Todd Kosloff - Academia.edu (original) (raw)

Papers by Todd Kosloff

Fast Image Filters for Depth of Field Post-Processing

Traditionally, images are represented on a computer as an ar r y of pixels, where each pixel is a... more Traditionally, images are represented on a computer as an ar r y of pixels, where each pixel is assigned red, green, and blue col or intensities. Each color channel is an 8 bit integer, meaning it can take on integer values ranging from 0 to 255. This numeric represe ntation corresponds to the display devices we have available. A value of 0 means ”the darkest that the display device can display”, which might mean making an lcd element as black as possible, or it mi ght mean placing pure black ink at that spot on the paper. The high end, 255, means ”as bright as possible”, perhaps by leaving t he lcd element as open as it goes, or by not placing any ink down on the paper, leaving the paper’s natural whiteness.

Gi, 2009

Figure 1: Spreading vs. Gathering. Left: Gathering leads to sharp silhouettes on blurred objects.... more Figure 1: Spreading vs. Gathering. Left: Gathering leads to sharp silhouettes on blurred objects. Right: Spreading correctly blurs silhouettes. This scene uses two layers, one for the background, one for the foreground.

The Art of “Mathematics for Industry”, 2010

Depth of field refers to the swath that is imaged in sufficient focus through an optics system, s... more Depth of field refers to the swath that is imaged in sufficient focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the laws of physics and by physical constraints. Depth of field has been rendered in computer graphics, but usually with the same limited control as found in real camera lenses. In this paper, we generalize depth of field in computer graphics by allowing the user to specify the distribution of blur throughout a scene in a more flexible manner. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to select objects from a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. We present three approaches for rendering generalized depth of field based on nonlinear distributed ray tracing, compositing, and simulated heat diffusion. Each of these methods has a different set of strengths and weaknesses, so it is useful to have all three available. The ray tracing approach allows the amount of blur to vary with depth in an arbitrary way. The compositing method creates a synthetic image with focus and aperture settings that vary per-pixel. The diffusion approach provides full generality by allowing each point in 3D space to have an arbitrary amount of blur.

Lecture Notes in Computer Science, 2000

Depth of field is the swath through a 3D scene that is imaged in acceptable focus through an opti... more Depth of field is the swath through a 3D scene that is imaged in acceptable focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the laws of physics and by physical constraints. The depth of field effect has been simulated in computer graphics, but with the same limited control as found in real camera lenses. In this report, we use anisotropic diffusion to generalize depth of field in computer graphics by allowing the user to independently specify the degree of blur at each point in three-dimensional space. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to pick objects out of a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. Our algorithm operates by blurring a sequence of nonplanar layers that form the scene. Choosing a suitable blur algorithm for the layers is critical; thus, we develop appropriate blur semantics such that the blur algorithm will properly generalize depth of field. We found that anisotropic diffusion is the process that best suits these semantics.

In this paper, we introduce a technique called filter spreading, which provides a novel mechanism... more In this paper, we introduce a technique called filter spreading, which provides a novel mechanism for filtering signals such as images. By using the repeated-integration technique of Heckbert, and the fast summed-area table construction technique of Hensley, we can implement fast filter spreading in real-time using current graphics processors. Our fast implementation of filter spreading is achieved by running the

Depth of field refers to the swath that is imaged in sharp focus through an optics system, such a... more Depth of field refers to the swath that is imaged in sharp focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool, which can be used, for example, to emphasize the subject of a photograph. The most efficient algorithms for simulating depth of field are post-processing methods. Post-processing can be made more efficient by making various approximations. We start with the assumption that the point spread function (PSF) is Gaussian. This assumption introduces structure into the problem which we exploit to achieve speed. Two methods will be presented. In our first approach, which we call pyramid spreading, PSFs are spread into a pyramid. By writing larger PSFs to coarser levels of the pyramid, the performance remains constant, independent of the size of the PSFs. After spreading all the PSFs, the pyramid is then collapsed to yield the final blurred image. Our second approach, called the tensor method, exploits the fact that blurring is a linear operator. The operator is treated as a large tensor which is compressed by finding structure in it. The compressed representation is then used to directly blur the image. Both methods present new perspectives on the problem of efficiently blurring an image.

