Abstract

Video completion algorithms have concentrated on obtaining visually consistent solutions to fill-in the missing portions, without any emphasis on the physical correctness of the video. Resulting solutions thus use texture or image structure based cues and are limited in the situations they can handle. In this paper we take a model based signal processing approach to video completion [1]. Completion of the video is then defined as satisfying the given model by detecting and removing the error (selected parts of the video to be replaced). Given a probabilistic model, video completion then becomes an unsupervised learning algorithm with the input video giving a “noisy” version. Dense completion is the automatic inferencing of the “noise-less” or “true” video from the input. This approach finds a solution that satisfies visual coherence and is applicable to a wide variety of scenarios. We demonstrate the efficacy of our approach and its wide applicability using two scenarios.

Abstract

A video from a moving camera produces different number of observations of different scene areas. We can construct an attention map of the scene by bringing the frames to a common reference and counting the number of frames that observed each scene point. Different representations can be constructed from this. The base of the attention map gives the scene mosaic. Super-resolved images of parts of the scene can be obtained using a subset of observations or video frames. We can combine mosaicing with superresolution by using all observations, but the magnification factor will vary across the scene based on the attention received. The height of the attention map indicates the amount of super-resolution for that scene point. We modify the traditional super-resolution framework to generate a varying resolution image for panning cameras in this paper. The varying resolution image uses all useful data available in a video. We introduce the concept of attention-based super-resolution and give the modified framework for it. We also show its applicability on a few indoor and outdoor videos.

Abstract

Intermodal (IM) trains are typically the fastest freight trains operated in North America. The aerodynamic characteristics of many of these trains are often relatively poor resulting in high fuel consumption. However, considerable variation in fuel efficiency is possible depending on how the loads are placed on railcars in the train. Consequently, substantial potential fuel savings are possible if more attention is paid on the loading configurations of trains. A wayside machine vision (MV) system was developed to automatically scan passing IM trains and assess their aerodynamic efficiency. MV algorithms are used to analyse these images, detect and measure gaps between loads. In order to make use of the data, a scoring system was devel- oped based on two attributes – the aerodynamic coefficient and slot efficiency. The aerodynamic coefficient is calculated using the Aerodynamic Subroutine of the Train Energy Model. Slot effi- ciency represents the difference between the actual and ideal loading configuration given the particular set of railcars in the train. This system can provide IM terminal managers feedback on loading performance for trains and be integrated into the software support systems used for loading assignment.

Abstract

The advent of advanced graphics technologies and im-proved hardware has enabled the generation of highly com-plex models with huge number of triangles. Point-basedrepresentations and rendering have emerged as viable rep-resentations for high-quality models in this scenario. Thesemethods have been demonstrated only on high resolution,compact objects so far. They have to be adapted to environ-ments that extend over large areas to be considered seriousrepresentations. In this paper, we present an adaptation ofthe point-based representation to large, hierarchical envi-ronments. We show how point-based data can be generatedby sampling polygon-based representations. We also showthe combination of an object hierarchy and multiresolutionpoint representations and develop rendering algorithms forthe same. The multiresolution representation is constructedduring the generation process. We then show a hybrid rep-resentation in which the more complex portions of the envi-ronment are represented using points and others using theoriginal polygon-based representation. This produces bet-ter rendering performance by keeping large, flat regions astriangles. We demonstrate the method on the model of theFatehpur Sikri which has 14000 objects with over 500,000triangles

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

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

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

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

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

Terrains and other geometric models have been traditionally stored locally. Their remote access presents the characteristics that are a combination of file serving and realtime streaming like audio-visual media. This paper presents a terrain streaming system based upon a client server architecture to handle heterogeneous clients over low-bandwidth networks. We present an efficient representation for handling terrains streaming. We design a client-server system that utilizes this representation to stream virtual environments containing terrains and overlayed geometry efficiently. We handle dynamic entities in environment and the synchronization of the same between multiple clients. We also present a method of sharing and storing terrain annotations for collaboration between multiple users. We conclude by presenting preliminary performance data for the streaming system