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camera->enableWhiteBalanceAuto(true); as well as automatic gain adjustment, to the environment light camera->enableGainAuto(true); Next, we declare an image that will contain the camera input. In OpenTL, images are handled through the Image class of the core::cvdata name space, built on top of the OpenCV IplImage data structure, and contains the necessary facilities for allocation, accessing, and copying. Image( int initWidth, int initHeight, ColorChannels initChannels = RGB, BitsPerChannel bpc = BPC8U); The rst two arguments specify the image resolution, the third the number of channels, and the last the number of bits per channel, which re ect the OpenCV image types. For example, RGB are three-channel color images, and BPC8U speci es an 8-bit unsigned char storage per channel. For a complete list of types available, refer to the Image.h header le. The camera input class already contains an image with the corresponding resolution and pixel format, whose pointer we can obtain using the function image(). Moreover, to record the input to a video le, we declare a video writer using the OpenCV structure CvVideoWriter: cvCreateVideoWriter( video.avi , codec, 30.0, cvSize(camera->width(), camera->height(), 1) where codec is a suitable video format; in our example we use the XViD codec, given by the OpenCV macro CV_FOURCC( X , V , I , D ). Now we can start the online loop, which performs the following operations: 1. Acquire a new frame: camera->captureNext(); 2. Get the camera image as a pointer: srcImage = camera->image(); 3. Display it onto an OpenCV-HighGUI window:



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Let us take a look at both the routing and the forwarding state created by the VPN routes for the network in Figure 7.5. For simplicity, we will look at the routing exchanges in one direction only, from PE2 towards PE1, in order to see the forwarding state that is used when forwarding traffic from PE1 towards PE2 (similar exchanges happen in the opposite direction as well): 1. Assuming that a dynamic routing protocol is running between PE2 and CE2 (attached to it), PE2 receives a route advertisement for prefix 10.2.0.0/16 from CE2. If no routing protocol is running, PE2 has this routing information configured as a static route. In Figure 7.5 BGP is assumed to be on the CE PE link. 2. PE2 attaches an RD, e.g. 65 000:1, to the route and allocates a VPN label for this prefix, e.g. label 100. PE2 then exports the labelled route into MP-BGP, with the appropriate RT for customer grey s VPN. 3. PE2 creates the forwarding state binding the VPN label (100) to the outgoing interface leading to CE2. 4. PE1 receives the MP-BGP advertisement for the VPN-IP route 65 000:1:10.2.0.0/16 with BGP next-hop of PE2 and label 100. 5. Based on the RT, PE1 determines this route must be installed in the VRF for customer grey. PE1 strips off the RD and installs a route to 10.2.0.0/16, with label 100 and next-hop PE2. In order to be able to forward traffic to this destination and make the route active, the next-hop PE2 information must be translated

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ffytche, D.H. (2005). Visual hallucinations and the Charles Bonnet syndrome. Current Psychiatry Reports, 7(3), 168 179.

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The rst issue in color-based tracking is proper representation of the color space: a mathematical model describing how perceptually different colors can be represented by t -uples of numbers, typically three values. For example, since any visible color can be obtained by combining a given amount of red, green, and blue, the RGB color space (Fig. 4.2) can be represented by an orthogonal Cartesian frame, where the three axes represent the respective percentage of each component, normalized from 0 to 1. Many different color spaces have been de ned, both according to psychophysical or technical representation criteria, along with the mapping between spaces. When dealing with devices such as printers or monitors, color spaces also take into account the difference between the human perception and the device representation capabilities given by the gamut, a speci c subset of visible colors that can actually be represented and processed by the device. In computer vision, the choice of color space may be crucial, since any segmentation or target localization procedure may bene t greatly from a wellbehaved distribution of the color features, a distribution that allows easy classi cation of target pixels among different objects, including the background. We begin by considering the human perception of color, which is based on the response of three types of receptors in the retina, called cones. Each cone is denoted either S, M, or L (short, medium, or long wavelength), each of which has a different range of sensitivity to the wavelengths of the visible light spectrum, depicted on the left in Fig. 4.3. The S-cones are more sensitive to high frequencies, corresponding to violet light, while the M- and L-cones are more

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