Abstract

Capture of dynamic events is an active research area to-day. Capturingthe212D geometric structure and photomet-ric appearance of dynamic scenesnds applications in 3Dtele-conferencing systems,3DTVetc.The captured ìn depth movies îcontain aligned sequences of depth maps and tex-tures and are often streamed to a distant location for immer-siveviewing. The depth maps are heavy and needed acient compression schemes.In this paper, we present a scheme to compress depth movies of human actors using a parametric proxy model for the under lying action.We use a generic articulated human model as the proxy and the various joint angles a sits parameters for reach time in stant to represent a common prediction of the scene structure. The difference or residue between the captured depth and the depth of the proxy represents the scene to exploits patial coherence. Dif-ferences in residues across time are used to exploit temporal coherence. In tra-frame code frames and difference-coded frames provider and om access and high compression.We show results on several synthetic and real actions to demon-strate the compression ratio and resulting quality using a depth-based rendering of the decoded scene

Abstract

Augmenting virtual objects between real objectsof a video poses a challenging problem, primarily because itnecessitates accurate knowledge of the scene’s depth. In thispaper, we propose an approach that generates an approximatedepth for every pixel in the vicinity of the virtual object, which weshow is enough to decide the ordering of objects in every image.We also draw a similarity between layered segmentation andaugmentation. We argue that augmentation only needs to knowwhich layers are in front of the object and which are behind it.Our algorithm makes effective use of object boundaries detectedin an image using image segmentation. The assumption thatthese boundaries correspond to depth discontinuities provides auseful simplification of the problem, sufficient to produce realisticresults. We use a combination of segmentation and featuredetection for sparse depth assignment. Results on challengingdata sets show the effectiveness of our approach

Abstract

Capturing dynamic scenes in 3D using a suitable depth or structure recovery mechanism has become popular today for image based modelling, 3D telelconferencing, etc. The 2 1 2 D geometric structure and the aligned texture – together called a depth image – are captured by such setups. Time varying sequences of depth images are called depth movies. Depth movies of humans performing interesting actions are being captured using specialized setups today in different research labs. The data so captured is often streamed to another location for immersive viewing using standard depth-image rendering techniques. The depth maps are bulky and need innovative algorithms for efficient representation and compression. In this paper, we present a compression scheme that uses parametric proxy models for the underlying action. We use a generic articulated human model as the proxy and the various joint angles as its parameters for each time instant. The proxy model represents common prediction of the scene structure for that time instant. It can be projected to each view and the difference or residue between the depth due to the proxy and the original depth can be used to represent the scene. The residues compress better and can exploit temporal and spatial relationship between multiple depth movies. We show results on several synthetic actions to demonstrate the usefulness of the scheme

Abstract

Graph cuts has become a powerful and popular opti-mization tool for energies defined over an MRF and havefound applications in image segmentation, stereo vision,image restoration, etc. The maxflow/mincut algorithm tocompute graph-cuts is computationally heavy. The best-reported implementation of graph cuts takes over 100 mil-liseconds even on images of size640×480and cannot beused for real-time applications or when iterated applica-tions are needed. The commodity Graphics Processor Unit(GPU) has emerged as an economical and fast computationco-processor recently. In this paper, we present an imple-mentation of the push-relabel algorithm for graph cuts onthe GPU. We can perform over 60 graph cuts per secondon1024×1024images and over 150 graph cuts per secondon640×480images on an Nvidia 8800 GTX. The time foreach complete graph-cut is about 1 millisecond when only afew weights change from the previous graph, as on dynamicgraphs resulting from videos. The CUDA code with a well-defined interface can be downloaded for anyone’s use

Abstract

Depth movies provide 2-D representations of a time-varying 3D scene. Multistream depth movies arise in many structure-capturing setups and have been used for image based rendering. They are bulky and carry redundant information. We propose a proxy-based compression scheme for multistream depth movies of a scene involving dynamic human actors. The input to our system is depth movies from different viewpoints and the calibration parameters to relate depths to 3D points. We use an articulated human model as a proxy to represent the common structure of the scene. The proxy is parametrized by various bone angles.

