Hello, I'm Monfer Ngu. And this will be a walk through of our research poster on understanding camouflage detection. The camouflage texture we have used for most of our stimuli is think-nice or one over f noise. This has the same one over f amplitude spectrum as natural scenes, but no phase information. The conflugging target has the same luminance contrast and texture as the background. As it sits on. The main cue for detecting it resides in the object boundary or edge. At a point on the boundary, we compute the image gradient perpendicular to the object boundary, normalize it and call it the edge element. You can compute this all along the boundary, which gives us the edge vector. This edge vector contains most of the information for detecting the presence of a target. We can measure the power of this edge vector signal, which is the sun, or in this case, the mean of the square of its elements. This edge power is larger when a target is present, and also larger the more visible that target gets. So this is a great decision variable for detection. But we can dive deeper into this edge vector. We can show using some math that the edge vector is an amplitude filter white noise signal. So if you form your transforming, the phase spectrum has no information, and only its amplitude or power spectrum is useful. We can calculate the sample distribution of power in each frequency when there is no target versus when there is a target. Here are the plots of the sample average power in each frequency. By knowing these distributions of power, we can derive the optimal Bayesian way to detect the presence of a target. It turns out to be a matched template detector on the edge power spectrum. This means that given a stimulus, you take the dot product of its edge power spectrum with this template and set a criterion on it to decide if there is a target. So far, we have been talking about edges computed at a single spatial scale. But we know that biological vision computes edges or gradients at multiple spatial scales and combines them. We can do the same thing for our stimuli. Here's the joint distribution of the edge power at different scales. We can also combine them into a single decision variable that utilizes the information at different scales and is even better at detecting the camouflage target. Another way of accumulating the information at multiple scales is to widen this stimulus, that is to unfilter its amplitude spectrum from one over F to flat. This transforms the texture to white noise, but leaves this fine edge where the true target boundary used to be. This is a lossless transformation, which means that it concentrates all of the information at all the scales into this one fine edge at a single scale. Okay, now let's talk about the experiments. We have performed psychophysics on humans using both pink noise and a more naturalistic bark texture. And we see that detection performance is smoothly and identically predicted by the edge power in both textures. So we can fit a psychometric function to this and extract a detection threshold. We can now vary the viewing conditions, such as the overall luminance and contrast of the stimulus, the size of the target or how long it is presented, and see how this detection threshold changes. We can also apply our findings to a designing effective camouflage. For example, an object such as a drone can compute its edge power and find the location and orientation on a background to land on that makes it easy or difficult to spot. We can also compare different textures for their effectiveness at camouflage. We see here, for example, that bark texture provides systematically better camouflage than the pink noise texture.