Humans move their eyes while looking at scenes and pictures. Eye movements correlate with shifts in attention and are thought to be a consequence of optimal resource allocation for high-level tasks such as visual recognition. Models of attention, such as "saliency maps," are often built on the assumption that "early" features (color, contrast, orientation, motion, and so forth) drive attention directly. We explore an alternative hypothesis: Observers attend to "interesting" objects. To test this hypothesis, we measure the eye position of human observers while they inspect photographs of common natural scenes. Our observers perform different tasks: artistic evaluation, analysis of content, and search. Immediately after each presentation, our observers are asked to name objects they saw. Weighted with recall frequency, these objects predict fixations in individual images better than early saliency, irrespective of task. Also, saliency combined with object positions predicts which objects are frequently named. This suggests that early saliency has only an indirect effect on attention, acting through recognized objects. Consequently, rather than treating attention as mere preprocessing step for object recognition, models of both need to be integrated.

We report a method for filtering white light into individual colors using metal-insulator-metal resonators. The resonators are designed to support photonic modes at visible frequencies, and dispersion relations are developed for realistic experimental configurations. Experimental results indicate that passive Ag/Si 3 N 4 /Au resonators exhibit color filtering across the entire visible spectrum. Full field electromagnetic simulations were performed on active resonators for which the resonator length was varied from 1-3 µm and the output slit depth was systematically varied throughout the thickness of the dielectric layer. These resonators are shown to filter colors based on interference between the optical modes within the dielectric layer. By careful design of the output coupling, the resonator can selectively couple to intensity maxima of different photonic modes and, as a result, preferentially select any of the primary colors. We also illustrate how refractive index modulation in metal-insulator-metal resonators can yield actively tunable color filters. Simulations using lithium niobate as the dielectric layer and the top and bottom Ag layers as electrodes, indicate that the output color can be tuned over the visible spectrum with an applied field.

How important is a particular object in a photograph of a complex scene? We propose a definition of importance and present two methods for measuring object importance from human observers. Using this ground truth, we fit a function for predicting the importance of each object directly from a segmented image; our function combines a large number of object-related and image-related features. We validate our importance predictions on 2,841 objects and find that the most important objects may be identified automatically. We find that object position and size are particularly informative, while a popular measure of saliency is not.

Abstract. We observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. We argue that a goal of visual recognition is, therefore, not only to detect and classify objects but also to associate with each a level of priority which we call 'importance'. We propose a definition of importance and show how this may be estimated reliably from data harvested from human observers. We conclude by showing that a first-order estimate of importance may be computed from a number of simple image region measurements and does not require access to image meaning.

Weak physical unclonable functions (PUFs) can instantiate read-proof hardware tokens (Tuyls et al. 2006, CHES) where benign variation, such as changing temperature, yields a consistent key, but invasive attempts to learn the key destroy it. Previous approaches evaluate security by measuring how much an invasive attack changes the derived key (Pappu et al. 2002, Science). If some attack insufficiently changes the derived key, an expert must redesign the hardware.An unexplored alternative uses software to enhance token response to known physical attacks. Our approach draws on machine learning. We propose a variant of linear discriminant analysis (LDA), called PUF LDA, which reduces noise levels in PUF instances while enhancing changes from known attacks.We compare PUF LDA with standard techniques using an optical coating PUF and the following feature types: raw pixels, fast Fourier transform, short-time Fourier transform, and wavelets. We measure the true positive rate for valid detection at a 0% false positive rate (no mistakes on samples taken after an attack). PUF LDA improves the true positive rate from 50% on average (with a large variance across PUFs) to near 100%.While a well-designed physical process is irreplaceable, PUF LDA enables system designers to improve the PUF reliabilitysecurity tradeoff by incorporating attacks without redesigning the hardware token.