response. Such an architecture, sensitive to local spatial discontinuities, is particularly well-suited to detecting locations which stand out from their surround and is a general computational principle in the retina, lateral geniculate nucleus, and primary visual cortex [11]. Center-surround is implemented in the model as the difference between and coarse scales: The center is a pixel at scale {2, 3, c ε 4}, and the surround is the corresponding pixel at scale = + with s c δ, 4}. The across-scale difference between two maps, denoted δ ε{3, ϴ below, is obtained by interpolation to the finer scale and point-by-point subtraction. Using several scales not only for but also for = - c s c δ yields truly multiscale feature extraction, by including different size ratios between the center and surround regions. Extraction of visual features With and being the red, green, and blue channels of the r, g, b input image, an intensity image is obtained as = (r + + is I I g b)/3. I used to create a Gaussian pyramid where is the scale. I(σ), σ ε[0..8] The and channels are normalized by in order to decouple hue r, g, b I from intensity. However, because hue variations are not perceivable at very low luminance (and hence are not salient),normalization is only applied at the locations where is larger than 1/10 of its maximum over I the entire image (other locations yield zero and Four r, g, b). color channels are created: = - (g + for red, = - (r + R r b)/2 G g for green, = - (r + for blue, and = (r + - |r - - b)/2 B b g)/2 Y g)/2 g|/2 b for yellow (negative values are set to zero). Four Gaussian pyramids and are created from these color channels R(σ), G(σ), B(σ), Y(σ) I(c,s)=|I(c)ϴI(s)|
response. Such an architecture, sensitive to local spatial discontinuities, is particularly well-suited to detecting locations which stand out from their surround and is a general computational principle in the retina, lateral geniculate nucleus, and primary visual cortex [11]. Center-surround is implemented in the model as the difference between and coarse scales: The center is a pixel at scale c ε {2, 3, 4}, and the surround is the corresponding pixel at scale s = c + δ, with δ ε{3, 4}. The across-scale difference between two maps, denoted ϴ below, is obtained by interpolation to the finer scale and point-by-point subtraction. Using several scales not only for c but also for δ = s - c yields truly multiscale feature extraction, by including different size ratios between the center and surround regions. Extraction of visual features With r, g, and b being the red, green, and blue channels of the input image, an intensity image I is obtained as I = (r + g + b)/3. I is used to create a Gaussian pyramid I(σ), where σ ε[0..8] is the scale. The r, g, and b channels are normalized by I in order to decouple hue from intensity. However, because hue variations are not perceivable at very low luminance (and hence are not salient),normalization is only applied at the locations where I is larger than 1/10 of its maximum over the entire image (other locations yield zero r, g, and b). Four color channels are created: R = r - (g + b)/2 for red, G = g - (r + b)/2 for green, B = b - (r + g)/2 for blue, and Y = (r + g)/2 - |r - g|/2 - b for yellow (negative values are set to zero). Four Gaussian pyramids R(σ), G(σ), B(σ), and Y(σ) are created from these color channels I(c,s)=|I(c)ϴI(s)|
101725324-Object-Recognition-in-the-Surveillance-Area-of-Visually-Impaired.docx