Recognition of familiar objects in cluttered backgrounds is a challenging computational problem. Camouflage provides a particularly striking case, where an object is difficult to detect, recognize, and segment even when in "plain view." Current computational approaches combine low-level features with high-level models to recognize objects. But what if the object is unfamiliar? A novel camouflaged object poses a paradox: A visual system would seem to require a model of an object's shape in order to detect, recognize, and segment it when camouflaged. But, how is the visual system to build such a model of the object without easily segmentable samples? One possibility is that learning to identify and segment is opportunistic in the sense that learning of novel objects takes place only when distinctive clues permit object segmentation from background, such as when target color or motion enables segmentation on single presentations. We tested this idea and discovered that, on the contrary, human observers can learn to identify and segment a novel target shape, even when for any given training image the target object is camouflaged. Further, perfect recognition can be achieved without accurate segmentation. We call the ability to build a shape model from high-ambiguity presentations bootstrapped learning.

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties 1 . Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties 2 .Many innovative and useful methods currently exist for creating novel objects and object categories [3][4][5][6] (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.First, shape variations are generally imposed by the experimenter 5,9,10 , and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.Second, the existing methods have difficulty capturing the shape complexity of natural objects [11][12][13] . If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms.Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis 14 . Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection 9,12,13 . Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics 15,16 . The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of realworld objects 9,13 . Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper.We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limita...

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A dominant theme in vision research is that important characteristics of the visual pathway evolved to be effective in processing natural scenes. Given this perspective, one can learn about the nature of visual processing from a quantitative analysis of natural images. Such analysis can benefit from the camera as a measuring device. As such, the camera should not introduce arbitrary artifacts into the image formation process. This paper describes how to correct a number of unnecessary artifacts associated with obtaining natural scene statistics with a digital camera. For artifacts that are inherently part of image formation, and where elimination is not possible or appropriate, we describe methods for transformation and quantification.