Fast Bokeh Effects Using Low-Rank Linear Filters
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
“Digital Single Lens Reflex”
PHOTOGRAPHY GENERIC ELECTIVE SEM-II DSLR stands for “Digital Single Lens Reflex”. In simple language, a DSLR is a digital camera that uses a mirror mechanism to either reflect light from a camera lens to an optical viewfinder (which is an eyepiece on the back of the camera that one looks through to see what they are taking a picture of) or let light fully pass onto the image sensor (which captures the image) by moving the mirror out of the way. Although single lens reflex cameras have been available in various shapes and forms since the 19th century with film as the recording medium, the first commercial digital SLR with an image sensor appeared in 1991. Compared to point-and-shoot and phone cameras, DSLR cameras typically use interchangeable lenses. Take a look at the following image of an SLR cross section (image courtesy of Wikipedia): When you look through a DSLR viewfinder / eyepiece on the back of the camera, whatever you see is passed through the lens attached to the camera, which means that you could be looking at exactly what you are going to capture. Light from the scene you are attempting to capture passes through the lens into a reflex mirror (#2) that sits at a 45 degree angle inside the camera chamber, which then forwards the light vertically to an optical element called a “pentaprism” (#7). The pentaprism then converts the vertical light to horizontal by redirecting the light through two separate mirrors, right into the viewfinder (#8). When you take a picture, the reflex mirror (#2) swings upwards, blocking the vertical pathway and letting the light directly through. -
Visual Tilt Estimation for Planar-Motion Methods in Indoor Mobile Robots
robotics Article Visual Tilt Estimation for Planar-Motion Methods in Indoor Mobile Robots David Fleer Computer Engineering Group, Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany; dfl[email protected]; Tel.: +49-521-106-5279 Received: 22 September 2017; Accepted: 28 October 2017; Published: date Abstract: Visual methods have many applications in mobile robotics problems, such as localization, navigation, and mapping. Some methods require that the robot moves in a plane without tilting. This planar-motion assumption simplifies the problem, and can lead to improved results. However, tilting the robot violates this assumption, and may cause planar-motion methods to fail. Such a tilt should therefore be corrected. In this work, we estimate a robot’s tilt relative to a ground plane from individual panoramic images. This estimate is based on the vanishing point of vertical elements, which commonly occur in indoor environments. We test the quality of two methods on images from several environments: An image-space method exploits several approximations to detect the vanishing point in a panoramic fisheye image. The vector-consensus method uses a calibrated camera model to solve the tilt-estimation problem in 3D space. In addition, we measure the time required on desktop and embedded systems. We previously studied visual pose-estimation for a domestic robot, including the effect of tilts. We use these earlier results to establish meaningful standards for the estimation error and time. Overall, we find the methods to be accurate and fast enough for real-time use on embedded systems. However, the tilt-estimation error increases markedly in environments containing relatively few vertical edges. -
Depth-Aware Blending of Smoothed Images for Bokeh Effect Generation
1 Depth-aware Blending of Smoothed Images for Bokeh Effect Generation Saikat Duttaa,∗∗ aIndian Institute of Technology Madras, Chennai, PIN-600036, India ABSTRACT Bokeh effect is used in photography to capture images where the closer objects look sharp and every- thing else stays out-of-focus. Bokeh photos are generally captured using Single Lens Reflex cameras using shallow depth-of-field. Most of the modern smartphones can take bokeh images by leveraging dual rear cameras or a good auto-focus hardware. However, for smartphones with single-rear camera without a good auto-focus hardware, we have to rely on software to generate bokeh images. This kind of system is also useful to generate bokeh effect in already captured images. In this paper, an end-to-end deep learning framework is proposed to generate high-quality bokeh effect from images. The original image and different versions of smoothed images are blended to generate Bokeh effect with the help of a monocular depth estimation network. The proposed approach is compared against a saliency detection based baseline and a number of approaches proposed in AIM 2019 Challenge on Bokeh Effect Synthesis. Extensive experiments are shown in order to understand different parts of the proposed algorithm. The network is lightweight and can process an HD image in 0.03 seconds. This approach ranked second in AIM 2019 Bokeh effect challenge-Perceptual Track. 1. Introduction tant problem in Computer Vision and has gained attention re- cently. Most of the existing approaches(Shen et al., 2016; Wad- Depth-of-field effect or Bokeh effect is often used in photog- hwa et al., 2018; Xu et al., 2018) work on human portraits by raphy to generate aesthetic pictures. -
Video Tripod Head