Computer generated images by default render the entire scene in perfect focus. Both camera optics... more Computer generated images by default render the entire scene in perfect focus. Both camera optics and the human visual system have limited depth of field, due to the finite aperture or pupil of the optical system. For more realistic computer graphics as well as to enable artistic control over what is and what is not in focus, it is desirable to add depth of field blurring. Starting with the work of Potmesil and Chakravarty[33][34], there have been numerous approaches to adding depth of field effects to computer graphics. Published work in depth of field for computer graphics has been previously surveyed by Barsky [2][3]. Later, interactive depth of field techniques were surveyed by Demers [12]. Subsequent to these surveys, however, there have been important developments. This paper surveys depth of field approaches in computer graphics, from its introduction to the current state of the art. Figure 1: (left) Image before and after depth of field has been added via postprocessing (cou...

Lecture Notes in Computer Science, 2006

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement II, 2003

Signaling System 7 (SS7) is vital to signaling and control in America's public telephone net... more Signaling System 7 (SS7) is vital to signaling and control in America's public telephone networks. This paper describes a class of attacks on SS7 networks involving the insertion of malicious signaling messages via compromised SS7 network components. Three attacks are discussed in detail: IAM flood attacks, redirection attacks and point code spoofing attacks. Depending on their scale of execution, these attacks can produce effects ranging from network congestion to service disruption. Methods for detecting these denial-of-service attacks and mitigating their effects are also presented.

Proceedings of the 1st Symposium on Applied perception in graphics and visualization - APGV '04, 2004

ABSTRACT Low light level affects human visual perception in various ways. Visual acuity is reduce... more ABSTRACT Low light level affects human visual perception in various ways. Visual acuity is reduced and scenes appear bluer, darker, less saturated, and with reduced contrast. We confine our attention to an approach to modeling the appearance of the bluish cast in dim light, which is known as blue shift. Both photographs and computer-generated images of night scenes can be made to appear more realistic by understanding these phenomena as well as how they are produced by the retina. The retina comprises two kinds of photoreceptors, called rods and cones. The rods are more sensitive in dim light than are the cones. Although there are three different kinds of cones with different spectral sensitivity curves, all rods have the same spectral response curve. Consequently, rods provide luminance information but no color discrimination. Thus, when the light is too dim to fully excite the cones, scenes appear desaturated. The opponent process theory of color vision [Hurvich and Jameson 1957] states that the outputs of the rods and cones are encoded as red-green, yellow-blue, and white-black opponent channels. We model loss of saturation and blue shift in this opponent color space.

Lecture Notes in Computer Science, 2005

In current 3D graphics architectures, the bus between the triangle server and the rendering engin... more In current 3D graphics architectures, the bus between the triangle server and the rendering engine GPU is clogged with triangle vertices and their many attributes (normal vectors, colors, texture coordinates). We develop a new 3D graphics architecture using data compression to unclog the bus between the triangle server and the rendering engine. The data compression is achieved by replacing the conventional idea of a GPU that renders triangles with a GPU that tessellates surface patches into triangles. 224-231 V.S. Sunderam et al. (Eds.): ICCS 2005, LNCS 3515, pp. , 2005. © Springer-Verlag Berlin Heidelberg 2005

Technical Report No. UCB/EECS-2009-54 http://www.eecs.berkeley.edu/Pubs/TechRpts/2009 /EECS-2009-... more Technical Report No. UCB/EECS-2009-54 http://www.eecs.berkeley.edu/Pubs/TechRpts/2009 /EECS-2009-54.html ... Copyright 2009, by the author(s). All rights reserved. ... Permission to make digital or hard copies of all or part of this work for personal or classroom use is ...

