This article presents a conceptual model of the transition process when changing from entrepreneurial management to functional management. It may provide a tool for predicting the success or failure of such a change.
In this letter, we derive a new super Gaussian Joint Maximum a Posteriori (SGJMAP) based single microphone speech enhancement gain function. The developed Speech Enhancement method is implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids. We introduce a “tradeoff” parameter in the derived gain function that allows the smartphone user to customize their listening preference, by controlling the amount of noise suppression and speech distortion in real-time based on their level of hearing comfort perceived in noisy real world acoustic environment. Objective quality and intelligibility measures show the effectiveness of the proposed method in comparison to benchmark techniques considered in this paper. Subjective results reflect the usefulness of the developed Speech Enhancement application in real-world noisy conditions at signal to noise ratio levels of -5 dB, 0 dB and 5 dB.
The leader's job is one of leadership and operations. The first is about inspiring and motivating people to go beyond their fears and painting a believable future that is waiting after the storm. The second is about the daily nitty-gritty of doing business successfully in a very tough and unpredictable environment. 8. The leader's actions have to be bold, not tentative, if we have to convey confidence. 9. Leadership must identify the constituents and things that are at the core of their company and protect them from loss or damage during the crisis. 10. This is a time that requires superb horizontal cooperation among people. Leaders must encourage cross-pollination. 11. Leaders have two major responsibilities that must be met through communication: information flow and motivation. It is essential to repeat it time and again to be sure everyone gets it. The need is for leadership to have clarity of intent, create context for people to act, demonstrate courage of conviction and establish the container or boundary within which people can practice.
Abstract. This paper presents a coarse-to-fine algorithm to obtain pinel trajectories in a long image sequence and to segment it into subsets corresponding to distinctly moving objects. Much of the previous related work has addressed the computation of optical flow over two frames or sparse feature trajectories in sequences. The features used are often small in number and restrictive assumptions are made about them such as the visibility of features in all the frames. The algorithm described here uses a coarse scale point feature detector to form a 3-D dot pattern in the spatiotemporal space. The trajectories are extracted as 3-D curves formed by the points using perceptual grouping. Increasingly dense correspondences are obtained iteratively from the sparse feature trajectories. At the finest level, which is the focus of this paper, all pixels are matched and the finest boundaries of the moving objects are obtained. Keywords: Motion Segmentation, Perceptual Grouping, Pixel Matching, Triangulation, Feature Matching, Optical Flow.
IntroductionThis paper describes a component of our work aimed at interpretation of image sequences. Given an image sequence containing an arbitrary number of rigid objects in motion, the objectives of the overall work are to identify feature points in the scene, obtain spatially dense trajectories of those points, segment moving objects, compute image flow at each pixel, and derive a qualitative description of the scene structure and dynamics from the image sequence. Such qualitative interpretation of the image sequence is useful for a variety of applications such as traffic scene analysis, biological image analysis and aerial image understanding. The focus of this paper is on the detection of pixel flow trajectories. Next section reviews some related previous work. Section 3 summarizes the steps of coarseto-fine detection of sparse feature trajectories. Section 4 then gives the details of the algorithm for finding pixel flow trajectories which is the objective of this paper. Section 5 presents experimental results and Section 6 presents concluding remarks.
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