Height from Photometric Ratio with Model-based Light Source Selection

In this paper, we present a photometric stereo algorithm for estimating surface height. We follow recent work that uses photometric ratios to obtain a linear formulation relating surface gradients and image intensity. Using smoothed finite difference approximations for the surface gradient, we are able to express surface height recovery as a linear least squares problem that is large but sparse. In order to make the method practically useful, we combine it with a model-based approach that excludes observations which deviate from the assumptions made by the image formation model. Despite its simplicity, we show that our algorithm provides surface height estimates of a high quality even for objects with highly non-Lambertian appearance. We evaluate the method on both synthetic images with ground truth and challenging real images that contain strong specular reflections and cast shadows

Robust 3D face capture using example-based photometric stereo

We show that using example-based photometric stereo, it is possible to achieve realistic reconstructions of the human face. The method can handle non-Lambertian reflectance and attached shadows after a simple calibration step. We use spherical harmonics to model and de-noise the illumination functions from images of a reference object with known shape, and a fast grid technique to invert those functions and recover the surface normal for each point of the target object. The depth coordinate is obtained by weighted multi-scale integration of these normals, using an integration weight mask obtained automatically from the images themselves. We have applied these techniques to improve the PHOTOFACE system of Hansen et al. (2010). © 2013 Elsevier B.V. All rights reserved

Computer Assisted Relief Generation - a Survey

In this paper we present an overview of the achievements accomplished to date in the field of computer aided relief generation. We delineate the problem, classify the different solutions, analyze similarities, investigate the evelopment and review the approaches according to their particular relative strengths and weaknesses. In consequence this survey is likewise addressed to researchers and artists through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application

Waiting Room Rounding to Decrease LWBS

Problem: Patients who leave the emergency department without being seen (LWBS) by a medical provider have been identified as a safety and liability risk as well as a profit loss. One high volume urban teaching emergency department (ED) in southern California has struggled with a percentage of LWBS well over the benchmark. Objectives: The department implemented waiting room rounding as an evidence based change in an effort to reduce LWBS. A goal was set to reduce LWBS to less than five percent in the month of April after rounding began. Methods: ED technicians assigned to triage round on patients who have been waiting in the waiting room every two hours until they have been moved to a room or discharged from triage. Rounding is tracked on paper with a rounding log. Education was provided to ED technicians and ED registered nurses (RN) with a power point presentation and one-to-one discussions. Results: Overall LWBS dropped to 3.4%, but poor enforcement and lack of accountability resulted in poor compliance with rounding in April. Mid-month, it was decided to reduce rounding activities to one day per week to allow staff an opportunity to become used to the change. For the month of April, 27 patients were rounded on in the waiting area and of those, one LWBS. Conclusion: Rounding activities will continue one day per week until compliance is enough to show whether or not it affects LWBS. Management has promised to step up with enforcement and create accountability for following through with the change

Models and methods for recovering shape, reflectance, and illumination from images

Recovery of scene shape, reflectance, and illumination are of fundamental importance to computer vision. However, the image formation process involves complex interactions between all three components, making inference difficult or impossible in the absence of simplifying models or prior scene knowledge. Unfortunately, real-world scenes often violate these approximations, leading to biased or incorrect reconstructions. Thus, there is a constant struggle between model complexity and tractability. In this dissertation, new models and methods are presented for recovering shape, reflectance, and illumination which are valid for broader classes of scenes than competing techniques. Underlying all of the research presented is the ability to handle objects with complex reflectance. First, a novel approach is presented for resolving the generalized bas-relief ambiguity which arises in uncalibrated photometric stereo. Previous work showed that the ambiguity can be resolved for textureless objects; however, as shown in this dissertation, it is also possible to resolve the ambiguity for textured objects, provided there is statistical regularity in the distribution of albedo values across the surface. Next, a photometric stereo algorithm is presented that is capable of handling nearly arbitrary reflectance. The main contribution is the utilization of bilateral symmetry in the reflectance function, a property shared by most real- world materials. By explicitly utilizing symmetry, surface shape can be constrained without relying on parametric models; a significant advance over most photometric stereo algorithms which depend on simple parametric models of surface reflectance, such as the Lambertian model. Another photometric stereo algorithm is also presented that is capable of fully recovering the surface shape as well as the reflectance function across the surface. While a few additional constraints are necessary, this is one of only a handful of photometric stereo methods capable of simultaneously recovering shape and complex reflectance; of these, the reflectance model is by far the least restrictive. It is also shown that bilateral symmetry of the reflectance function can be exploited for multi-view shape reconstruction. The method presented handles both textured and textureless surfaces and is capable of recovering surface concavities. Finally, a novel technique for measuring illumination is presented which relies on spatially varying reflectanc

Models and methods for recovering shape, reflectance, and illumination from images

Recovery of scene shape, reflectance, and illumination are of fundamental importance to computer vision. However, the image formation process involves complex interactions between all three components, making inference difficult or impossible in the absence of simplifying models or prior scene knowledge. Unfortunately, real-world scenes often violate these approximations, leading to biased or incorrect reconstructions. Thus, there is a constant struggle between model complexity and tractability. In this dissertation, new models and methods are presented for recovering shape, reflectance, and illumination which are valid for broader classes of scenes than competing techniques. Underlying all of the research presented is the ability to handle objects with complex reflectance. First, a novel approach is presented for resolving the generalized bas-relief ambiguity which arises in uncalibrated photometric stereo. Previous work showed that the ambiguity can be resolved for textureless objects; however, as shown in this dissertation, it is also possible to resolve the ambiguity for textured objects, provided there is statistical regularity in the distribution of albedo values across the surface. Next, a photometric stereo algorithm is presented that is capable of handling nearly arbitrary reflectance. The main contribution is the utilization of bilateral symmetry in the reflectance function, a property shared by most real- world materials. By explicitly utilizing symmetry, surface shape can be constrained without relying on parametric models; a significant advance over most photometric stereo algorithms which depend on simple parametric models of surface reflectance, such as the Lambertian model. Another photometric stereo algorithm is also presented that is capable of fully recovering the surface shape as well as the reflectance function across the surface. While a few additional constraints are necessary, this is one of only a handful of photometric stereo methods capable of simultaneously recovering shape and complex reflectance; of these, the reflectance model is by far the least restrictive. It is also shown that bilateral symmetry of the reflectance function can be exploited for multi-view shape reconstruction. The method presented handles both textured and textureless surfaces and is capable of recovering surface concavities. Finally, a novel technique for measuring illumination is presented which relies on spatially varying reflectanc

CSE202 Project- Consistent Hashing

Consistent hashing is a special kind of hashing which is useful for certain applications. Originally devised by Karger et. al. at MIT[2] for use in distribute