Abstract

We introduce methods for augmenting aerial visualizations of Earth (from tools such as Google Earth or Microsoft Virtual Earth) with dynamic information obtained from videos. Our goal is to make Augmented Earth Maps that visualize the live broadcast of dynamic sceneries within a city. We propose different approaches to analyze videos of pedestrians and cars, under differing conditions and then augment Aerial Earth Maps (AEMs) with live and dynamic information. We also analyze natural phenomenon (clouds) and project information from these to the AEMs to add the visual reality.

Check out the media coverage of our new paper to appear in ISMAR 2009, in October.

Also see

• “Latest videos makes Google Earth cities bustle” New Scientist (Sep 30, 2009 Issue)
• “Video: Google Earth animated with real time human and vehicular traffic” Endgadget (Sep 30, 2009)

(Slides, NO Video)

Abstract

I will present a variety of temporal models of video that we have been studying (and developing on) for analysis and synthesis of video. Forsynthesis of videos, we have been developing representations that support example-based re-synthesis and spatio-temporal re-targeting. These approaches build on graph-based methods and we present techniques for similarity metrics for video, segmentation in video, and merging of different video streams. I will showcase a series of examples of these approaches applied to generate new videos.

For analysis of videos, we have developed a series of representations to observe and model activities in videos. Building on low-level measures of movement and motion in videos, we have incorporated higher-level temporal generative models to represent and recognize observed activities. I will discuss the strengths of a variety of State-based, Markovian, Grammar-based and Network-based representations that we have employed for recognizing activities from video. I will also discuss approaches for unsupervised discovery and recognition of activities.

Time permitting, I will describe some new efforts that move towards understanding mobile imaging and video, and video authoring and video on the web, Within these I will discuss issues of collaborative imaging, collective authoring, ad-hoc sensor networks, and peer production with images and videos. Using these concepts, to focus the conversation, I will discuss how all of these issues are impacting the field Journalism and Reporting and how we have started on a new interdisciplinary research and education effort, we call Computational Journalism.

Irfan Essa
Georgia Institute of Technology
School of Interactive Computing, GVU and RIM Centers
April 21, 2009.

(see the video of this presentation)

Abstract

Our consumption of images (photography/video) continues to grow with the pervasiveness of computing (networking, mobile and media) technologies into our daily lives. Everyone now has a mobile camera, and digital image capture, processing, and sharing has become ubiquitous in our society. This has led to a significant impact on we want to (a) create novel scenes, (b) share our experiences with images, and (c) interact with  large amounts of images and videos from many sources. In this talk, I will start  with a brief overview of series of ongoing efforts in the analysis of images and videos for rendering novel scenes, interacting with images/videos and collaboratively authoring new content. I will describe some work on video-based rendering and synthesizing novel videos (and scenes) and highlight the technical contributions being made in areas of Computational Photography and Video.

Using these sets of efforts as a foundation I will showcase where things are headed in terms of user generated content, media sharing, annotation, and reuse with large scale networks. In essence, everybody is a content, producer, distributor, and consumer. I will describe some new efforts that move towards understanding mobile imaging and video, and also discuss issues of collaborative imaging, collective authoring, ad-hoc sensor networks, and peer production with images and videos.  Using these concepts I will discuss how all of these issues are impacting the field Journalism and Reporting and how we have started on a new interdisciplinary research and education effort, we call Computational Journalism.  The concept of Computational Journalism includes more than just imaging, and relates to media and information in general and is aimed at the study of how we remain informed in this connected world. I will outline this new field and relate it back to imaging, with examples from some of our recent work in this new area.

Abstract

Matthew Flagg, Atsushi Nakazawa, Qiushuang Zhang, Sing Bing Kang, Young Kee Ryu, Irfan Essa, James M. Rehg (2009), Human Video Textures In Proceedings of the ACM Symposium on Interactive 3D Graphics and Games 2009 (I3D ’09), Boston, MA, February 27-March 1 (Fri-Sun), 2009 [PDF (see Copyright) | Video in DiVx | Website ]

Abstract

This paper describes a data-driven approach for generating photorealistic animations of human motion. Each animation sequence follows a user-choreographed path and plays continuously by seamlessly transitioning between different segments of the captured data. To produce these animations, we capitalize on the complementary characteristics of motion capture data and video. We customize our capture system to record motion capture data that are synchronized with our video source. Candidate transition points in video clips are identified using a new similarity metric based on 3-D marker trajectories and their 2-D projections into video. Once the transitions have been identified, a video-based motion graph is constructed. We further exploit hybrid motion and video data to ensure that the transitions are seamless when generating animations. Motion capture marker projections serve as control points for segmentation of layers and nonrigid transformation of regions. This allows warping and blending to generate seamless in-between frames for animation. We show a series of choreographed animations of walks and martial arts scenes as validation of our approach.