Computer generated images by default render the entire scene in perfect focus. Both camera optics... more Computer generated images by default render the entire scene in perfect focus. Both camera optics and the human visual system have limited depth of field, due to the finite aperture or pupil of the optical system. For more realistic computer graphics as well as to enable artistic control over what is and what is not in focus, it is desirable to add depth of field blurring. Starting with the work of Potmesil and Chakravarty [34], there have been numerous approaches to adding depth of field effects to computer graphics. Published work in depth of field for computer graphics has been previously surveyed by Barsky [2][3]. Later, interactive depth of field techniques were surveyed by Demers . Subsequent to these surveys, however, there have been important developments. This paper surveys depth of field approaches in computer graphics, from its introduction to the current state of the art. Figure 1: (left) Image before and after depth of field has been added via postprocessing (courtesy of Indranil Chakravarty [33]). (right) A dragon scene rendered with a distributed ray tracing approach (courtesy of Magnus Strengert [23]).

This paper examines vulnerabilities present within SS7 networks-vulnerabilities whose threat has ... more This paper examines vulnerabilities present within SS7 networks-vulnerabilities whose threat has been magnified by deregulation and emerging trends in network technology. First, it provides an overview of the SS7 network and protocol. Then it explains how modem deregulated telephone networks combine with next-generation technologies in a manner that poses a threat to the security of the telecommunications signaling network. This

Control over what is in focus and what is not in focus in an image is an important artistic tool.... more Control over what is in focus and what is not in focus in an image is an important artistic tool. The range of depth in a 3D scene that is imaged in sufficient focus through an optics system, such as a camera lens, is called depth of field. Without depth of field, everything appears completely in sharp focus, leading to an unnatural, overly crisp appearance. Current techniques for rendering depth of field in computer graphics are either slow or suffer from artifacts and limitations in the type of blur. In this paper, we present a new image filter based on rectangle spreading which is constant time per pixel. When used in a layered depth of field framework, it eliminates the intensity leakage and depth discontinuity artifacts that occur in previous methods. We also present several extensions to our rectangle spreading method to allow flexibility in the appearance of the blur through control over the point spread function.

Fast Image Filters for Depth of Field Post-Processing

Traditionally, images are represented on a computer as an ar r y of pixels, where each pixel is a... more Traditionally, images are represented on a computer as an ar r y of pixels, where each pixel is assigned red, green, and blue col or intensities. Each color channel is an 8 bit integer, meaning it can take on integer values ranging from 0 to 255. This numeric represe ntation corresponds to the display devices we have available. A value of 0 means ”the darkest that the display device can display”, which might mean making an lcd element as black as possible, or it mi ght mean placing pure black ink at that spot on the paper. The high end, 255, means ”as bright as possible”, perhaps by leaving t he lcd element as open as it goes, or by not placing any ink down on the paper, leaving the paper’s natural whiteness.

Gi, 2009

Figure 1: Spreading vs. Gathering. Left: Gathering leads to sharp silhouettes on blurred objects.... more Figure 1: Spreading vs. Gathering. Left: Gathering leads to sharp silhouettes on blurred objects. Right: Spreading correctly blurs silhouettes. This scene uses two layers, one for the background, one for the foreground.

The Art of “Mathematics for Industry”, 2010

Depth of field refers to the swath that is imaged in sufficient focus through an optics system, s... more Depth of field refers to the swath that is imaged in sufficient focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the laws of physics and by physical constraints. Depth of field has been rendered in computer graphics, but usually with the same limited control as found in real camera lenses. In this paper, we generalize depth of field in computer graphics by allowing the user to specify the distribution of blur throughout a scene in a more flexible manner. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to select objects from a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. We present three approaches for rendering generalized depth of field based on nonlinear distributed ray tracing, compositing, and simulated heat diffusion. Each of these methods has a different set of strengths and weaknesses, so it is useful to have all three available. The ray tracing approach allows the amount of blur to vary with depth in an arbitrary way. The compositing method creates a synthetic image with focus and aperture settings that vary per-pixel. The diffusion approach provides full generality by allowing each point in 3D space to have an arbitrary amount of blur.

