Creating complex multimedia presentations involves the specification of temporal and spatial relations in the form of constraints. However, some of these constraints could contradict each other and hence lead to an inconsistency. The user may not be aware of this inconsistency while authoring. Hence this inconsistency has to be identified and removed by the presentation process prior to the play-out. In this paper, we examined an existing work based on graph theory for consistency checking. We propose a modification to this approach which simplifies the algorithm, reduces its total running time, and helps to make it dynamic. Another salient feature of our paper is the introduction of new temporal and spatial operators with higher expressive power than traditional ones. Thus, this paper presents a multimedia presentation mechanism, which dynamically maintains a consistent and complete set of constraints during authoring and play-out of the presentation.
Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.
An algorithm proposed by Sridhar and Kumaravel is extended to include a framework for the detection of renal calculi. Calculi occur due to abnormal collection of certain chemicals like oxalate, phosphate and uric acid. These calculi can be present in the kidney, ureter or urinary bladder. Performance analysis is done to a set of five known algorithms using parameters such as success rate in calculi detection, border error metric and time. The framework is constructed by combining the best algorithm based on the performance analysis and a procedure to validate the detected calculi using the shadow it casts in ultrasound images. Ultrasound images of 37 patients are used for testing the algorithm. The detected calculi based on the framework match those determined by expert clinicians in more than 95% of the cases.
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