75 s, which was adequate for the temperature measurements. Figure 3 shows the fluorescence intensity of each dye versus the pulse count. The fluorescence intensity was normalized by the initial intensity for each dye. The fluorescence intensities of RhB and Rh110 after 20 pulses were 101% and 98% of the initial dye intensities, respectively. Thus, almost no photobleaching occurred using pulse excitation for several tens of pulses. Consequently, pulse excitation was used in the temperature measurements. The un
The emphasis of vision sensor technology becomes more and more evident in various visual measurements, such as automotive, human machine interface, surveillance and security, and industry control.

For example, if we introduce a vision sensor for high-speed visual information [1] and proposed an appropriate control algorithm for the vision sensor utilizing some unique features, real-time visual measurement [2] and wearable biometrics devices will be achieved. Ideally, synchronization can be achieved without any external triggers or references in the computer vision field. Multiple groups of images bring much more valuable additional information, such as the depth parameter, to perform accurate measurements in the real world, without the limitation of one-view measurement techniques [3].Firstly, there are a group of studies in which geometric correspondences such as points are used for synchronization [4�C11]. Although these methods can carry out geometric calibration and synchronization simultaneously, a sufficient number of correspondences across images are necessary.

This is not appropriate depending on applications. Also, estimating simultaneously geometric parameters and time delays, which are inherently independent of each other, might sacrifice accuracy to some degree.Therefore, AV-951 it is more desirable to synchronize without using image correspondences. Yan and Pollefeys proposed a method for video synchronization [12] that uses the space-time interest points defined by Laptev and Lindeberg [13]. This method also fails to synchronize images in the case of foreground objects [14]. When the feature points are not available or reliable, some alternative algorithms that use the object outline or silhouette as the reliable image feature exploit into the epipolar tangents [15], i.e., points on the silhouette contours in which the tangent to the silhouette is an epipolar line [16].

A rich literature exists on exploiting epipolar tangents, both for orthographic cameras [15,17] and perspective cameras [18]. There are also factorization-based methods to recover 3D models from multiple perspective views with uncalibrated cameras, performing a projective reconstruction using a bilinear factorization algorithm and then converting the projective solution to a Euclidean one by enforcing metric constrains, but they are based on static scenes and moving objects [11, 19�C 21].