet (ViTri). ViTri models a cluster as a tightly bounded hypersphere described by its position, radius, and density. The ViTri similarity is measured by the volume of intersection between two hyperspheres multiplying the minimal density, i.e., the estimated number of similar frames shared by two clusters. The total number of similar frames is then estimated to derive the overall similarity between two video sequences. Hence the time complexity of video similarity measure can be reduced greatly. To further reduce the number of similarity computations on ViTris, we introduce a new one dimensional transformation technique which rotates and shifts the original axis system using PCA in such a way that the original inter-distance between two high-dimensional vectors can be maximally retained after mapping. An efficient B+-tree is then built on the transformed one dimensional values of ViTris' positions. Such a transformation enables B+-tree to achieve its optimal performance by quickly filtering a large portion of non-similar ViTris. Our extensive experiments on real large video datasets prove the effectiveness of our proposals that outperform existing methods significantly.