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Edges in images of translucent objects are very different from edges in images of opaque objects. The physical causes for these differences are hard to characterize analytically and are not well understood. This paper considers one class of translucency edges - those caused by a discontinuity in surface orientation - and describes the physical causes of their appearance. We simulate thousands of translucency edge profiles using many different scattering material parameters, and we explain the resulting variety of edge patterns by qualitatively analyzing light transport. We also discuss the existence of shape and material metamers, or combinations of distinct shape or material parameters that generate the same edge profile. This knowledge is relevant to visual inference tasks that involve translucent objects, such as shape or material estimation., National Science Foundation (U.S.) (IIS 1161564), National Science Foundation (U.S.) (IIS 1012454), National Science Foundation (U.S.) (IIS 1212928), National Science Foundation (U.S.) (IIS 1011919), National Institutes of Health (U.S.) (R01- EY019262-02), National Institutes of Health (U.S.) (R21-EY019741-02)

Multiple scattering contributes critically to the characteristic translucent appearance of food, liquids, skin, and crystals; but little is known about how it is perceived by human observers. This article explores the perception of translucency by studying the image effects of variations in one factor of multiple scattering: the phase function. We consider an expanded space of phase functions created by linear combinations of Henyey-Greenstein and von Mises-Fisher lobes, and we study this physical parameter space using computational data analysis and psychophysics. Our study identifies a two-dimensional embedding of the physical scattering parameters in a perceptually meaningful appearance space. Through our analysis of this space, we find uniform parameterizations of its two axes by analytical expressions of moments of the phase function, and provide an intuitive characterization of the visual effects that can be achieved at different parts of it. We show that our expansion of the space of phase functions enlarges the range of achievable translucent appearance compared to traditional single-parameter phase function models. Our findings highlight the important role phase function can have in controlling translucent appearance, and provide tools for manipulating its effect in material design applications., National Institutes of Health (U.S.) (Award R01-EY019262-02), National Institutes of Health (U.S.) (Award R21-EY019741-02)

Recent attempts to fabricate surfaces with custom reflectance functions boast impressive angular resolution, yet their spatial resolution is limited. In this paper we present a method to construct spatially varying reflectance at a high resolution of up to 220dpi, orders of magnitude greater than previous attempts, albeit with a lower angular resolution. The resolution of previous approaches is limited by the machining, but more fundamentally, by the geometric optics model on which they are built. Beyond a certain scale geometric optics models break down and wave effects must be taken into account. We present an analysis of incoherent reflectance based on wave optics and gain important insights into reflectance design. We further suggest and demonstrate a practical method, which takes into account the limitations of existing micro-fabrication techniques such as photolithography to design and fabricate a range of reflection effects, based on wave interference., United States-Israel Binational Science Foundation, Intel Corporation (Intel Collaborative Research Institute for Computational Intelligence), National Science Foundation (U.S.) (CGV 1116303)

In this report, we describe a fast deconvolution approach for color images that combines a sparse regularization cost on the magnitudes of gradients with constraints on their direction in color space. We form these color constraints in a way that allows retaining the computationally-efficient optimization strategy introduced in recent deconvolution methods based on half-quadratic splitting. The proposed algorithm is capable of handling a different blur kernel in each color channel, and is used for per-layer deconvolution in our paper: Depth and Deblurring from a Spectrally-varying Depth-of-Field [1]. A MATLAB implementation of this method is available at http://vision.seas.harvard. edu/ccap, and takes roughly 20 seconds to deconvolve a three-channel 1544 1028 color image, on a Linux-based Intel I-3 2.1GHz machine.

This manuscript completes the analysis of our SIGGRAPH 2013 paper "Fabricating BRDFs at High Spatial Resolution Using Wave Optics" in which photolithography fabrication was used for manipulating reflectance effects. While photolithography allows for precise reflectance control, it is costly to fabricate. Here we explore an inexpensive alternative to micro-fabrication, in the form of metallic powders. Such powders are readily available at a variety of particle sizes and morphologies. Using an analysis similar to the micro-fabrication paper, we provide guidelines for the relation between the particles' shape and size and the reflectance functions they can produce.

The broad goal of this project is to develop new computational processes for leveraging visual cues for image-based scene understanding, and we seek to achieve this goal through a careful analysis of reflectance, light transport, and imaging. During the past year, we have continued our exploration of micro computer vision systems, meaning those that can accomplish useful visual tasks within the power and size constraints of micro-scale platforms like micro air vehicles and micro sensor nodes. We have also developed a passive method for simultaneous depth and color imaging that exploits a new statistical model of joint spatial and spectral structure in natural images.

Blur is caused by a pixel receiving light from multiple scene points, and in many cases, such as object motion, the induced blur varies spatially across the image plane. However, the seemingly straight-forward task of estimating spatially-varying blur from a single image has proved hard to accomplish reliably. This work considers such blur and makes two contributions: a local blur cue that measures the likelihood of a small neighborhood being blurred by a candidate blur kernel; and an algorithm that, given an image, simultaneously selects a motion blur kernel and segments the region that it affects. The methods are shown to perform well on a diversity of images., United States. National Geospatial-Intelligence Agency (NEGI-1582-04-0004), United States. Army Research Office. Multidisciplinary University Research Initiative (Grant Number N00014-06-1-0734), Google (Firm), Microsoft Corporation, Adobe Systems

Different materials reflect light in different ways, and this reflectance interacts with shape, lighting, and viewpoint to determine an object's image. Common materials exhibit diverse reflectance effects, and this is a significant source of difficulty for image analysis. One strategy for dealing with this diversity is to build computational tools that exploit reflectance symmetries, such as reciprocity and isotropy, that are exhibited by broad classes of materials. By building tools that exploit these symmetries, one can create vision systems that are more likely to succeed in real-world, non-Lambertian environments. In this paper, we develop a framework for representing and exploiting reflectance symmetries. We analyze the conditions for distinct surface points to have local view and lighting conditions that are equivalent under these symmetries, and we represent these conditions in terms of the geometric structure they induce on the Gaussian sphere and its abstraction, the projective plane. We also study the behavior of these structures under perturbations of surface shape and explore applications to both calibrated and uncalibrated photometric stereo.

Different materials reflect light in different ways, and reflectance interacts with shape, lighting, and viewpoint to determine an object's image. Common materials exhibit diverse reflectance effects, and this is a significant source of difficulty for radiometric image analysis. One strategy for dealing with this diversity is to build computational tools that exploit reflectance symmetries, such as reciprocity and isotropy, that are exhibited by broad classes of materials. In this paper, we advocate the real projective plane as a tool for representing and exploiting these symmetries. In this approach, each point in the plane represents a surface normal that is visible from a fixed viewpoint, and reflectance symmetries are analyzed in terms of the geometric structures that they induce. We provide an overview of these structures and explore applications to both calibrated and uncalibrated photometric stereo.©2009 IEEE.