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Visual object tracking is concerned with the problem of sequentially localizing one or more objects in real time by exploiting information from imaging devices through fast, model-based computer vision and image-understanding techniques (Fig. 1.1). Applications already span many elds of interest, including robotics, man machine interfaces, video surveillance, computer-assisted surgery, and navigation systems. Recent surveys on the current state of the art have appeared in the literature (e.g., [169,101]), together with a variety of valuable and ef cient methodologies. Many of the low-level image processing and understanding algorithms involved in a visual tracking system can now be found in open-source vision libraries such as the Intel OpenCV [15], which provides a worldwide standard; and at the same time, powerful programmable graphics hardware makes it possible both to visualize and to perform computations with very complex object models in negligible time on common PCs, using the facilities provided by the OpenGL [17] language and its extensions [19]. Despite these facts, to my knowledge, no wide-scale examples of software libraries for model-based visual tracking are available, and most existing software deals with more or less limited application domains, not easily allowing extensions or inclusion of different methodologies in a modular and scalable way. Therefore, a unifying, general-purpose, open framework is becoming a compelling issue for both users and researchers in the eld. This challenging

for the one-step-ahead forecast. The 95 percent con dence interval for the onestep-ahead forecast is then computed: 0.005911 + 0.2229yt 1.96 (1 step-ahead GARCH forecast) 0.005911 + 0.2229yt 1.96 (0.000163 + 0.1314 2 + 0.7187 2) t t It was found that a sample proportion of 93.7 percent of the one-step-ahead volatility forecasts fell inside the 95 percent con dence bands. The con dence bands widen when volatility increases and narrows when volatility decreases. Moreover, the sample proportion of volatility forecasts that fall inside the con dence bands increases as the step-ahead size increases. This is because there is more variability over a longer forecast horizon than over a one-period horizon so that the GARCH model has more information (conditional variances) on which to base forecasts. GARCH has a long-term memory of these past conditional variances. For a s = six-step-ahead forecast, the 95 percent con dence interval is:

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TABLE 5-2. The Results of Cleavage of 1 and Its Derivatives With FeSO4 in Aqueous CH3CN Entry 1 2 3 4 5 6 Compound 159a (1) 159b (123) 159c (125) 159d 159e (98) 159f 160a (28) (25%) 160b (37%) 160c (45%) 160d* (39%) 160e (46%) 160f (59%) Products (yields) 161a (15) (67%) 161b (45%) 161c (23%) 161d (56%) 161e (25%) 161f (25%) Others (<10%) 161e (4%, a b) 161e (25%)

a deviation of 4.73%. The two deviations are multiplied together to form the contribution to covariance for the period. We can see in cell H11 the large contribution to covariance for that month equal to 34.73. 2. Periods where the fund and benchmark returns move in opposite directions, relative to their averages, contribute negatively towards covariance. In month 11 the fund return fell below its own average, but the benchmark return was above average. The product of the differences leads to a negative contribution to covariance equal to 1.54. A total positive covariance indicates that the return series are associated, in that they move together. A negative covariance indicates that they move in opposite directions. This would be an interesting property for a fund to have because of its diversi cation bene ts. When there is no relation between the fund and the benchmark returns, the products of the monthly differences will sometimes be positive and sometimes be negative. Averaging these over the entire period produces a low or zero covariance. Exhibit 11.3 summarizes the interpretation of covariance. The fund has a covariance with the benchmark equal to 13.30, but what does this mean Covariance is dif cult to interpret as anything other than the average product of the differences between the fund and benchmark return deviations. Covariance is also dif cult to use for portfolio comparisons because it is impacted by the absolute size of the returns.

Table 1-2 NAME enum sipping simple xcon behave ecrit p2psip (continued) FIRST RFC 2000 2002 2004 2005 (2005) (2005) (2005) CHARTER DNS-based use of ITU-T E.164 telephone numbers Applications and extensions to SIP Use of SIP for Instant Messaging (IM) and Presence Centralized conferences Behavior for Network Address Translation (NAT) for use with SIP, RTP Emergency communications (such as 911, 112) Peer-to-peer SIP (not yet a formal WG)

N OT E Any column in a dataset that has a name that includes spaces or a nonalphanumeric character must be enclosed within brackets ([ ]) in your SQL statement. For example, the SQL statement selecting data from a column called Last Name would look like this: SELECT [Last Name] FROM EmployeeTable.

In this example, the filtered list shows the rows that meet either of the following conditions:

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