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

We present a system that is specifically designed for the information retrieval applications on broadcast news videos. The system is directly useful to an end user for easy access to the news stories of interest. It also act as a platform for convenient deployment and experimentation of various video analysis and indexing techniques on real data, and on a large scale. The system is built upon four layer architecture with certain software design choices that makes the system highly scalable, extensible and modular. The system has been in use for 20 months and has processed around 70Tb of broadcast news data till date. Extensive performance analysis of the system was done by deploying 14 state of the art desktop systems. Results of the same are reported here. This system holds immense potential for the emerging video information retrieval applications.

Abstract

The single-class verification framework is gaining increasing attention for problems involving authentication and retrieval. In this paper, nonlinear features are extracted using the kernel trick. The class of interest is modeled by using all the available samples rather than a single representative sample. Kernel selection is used to enhance the class specific feature set. A tunable objective function is used to select the kernel which enables the adjustment of the false acceptance and false rejection rates. The errors caused due to the presence of highly similar classes are reduced by using a two-stage hierarchical authentication framework. The performance of the resulting verification system is demonstrated on the hand-geometry based authentication problem with encouraging results.

Abstract

In Africa there are a number of languages. Some of these languages have their own indigenous scripts. In this paper we present script analysis for the indigenous scripts of African languages, with particular emphasis to Amharic language. Amharic is the official and working language of Ethiopia, which has its own writing system. This is the first attempt to analyze scripts of African language to ease document analysis and understanding. We believe researchers will continue exploring African indigenous languages and their scripts to be part of the revolving information technology for local development. We also highlighted problems related to the scripts that have bearings on Amharic document analysis and understanding. Especially availability of large number of characters and similarity among characters makes the task of document understanding research much tougher than that of most Latin-based scripts.

Abstract

A kernel-based approach for nonlinear modeling of time series data is proposed in this paper. Autoregressive modeling is achieved in a feature space defined by a kernel function using a linear algorithm. The method extends the advantages of the conventional autoregressive models to characterization of nonlinear signals through the intelligent use of kernel functions.Experiments with synthetic signals demonstrate that this method seems to be a promising alternative to nonlinear modeling schemes.

Abstract

In this paper, we present a novel boosted robot vision control algorithm. The method utilizes on-line boosting to produce a strong vision-based robot control starting from two weak algorithms. The notion of weak and strong algorithms has been presented in the context of robot vision control, in presence of uncertainty in the measurement process. Appropriate probabilistic error functions are defined for the weak algorithm to evaluate their suitability in the task. An on-line boosting algorithm is employed to derive a final strong algorithm starting from two weak algorithms. This strong one has superior performance both in image and Cartesian spaces. Experiments justify this claim.

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

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