Lecture Notes in Computer Science, 2000

Depth of field is the swath through a 3D scene that is imaged in acceptable focus through an opti... more Depth of field is the swath through a 3D scene that is imaged in acceptable focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the laws of physics and by physical constraints. The depth of field effect has been simulated in computer graphics, but with the same limited control as found in real camera lenses. In this report, we use anisotropic diffusion to generalize depth of field in computer graphics by allowing the user to independently specify the degree of blur at each point in three-dimensional space. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to pick objects out of a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. Our algorithm operates by blurring a sequence of nonplanar layers that form the scene. Choosing a suitable blur algorithm for the layers is critical; thus, we develop appropriate blur semantics such that the blur algorithm will properly generalize depth of field. We found that anisotropic diffusion is the process that best suits these semantics.

In this paper, we introduce a technique called filter spreading, which provides a novel mechanism... more In this paper, we introduce a technique called filter spreading, which provides a novel mechanism for filtering signals such as images. By using the repeated-integration technique of Heckbert, and the fast summed-area table construction technique of Hensley, we can implement fast filter spreading in real-time using current graphics processors. Our fast implementation of filter spreading is achieved by running the

Depth of field refers to the swath that is imaged in sharp focus through an optics system, such a... more Depth of field refers to the swath that is imaged in sharp focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool, which can be used, for example, to emphasize the subject of a photograph. The most efficient algorithms for simulating depth of field are post-processing methods. Post-processing can be made more efficient by making various approximations. We start with the assumption that the point spread function (PSF) is Gaussian. This assumption introduces structure into the problem which we exploit to achieve speed. Two methods will be presented. In our first approach, which we call pyramid spreading, PSFs are spread into a pyramid. By writing larger PSFs to coarser levels of the pyramid, the performance remains constant, independent of the size of the PSFs. After spreading all the PSFs, the pyramid is then collapsed to yield the final blurred image. Our second approach, called the tensor method, exploits the fact that blurring is a linear operator. The operator is treated as a large tensor which is compressed by finding structure in it. The compressed representation is then used to directly blur the image. Both methods present new perspectives on the problem of efficiently blurring an image.

Computer generated images by default render the entire scene in perfect focus. Both camera optics... more Computer generated images by default render the entire scene in perfect focus. Both camera optics and the human visual system have limited depth of field, due to the finite aperture or pupil of the optical system. For more realistic computer graphics as well as to enable artistic control over what is and what is not in focus, it is desirable to add depth of field blurring. Starting with the work of Potmesil and Chakravarty[33][34], there have been numerous approaches to adding depth of field effects to computer graphics. Published work in depth of field for computer graphics has been previously surveyed by Barsky [2][3]. Later, interactive depth of field techniques were surveyed by Demers [12]. Subsequent to these surveys, however, there have been important developments. This paper surveys depth of field approaches in computer graphics, from its introduction to the current state of the art. Figure 1: (left) Image before and after depth of field has been added via postprocessing (cou...

Lecture Notes in Computer Science, 2006

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement II, 2003

Signaling System 7 (SS7) is vital to signaling and control in America's public telephone net... more Signaling System 7 (SS7) is vital to signaling and control in America's public telephone networks. This paper describes a class of attacks on SS7 networks involving the insertion of malicious signaling messages via compromised SS7 network components. Three attacks are discussed in detail: IAM flood attacks, redirection attacks and point code spoofing attacks. Depending on their scale of execution, these attacks can produce effects ranging from network congestion to service disruption. Methods for detecting these denial-of-service attacks and mitigating their effects are also presented.