Human Video Textures (Output Rendered as a Collage!)

 
Abstract

Digital image capture, processing, and sharing has become pervasive in our society. This has had significant impact on how we create novel scenes, how we share our experiences, and how we interact with images and videos. In this talk, I will present an overview of series of ongoing efforts in the analysis of images and videos for rendering novel scenes. First I will discuss (in brief) our work on Video Textures, where repeating information is extracted to generate extended sequences of videos. I will also describe some our extensions to this approach that allows for controlled generation of animations of video sprites. We have developed various learning and optimization techniques that allow for video-based animations of photo-realistic characters. Using these sets of approaches as a foundation, then I will show how new images and videos can be generated. I will show examples of Photorealistic and Non-photorealistic Renderings of Scenes (Videos and Images) and how these methods support the media reuse culture, so common these days with user generated content.   I will then describe some of our new efforts that move towards understanding mobile imaging and video, and also discuss issues of collaborative imaging and authoring and ad-hoc sensor networks, and peer production with images and videos, leading to a new concepts of how computation has impacted journalism. Time permitting, I will also share some of our efforts on video annotation and how we have taken some of these new concepts of video analysis to classrooms.

My students decided to play a very nice joke on me. This morning I walked in to find my office open (and it was not!)

check out the left image, with door closed and the right image with the door open.

And, then the inside of the office was kinda different too.

Abstract

This paper presents an algorithm for the automatic segmentation of monocular videos into foreground and background layers. Correct segmentations are produced even in the presence of large background motion with nearly stationary foreground. There are three key contributions. The first is the introduction of a novel motion representation, “motons”, inspired by research in object recognition. Second, we propose learning the segmentation likelihood from the spatial context of motion. The learning is efficiently performed by Random Forests. The third contribution is a general taxonomy of tree-based classifiers, which facilitates theoretical and experimental comparisons of several known classification algorithms, as well as spawning new ones. Diverse visual cues such as motion, motion context, colour, contrast and spatial priors are fused together by means of a Conditional Random Field (CRF) model. Segmentation is then achieved by binary min-cut. Our algorithm requires no initialization. Experiments on many video-chat type sequences demonstrate the effectiveness of our algorithm in a variety of scenes. The segmentation results are comparable to those obtained by stereo systems.

Abstract

Navigation through large multimedia collections that include videos and images still remains cumbersome. In this paper, we introduce a novel method to visualize and navigate through the collection by creating a mosaic image that visually represents the compilation. This image is generated by a labeling-based layout algorithm using various sizes of sample tile images from the collection. Each tile represents both the photographs and video files representing scenes selected by matching algorithms. This generated mosaic image provides a new way for thematic video and visually summarizes the videos. Users can generate these mosaics with some predefined themes and layouts, or base it on the results of their queries. Our approach supports automatic generation of these layouts by using meta-information such as color, time-line and existence of faces or manually generated annotated information from existing systems (e.g., the Family Video Archive).

Interactive Video Mosaic

]]> http://prof.irfanessa.com/2006/10/22/paper-in-acm-multimedia-2006-interactive-mosaic-generation-for-video-navigation/feed/ 0 http://prof.irfanessa.com/2006/09/30/experiences-with-optimizing-two-stream-based-applications-for-cluster-execution/ http://prof.irfanessa.com/2006/09/30/experiences-with-optimizing-two-stream-based-applications-for-cluster-execution/#comments Sun, 01 Oct 2006 01:23:01 +0000 Irfan Essa http://essa.org/irfan/wp/?p=21 Experiences with optimizing two stream-based applications for cluster execution Angelov, Y., Ramachandran, U., Mackenzie, K., Rehg, J. M., and Essa, I. 2005. “Experiences with optimizing two stream-based applications for cluster execution”. J. Parallel Distrib. Comput. 65, 6 (Jun. 2005), 678-691. [DOI]

Abstract

We explore optimization strategies and resulting performance of two stream-based video applications, video texture and color tracker, on a cluster of SMPs. The two applications are representative of a class of emerging applications, which we call “stream-based applications”, that are sensitive to both latency of individual results and overall throughput. Such applications require non-trivial parallelization techniques in order to improve both latency and throughput, given that the stream data emanates from a limited set of sources (exactly one in the two applications studied) and that the distribution of the data cannot be done a priori.We suggest techniques that address in a coordinated fashion the problems of data distribution and work partitioning. We believe the two problems are related and need to be addressed together. We have parallelized two applications using the Stampede cluster programming system that provides abstractions for implementing time-and throughput-sensitive applications elegantly and efficiently. For the Video Textures application we show that we can achieve a speedup of 24.26 on a 112 processor cluster. For the Color Tracker application, where latency is more crucial, we identify the extent of data parallelism that ensures that the slowest member of the pipeline is no longer the bottleneck for achieving a decent frame rate.