Abstract

Post-processors are critical to the performance of language recognizers like OCRs, speech recognizers, etc. Dictionary-based post-processing commonly employ either an algorithmic approach or a statistical approach. Other linguistic features are not exploited for this purpose. The language analysis is also largely limited to the prose form.This paper proposes a framework to use the rich metric and formal structure of classical poetic forms in Indian languages for post-processing a recognizer like an OCR engine. We show that the structure present in the form of the vrtta and prasa ¯ can be efficiently used to disambiguate some cases that may be difficult for an OCR. The approach is efficient, and complementary to other post-processing approaches and can be used in conjunction with them.

Abstract

We describe the design and implementation of a vision based Intermodal Train Monitoring System(ITMS) for extracting various features like length of gaps in an intermodal(IM) train which can later be used for higher level inferences. An intermodal train is a freight train consisting of two basic types of loads - containers and trailers. Our system first captures the video of an IM train, and applies image processing and machine learning techniques developed in this work to identify the various types of loads as containers and trailers. The whole process relies on a sequence of following tasks - robust background subtraction in each frame of the video, estimation of train velocity, creation of mosaic of the whole train from the video and classification of train loads into containers and trailers. Finally, the length of gaps between the loads of the IM train is estimated and is used to analyze the aerodynamic efficiency of the loading pattern of the train, which is a critical aspect of freight trains. This paper focusses on the machine vision aspect of the whole system.

Abstract

Large graphs involving millions of vertices are common in many prac-tical applications and are challenging to process. Practical-time implementationsusing high-end computers are reported but are accessible only to a few. GraphicsProcessing Units (GPUs) of today have high computation power and low price.They have a restrictive programming model and are tricky to use. The G80 lineof Nvidia GPUs can be treated as a SIMD processor array using the CUDA pro-gramming model. We present a few fundamental algorithms – including breadthfirst search, single source shortest path, and all-pairs shortest path – using CUDAon large graphs. We can compute the single source shortest path on a 10 millionvertex graph in1.5seconds using the Nvidia 8800GTX GPU costing $600. Insome cases optimal sequential algorithm is not the fastest on the GPU architec-ture. GPUs have great potential as high-performance co-processors.

Abstract

Cluster-based tiled display walls can provide cost-effective and scalable displays with high resolution and a large displayarea. The software to drive them needs to scale too if arbitrarily large displays are to be built. Chromium is a popular software API usedto construct such displays. Chromium transparently renders any OpenGL application to a tiled display by partitioning and sendingindividual OpenGL primitives to each client per frame. Visualization applications often deal with massive geometric data with millions ofprimitives. Transmitting them every frame results in huge network requirements that adversely affect the scalability of the system. Inthis paper, we present Garuda, a client-server-based display wall framework that uses off-the-shelf hardware and a standard network.Garuda is scalable to large tile configurations and massive environments. It can transparently render any application built using theOpen Scene Graph (OSG) API to a tiled display without any modification by the user. The Garuda server uses an object-based scenestructure represented using a scene graph. The server determines the objects visible to each display tile using a novel adaptivealgorithm that culls the scene graph to a hierarchy of frustums. Required parts of the scene graph are transmitted to the clients, whichcache them to exploit the interframe redundancy. A multicast-based protocol is used to transmit the geometry to exploit the spatialredundancy present in tiled display systems. A geometry push philosophy from the server helps keep the clients in sync with oneanother. Neither the server nor a client needs to render the entire scene, making the system suitable for interactive rendering ofmassive models. Transparent rendering is achieved by intercepting the cull, draw, and swap functions of OSG and replacing them withour own. We demonstrate the performance and scalability of the Garuda system for different configurations of display wall. We alsoshow that the server and network loads grow sublinearly with the increase in the number of tiles, which makes our scheme suitable toconstruct very large displays

Abstract

Digitally recording dynamic events, such as sporting events, for experiencing in a spatio-temporally distant and arbitrary setting requires 4D capture: three dimensions for their geometry and appearance over the fourth dimension of time. Today's computer vision techniques make 4D capture possible. The virtualized reality system serves as an example on the general problem of digitizing dynamic events. In this article, we present the virtualized reality system's details from a historical perspective