thank you for choosing magnus. One (1) year limited warranty Congratulations on your purchase of the VPH-20 This MAGNUS product is warranted to the original purchaser Video Pan Head by Magnus. to be free from defects in materials and workmanship All Magnus Video Heads are designed to balance under normal consumer use for a period of one (1) year features professionals want with the affordability they from the original purchase date or thirty (30) days after need. They’re durable enough to provide many years replacement, whichever occurs later. The warranty provider’s of trouble-free service and enjoyment. Please carefully responsibility with respect to this limited warranty shall be read these instructions before setting up and using limited solely to repair or replacement, at the provider’s your Video Pan Head. discretion, of any product that fails during normal use of this product in its intended manner and in its intended VPH-20 Box Contents environment. Inoperability of the product or part(s) shall be determined by the warranty provider. If the product has • VPH-20 Video Pan Head Owner’s been discontinued, the warranty provider reserves the right • 3/8” and ¼”-20 reducing bushing to replace it with a model of equivalent quality and function. manual This warranty does not cover damage or defect caused by misuse, • Quick-release plate neglect, accident, alteration, abuse, improper installation or maintenance. EXCEPT AS PROVIDED HEREIN, THE WARRANTY Key Features PROVIDER MAKES NEITHER ANY EXPRESS WARRANTIES NOR ANY IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED Tilt-Tension Adjustment Knob TO ANY IMPLIED WARRANTY OF MERCHANTABILITY Tilt Lock OR FITNESS FOR A PARTICULAR PURPOSE. -
Portraiture, Surveillance, and the Continuity Aesthetic of Blur
Michigan Technological University Digital Commons @ Michigan Tech Michigan Tech Publications 6-22-2021 Portraiture, Surveillance, and the Continuity Aesthetic of Blur Stefka Hristova Michigan Technological University, [email protected] Follow this and additional works at: https://digitalcommons.mtu.edu/michigantech-p Part of the Arts and Humanities Commons Recommended Citation Hristova, S. (2021). Portraiture, Surveillance, and the Continuity Aesthetic of Blur. Frames Cinema Journal, 18, 59-98. http://doi.org/10.15664/fcj.v18i1.2249 Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15062 Follow this and additional works at: https://digitalcommons.mtu.edu/michigantech-p Part of the Arts and Humanities Commons Portraiture, Surveillance, and the Continuity Aesthetic of Blur Stefka Hristova DOI:10.15664/fcj.v18i1.2249 Frames Cinema Journal ISSN 2053–8812 Issue 18 (Jun 2021) http://www.framescinemajournal.com Frames Cinema Journal, Issue 18 (June 2021) Portraiture, Surveillance, and the Continuity Aesthetic of Blur Stefka Hristova Introduction With the increasing transformation of photography away from a camera-based analogue image-making process into a computerised set of procedures, the ontology of the photographic image has been challenged. Portraits in particular have become reconfigured into what Mark B. Hansen has called “digital facial images” and Mitra Azar has subsequently reworked into “algorithmic facial images.” 1 This transition has amplified the role of portraiture as a representational device, as a node in a network -
Surface Tilt (The Direction of Slant): a Neglected Psychophysical Variable
Perception & Psychophysics 1983,33 (3),241-250 Surface tilt (the direction of slant): A neglected psychophysical variable KENT A. STEVENS Massachusetts InstituteofTechnology, Cambridge, Massachusetts Surface slant (the angle between the line of sight and the surface normal) is an important psy chophysical variable. However, slant angle captures only one of the two degrees of freedom of surface orientation, the other being the direction of slant. Slant direction, measured in the image plane, coincides with the direction of the gradient of distance from viewer to surface and, equivalently, with the direction the surface normal would point if projected onto the image plane. Since slant direction may be quantified by the tilt of the projected normal (which ranges over 360 deg in the frontal plane), it is referred to here as surfacetilt. (Note that slant angle is mea sured perpendicular to the image plane, whereas tilt angle is measured in the image plane.) Com pared with slant angle's popularity as a psychophysical variable, the attention paid to surface tilt seems undeservedly scant. Experiments that demonstrate a technique for measuring ap parent surface tilt are reported. The experimental stimuli were oblique crosses and parallelo grams, which suggest oriented planes in SoD. The apparent tilt of the plane might beprobed by orienting a needle in SoD so as to appear normal, projecting the normal onto the image plane, and measuring its direction (e.g., relative to the horizontal). It is shown to be preferable, how ever, to merely rotate a line segment in 2-D, superimposed on the display, until it appears nor mal to the perceived surface. -
Rethinking Coalitions: Anti-Pornography Feminists, Conservatives, and Relationships Between Collaborative Adversarial Movements
