Feelings are actively induced as the actor “psyches” him/herself into the desired persona. Emotional labor is thought by many to be an important part of the role of many health care professionals and it has been the focus of much debate and empirical enquiry within a range of health care settings, especially within nursing. However, the research to date is limited in a number of important ways. First, as mentioned, much of the existing focus is limited to the nursing profession, despite the recognition that emotional labor is likely to be an important feature of other health-care settings. Second, a theoretical model driving the research direction seems to be lacking, resulting in a range of varied and interesting studies that are difficult to relate into a coherent whole.




Emotional labor was first defined by Hochschild (1983) and has more recently been described as the effort involved when employees “regulate their emotional display in an attempt to meet organizationally-based expectations specific to their roles”. These “expectations”, or display rules, specify either formally or informally, which emotions employees ought to express and which ought to be suppressed. Whilst many employees want to portray emotions in accordance with display rules because they care about their clients, there are likely to be many occasions when genuinely felt emotions do not concur with desired emotions. It is this emotional dissonance that leads to emotional labor. Payday Loans Online




Within one shot, due to the motion, appearance or disappearance of objects, the color histogram may not be consistent. Gist captures the overall texture of the background while ignoring these small change due to foreground objects. A classifier trained to detect such rare occurrence would be biased to treat all frames as non-transition. But the classifier approach is impractical. Instead, we treat the task of detecting shot boundaries as an outlier detection problem. We use a simple z-score metric to determine outliers. The first strategy measures the z-score across all dimensions (assuming they are independent). The second strategy counts the number of dimensions in which the given data-point is an outlier. Payday Loans Online



This method results in a large number of vectors and is susceptible to camera motion and object motion. The sensitivity to motion is reduced by extracting features from the whole frame, among which histogram is the mostly used. The disadvantage of global features is it tends to have low performance at detecting the boundary of two similar shots. To balance the tradeoff of resistance to motion and discriminating similar shots, a region based feature is proposed. Region-based method divides each frame into equal-sized blocks, and extracts a set of features per block. Based on the assumption that color content doesn’t change rapidly within but across shots, color is the mostly used features, others are edges and textures.




Block matching is used to retrieve an initial estimate of the image displacement. To obtain a dense displacement field, matching with adaptive block sizes was implemented. In this typical algorithm, a frame is divided into blocks of M x N pixels or, more usually, square blocks of N2 pixels. Then, we assume that each block undergoes translation only with no scaling or rotation. The blocks in the first frame are compared to the blocks in the second frame. Motion Vectors can then be calculated for each block to see where each block from the first frame ends up in the second frame. For every video sequence we determine the number of shots, the number of shots correctly reported, the number of false detections and the number of non reported shots.



Motion Activity Discriptor

Video segmentation based on motion is a new research area. Motion is a salient feature in video, in addition to other typical image features such as color, shape and texture. The motion activity is used in different applications such as video surveillance, fast browsing, dynamic video summarization, content-based querying etc. This information can for example be used for shot boundary detection, for shot classification or scene segmentation.




SVM is used in this method for shot boundary detection. Support Vector Machines (SVM) is a statistical learning method based on the structure risk minimization principle. It has been very efficiently proved to be useful in many pattern recognition applications. In the case of binary classification, the objective of the SVM is to find the best separating hyperplane with a maximum margin. To train this classifier, we manually annotated frames in the training data. Using the trained classifier, we can label a sequence of frames.


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