ABSTRACT

We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that progressively refines the entire texture. Additionally, our approach is ideally suited to allow for controllable synthesis of textures. Specifically, we demonstrate controllability by animating image textures using flow fields. We allow for general two-dimensional flow fields that may dynamically change over time. Applications of this technique include dynamic texturing of fluid animations and texture-based flow visualization.

Abstract

We present an algorithm that approximates the output of an arbitrary video processing algorithm based on a pair of input and output exemplars. Our algorithm relies on learning the mapping between the input and output exemplars to model the processing that has taken place. We approximate the processing by observing that pixel neighborhoods similar in appearance and motion to those in the exemplar input should result in neighborhoods similar to the exemplar output. Since there are not many pixel neighborhoods in the exemplars, we use techniques from texture synthesis to generalize the output of neighborhoods not observed in the exemplars. The same algorithm is used to learn such processing as motion blur color correction, and painting.

ABSTRACT

We introduce a new optimization algorithm for video sprites to animate realistic-looking characters. Video sprites are animations created by rearranging recorded video frames of a moving object. Our new technique to find good frame arrangements is based on repeated partial replacements of the sequence. It allows the user to specify animations using a flexible cost function. We also show a fast technique to compute video sprite transitions and a simple algorithm to correct for perspective effects of the input footage. We use our techniques to create character animations of animals, which are difficult both to train in the real world and to animate as 3D models.

Temporal Reasoning from Video to Temporal Synthesis of Video

Abstract

In this talk, I will present some ongoing work on extracting spatio-temporal cues from video for both synthesis of novel video sequences, and recognition of complex activities. First I will discuss (in brief) our work on Video Textures, where repeating information is extracted to generate extended sequences of videos. I will then describe some our extensions to this approach that allows for controlled generation of animations of video sprites. We have developed various learning and optimization techniques that allow for video-based animations of photo-realistic characters. Then I will describe our new approach for image and video synthesis that builds on optimal patch-based copying of samples. I will show how our method allows for iterative refinement and extend to synthesis of both images and video from very limited samples. In the next part of my talk, I will describe how a similar analysis of video can be used to recognize what a person is doing in a scene. Such an analysis of video, aimed at recognition, requires more contextual information about the environment. I will show how we leverage off contextual information shared between actions and objects to recognize what is happening in complex environments. I will also show that by adding some form of grammar (we use Stochastic Context Free Grammar) we can recognize very complex, multi-tasked activities. Finally, I will describe (very briefly) the Aware Home project at Georgia Tech, which is one primary area of ongoing and future research for me and my group. Further information on my work with videos is available from my webpage at http://www.cc.gatech.edu/~irfan

ABSTRACT

Stop motion animation is a well-established technique where still pictures of static scenes are taken and then played at film speeds to show motion. A major limitation of this method appears when fast motions are desired; most motion appears to have sharp edges and there is no visible motion blur. Appearance of motion blur is a strong perceptual cue, which is automatically present in live-action films, and synthetically generated in animated sequences. In this paper, we present an approach for automatically simulating motion blur. Ours is wholly a post-process, and uses image sequences, both stop motion or raw video, as input. First we track the frame-to-frame motion of the objects within the image plane. We then integrate the scene’s appearance as it changed over a period of time. This period of time corresponds to shutter speed in live-action filming, and gives us interactive control over the extent of the induced blur. We demonstrate a simple implementation of our approach as it applies to footage of different motions and to scenes of varying complexity. Our photorealistic renderings of these input sequences approximate the effect of capturing moving objects on film that is exposed for finite periods of time.

Abstract

This paper introduces a new type of medium, called a video texture, which has qualities somewhere between those of a photograph and a video. A video texture provides a continuous infinitely varying stream of images. While the individual frames of a video texture may be repeated from time to time, the video sequence as a whole is never repeated exactly. Video textures can be used in place of digital photos to infuse a static image with dynamic qualities and explicit actions. We present techniques for analyzing a video clip to extract its structure, and for synthesizing a new, similar looking video of arbitrary length. We combine video textures with view morphing techniques to obtain 3D video textures. We also introduce video-based animation, in which the synthesis of video textures can be guided by a user through high-level interactive controls. Applications of video textures and their extensions include the display of dynamic scenes on web pages, the creation of dynamic backdrops for special effects and games, and the interactive control of video-based animation.

• Schödl, A., Szeliski, R., Salesin, D. H., and Essa, I. 2000. Video textures. In Proceedings of the 27th Annual Conference on Computer Graphics and interactive Techniques International Conference on Computer Graphics and Interactive Techniques. ACM Press/Addison-Wesley Publishing Co., New York, NY, 489-498. [DOI][PDF]
• Project WebPage
• Presentation Slides
• SIGGRAPH 2000 Video