Rethinking Coalitions: Anti-Pornography Feminists, Conservatives, and Relationships between Collaborative Adversarial Movements Nancy Whittier This research was partially supported by the Center for Advanced Study in Behavioral Sciences. The author thanks the following people for their comments: Martha Ackelsberg, Steven Boutcher, Kai Heidemann, Holly McCammon, Ziad Munson, Jo Reger, Marc Steinberg, Kim Voss, the anonymous reviewers for Social Problems, and editor Becky Pettit. A previous version of this paper was presented at the 2011 Annual Meetings of the American Sociological Association. Direct correspondence to Nancy Whittier, 10 Prospect St., Smith College, Northampton MA 01063. Email: [email protected]. 1 Abstract Social movements interact in a wide range of ways, yet we have only a few concepts for thinking about these interactions: coalition, spillover, and opposition. Many social movements interact with each other as neither coalition partners nor opposing movements. In this paper, I argue that we need to think more broadly and precisely about the relationships between movements and suggest a framework for conceptualizing non- coalitional interaction between movements. Although social movements scholars have not theorized such interactions, “strange bedfellows” are not uncommon. They differ from coalitions in form, dynamics, relationship to larger movements, and consequences. I first distinguish types of relationships between movements based on extent of interaction and ideological congruence and describe the relationship between collaborating, ideologically-opposed movements, which I call “collaborative adversarial relationships.” Second, I differentiate among the dimensions along which social movements may interact and outline the range of forms that collaborative adversarial relationships may take. Third, I theorize factors that influence collaborative adversarial relationships’ development over time, the effects on participants and consequences for larger movements, in contrast to coalitions. -
Tilt Anisoplanatism in Laser-Guide-Star-Based Multiconjugate Adaptive Optics
A&A 400, 1199–1207 (2003) Astronomy DOI: 10.1051/0004-6361:20030022 & c ESO 2003 Astrophysics Tilt anisoplanatism in laser-guide-star-based multiconjugate adaptive optics Reconstruction of the long exposure point spread function from control loop data R. C. Flicker1,2,F.J.Rigaut2, and B. L. Ellerbroek2 1 Lund Observatory, Box 43, 22100 Lund, Sweden 2 Gemini Observatory, 670 N. A’Ohoku Pl., Hilo HI-96720, USA Received 22 August 2002 / Accepted 6 January 2003 Abstract. A method is presented for estimating the long exposure point spread function (PSF) degradation due to tilt aniso- planatism in a laser-guide-star-based multiconjugate adaptive optics systems from control loop data. The algorithm is tested in numerical Monte Carlo simulations of the separately driven low-order null-mode system, and is shown to be robust and accurate with less than 10% relative error in both H and K bands down to a natural guide star (NGS) magnitude of mR = 21, for a symmetric asterism with three NGS on a 30 arcsec radius. The H band limiting magnitude of the null-mode system due to NGS signal-to-noise ratio and servo-lag was estimated previously to mR = 19. At this magnitude, the relative errors in the reconstructed PSF and Strehl are here found to be less than 5% and 1%, suggesting that the PSF retrieval algorithm will be applicable and reliable for the full range of operating conditions of the null-mode system. Key words. Instrumentation – adaptive optics – methods – statistical 1. Introduction algorithms is much reduced. Set to amend the two failings, to increase the sky coverage and reduce anisoplanatism, are 1.1. -
Depth of Field Lenses Form Images of Objects a Predictable Distance Away from the Lens. the Distance from the Image to the Lens Is the Image Distance
Depth of Field Lenses form images of objects a predictable distance away from the lens. The distance from the image to the lens is the image distance. Image distance depends on the object distance (distance from object to the lens) and the focal length of the lens. Figure 1 shows how the image distance depends on object distance for lenses with focal lengths of 35 mm and 200 mm. Figure 1: Dependence Of Image Distance Upon Object Distance Cameras use lenses to focus the images of object upon the film or exposure medium. Objects within a photographic Figure 2 scene are usually a varying distance from the lens. Because a lens is capable of precisely focusing objects of a single distance, some objects will be precisely focused while others will be out of focus and even blurred. Skilled photographers strive to maximize the depth of field within their photographs. Depth of field refers to the distance between the nearest and the farthest objects within a photographic scene that are acceptably focused. Figure 2 is an example of a photograph with a shallow depth of field. One variable that affects depth of field is the f-number. The f-number is the ratio of the focal length to the diameter of the aperture. The aperture is the circular opening through which light travels before reaching the lens. Table 1 shows the dependence of the depth of field (DOF) upon the f-number of a digital camera. Table 1: Dependence of Depth of Field Upon f-Number and Camera Lens 35-mm Camera Lens 200-mm Camera Lens f-Number DN (m) DF (m) DOF (m) DN (m) DF (m) DOF (m) 2.8 4.11 6.39 2.29 4.97 5.03 0.06 4.0 3.82 7.23 3.39 4.95 5.05 0.10 5.6 3.48 8.86 5.38 4.94 5.07 0.13 8.0 3.09 13.02 9.93 4.91 5.09 0.18 22.0 1.82 Infinity Infinite 4.775 5.27 0.52 The DN value represents the nearest object distance that is acceptably focused. -
What's a Megapixel Lens and Why Would You Need One?