Proceedings of the 1st Symposium on Applied perception in graphics and visualization - APGV '04, 2004

ABSTRACT Low light level affects human visual perception in various ways. Visual acuity is reduce... more ABSTRACT Low light level affects human visual perception in various ways. Visual acuity is reduced and scenes appear bluer, darker, less saturated, and with reduced contrast. We confine our attention to an approach to modeling the appearance of the bluish cast in dim light, which is known as blue shift. Both photographs and computer-generated images of night scenes can be made to appear more realistic by understanding these phenomena as well as how they are produced by the retina. The retina comprises two kinds of photoreceptors, called rods and cones. The rods are more sensitive in dim light than are the cones. Although there are three different kinds of cones with different spectral sensitivity curves, all rods have the same spectral response curve. Consequently, rods provide luminance information but no color discrimination. Thus, when the light is too dim to fully excite the cones, scenes appear desaturated. The opponent process theory of color vision [Hurvich and Jameson 1957] states that the outputs of the rods and cones are encoded as red-green, yellow-blue, and white-black opponent channels. We model loss of saturation and blue shift in this opponent color space.

Lecture Notes in Computer Science, 2005

In current 3D graphics architectures, the bus between the triangle server and the rendering engin... more In current 3D graphics architectures, the bus between the triangle server and the rendering engine GPU is clogged with triangle vertices and their many attributes (normal vectors, colors, texture coordinates). We develop a new 3D graphics architecture using data compression to unclog the bus between the triangle server and the rendering engine. The data compression is achieved by replacing the conventional idea of a GPU that renders triangles with a GPU that tessellates surface patches into triangles. 224-231 V.S. Sunderam et al. (Eds.): ICCS 2005, LNCS 3515, pp. , 2005. © Springer-Verlag Berlin Heidelberg 2005

Technical Report No. UCB/EECS-2009-54 http://www.eecs.berkeley.edu/Pubs/TechRpts/2009 /EECS-2009-... more Technical Report No. UCB/EECS-2009-54 http://www.eecs.berkeley.edu/Pubs/TechRpts/2009 /EECS-2009-54.html ... Copyright 2009, by the author(s). All rights reserved. ... Permission to make digital or hard copies of all or part of this work for personal or classroom use is ...

Computer generated images by default render the entire scene in perfect focus. Both camera optics... more Computer generated images by default render the entire scene in perfect focus. Both camera optics and the human visual system have limited depth of field, due to the finite aperture or pupil of the optical system. For more realistic computer graphics as well as to enable artistic control over what is and what is not in focus, it is desirable to add depth of field blurring. Starting with the work of Potmesil and Chakravarty [34], there have been numerous approaches to adding depth of field effects to computer graphics. Published work in depth of field for computer graphics has been previously surveyed by Barsky [2][3]. Later, interactive depth of field techniques were surveyed by Demers . Subsequent to these surveys, however, there have been important developments. This paper surveys depth of field approaches in computer graphics, from its introduction to the current state of the art. Figure 1: (left) Image before and after depth of field has been added via postprocessing (courtesy of Indranil Chakravarty [33]). (right) A dragon scene rendered with a distributed ray tracing approach (courtesy of Magnus Strengert [23]).

This paper examines vulnerabilities present within SS7 networks-vulnerabilities whose threat has ... more This paper examines vulnerabilities present within SS7 networks-vulnerabilities whose threat has been magnified by deregulation and emerging trends in network technology. First, it provides an overview of the SS7 network and protocol. Then it explains how modem deregulated telephone networks combine with next-generation technologies in a manner that poses a threat to the security of the telecommunications signaling network. This

Control over what is in focus and what is not in focus in an image is an important artistic tool.... more Control over what is in focus and what is not in focus in an image is an important artistic tool. The range of depth in a 3D scene that is imaged in sufficient focus through an optics system, such as a camera lens, is called depth of field. Without depth of field, everything appears completely in sharp focus, leading to an unnatural, overly crisp appearance. Current techniques for rendering depth of field in computer graphics are either slow or suffer from artifacts and limitations in the type of blur. In this paper, we present a new image filter based on rectangle spreading which is constant time per pixel. When used in a layered depth of field framework, it eliminates the intensity leakage and depth discontinuity artifacts that occur in previous methods. We also present several extensions to our rectangle spreading method to allow flexibility in the appearance of the blur through control over the point spread function.