Non-degenerate 2-photon excitation of a fluorophore with two laser beams of different photon energies may offer independent degree of freedom in tuning of the photon flux (i.e., the power) for each beam. Wereport a practical demonstration that the emission intensity of a fluorophore excited in the non-degenerate regime in scattering medium is more efficient than the commonly used degenerate 2-photon excitation. In our experiments we use spatially and temporally aligned Ti:Sapphiremode-locked laser and optical parametric oscillator beams operating at near infrared (NIR) and short-wavelength infrared (SWIR) optical frequencies, respectively. The non-degenerate 2-photon excitation mechanism takes advantage of the infrared wavelengths used in 3-photon microscopy to achieve increased penetration depth, while preserving relatively high 2-photon excitation cross section, exceeding that achievable with the 3-photon excitation. Importantly, independent control of power for each beam implies that the flux requirement for the higher photon energy NIR beam, which experiences higher scattering in biological tissue, can be relaxed at the expense of increasing the flux of the lower photon energy SWIR beam which experiences lower scattering, thus promising deeper penetration with higher efficiency of excitation.Applications for in vivo brain imaging will be also discussed.
Good pedestrian classifiers that analyze static images for presence of pedestrians are in existence. However, even a low false positive error rating is sufficient to flood a real system with false warnings. We address the problem of pedestrian motion (gait) modeling and recognition using sequences of images rather than static individual frames, thereby exploiting information in the dynamics. We use two different representations and corresponding distances for gait sequences. In the first a gait is represented as a manifold in a lower dimensional space corresponding to gait images. In the second a gait image sequence is represented as the output of a dynamical system whose underlying driving process is an action like walking or running. We examine distance functions corresponding to these representations. For dynamical systems we formulate distances derived based on parameters of the system taking into account both the structure of the output space and the dynamics within it. Given appearance based models we present results demonstrating the discriminative power of the proposed distances