Theia Technologies white paper Page 1 of 3 What's a Megapixel Lens and Why Would You Need One? It's an exciting time in the security department. You've finally received approval to migrate from your installed analog cameras to new megapixel models and Also translated in Russian: expectations are high. As you get together with your integrator, you start selecting Что такое мегапиксельный the cameras you plan to install, looking forward to getting higher quality, higher объектив и для чего он resolution images and, in many cases, covering the same amount of ground with нужен one camera that, otherwise, would have taken several analog models. You're covering the basics of those megapixel cameras, including the housing and mounting hardware. What about the lens? You've just opened up Pandora's Box. A Lens Is Not a Lens Is Not a Lens The lens needed for an IP/megapixel camera is much different than the lens needed for a traditional analog camera. These higher resolution cameras demand higher quality lenses. For instance, in a megapixel camera, the focal plane spot size of the lens must be comparable or smaller than the pixel size on the sensor (Figures 1 and 2). To do this, more elements, and higher precision elements, are required for megapixel camera lenses which can make them more costly than their analog counterparts. Figure 1 Figure 2 Spot size of a megapixel lens is much smaller, required Spot size of a standard lens won't allow sharp focus on for good focus on megapixel sensors. a megapixel sensor. -
EVERYDAY MAGIC Bokeh
EVERYDAY MAGIC Bokeh “Our goal should be to perceive the extraordinary in the ordinary, and when we get good enough, to live vice versa, in the ordinary extraordinary.” ~ Eric Booth Welcome to Lesson Two of Everyday Magic. In this week’s lesson we are going to dig deep into those magical little orbs of light in a photograph known as bokeh. Pronounced BOH-Kə (or BOH-kay), the word “bokeh” is an English translation of the Japanese word boke, which means “blur” or “haze”. What is Bokeh? bokeh. And it is the camera lens and how it renders the out of focus light in Photographically speaking, bokeh is the background that gives bokeh its defined as the aesthetic quality of the more or less circular appearance. blur produced by the camera lens in the out-of-focus parts of an image. But what makes this unique visual experience seem magical is the fact that we are not able to ‘see’ bokeh with our superior human vision and excellent depth of field. Bokeh is totally a function ‘seeing’ through the lens. Playing with Focal Distance In addition to a shallow depth of field, the bokeh in an image is also determined by 1) the distance between the subject and the background and 2) the distance between the lens and the subject. Depending on how you Bokeh and Depth of Field compose your image, the bokeh can be smooth and ‘creamy’ in appearance or it The key to achieving beautiful bokeh in can be livelier and more energetic. your images is by shooting with a shallow depth of field (DOF) which is the amount of an image which is appears acceptably sharp. -
Using Depth Mapping to Realize Bokeh Effect with a Single Camera Android Device EE368 Project Report Authors (SCPD Students): Jie Gong, Ran Liu, Pradeep Vukkadala
Using Depth Mapping to realize Bokeh effect with a single camera Android device EE368 Project Report Authors (SCPD students): Jie Gong, Ran Liu, Pradeep Vukkadala Abstract- In this paper we seek to produce a bokeh Bokeh effect is usually achieved in high end SLR effect with a single image taken from an Android device cameras using portrait lenses that are relatively large in size by post processing. Depth mapping is the core of Bokeh and have a shallow depth of field. It is extremely difficult effect production. A depth map is an estimate of depth to achieve the same effect (physically) in smart phones at each pixel in the photo which can be used to identify which have miniaturized camera lenses and sensors. portions of the image that are far away and belong to However, the latest iPhone 7 has a portrait mode which can the background and therefore apply a digital blur to the produce Bokeh effect thanks to the dual cameras background. We present algorithms to determine the configuration. To compete with iPhone 7, Google recently defocus map from a single input image. We obtain a also announced that the latest Google Pixel Phone can take sparse defocus map by calculating the ratio of gradients photos with Bokeh effect, which would be achieved by from original image and reblured image. Then, full taking 2 photos at different depths to camera and defocus map is obtained by propagating values from combining then via software. There is a gap that neither of edges to entire image by using nearest neighbor method two biggest players can achieve Bokeh effect only using a and matting Laplacian.