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 propose a video mosaicing scheme which exploits the motion information, implicitly available in the video. The information about the camera motion is propagated to the homographies used for mosaicing. While some of the recent approaches make use of the information stemming from non-overlapping pairs of frames, the smoothness of the camera motion has gone largely under-capitalized. We present a technique which exploits this useful cue for refining homographies. Moreover, a generic framework which exploits the camera motion model, to relate homographies in a video, is also proposed. The analysis and results of the proposed algorithms demonstrate significant promise, in terms of accuracy and robustness.

Abstract

Population size plays a major role in determining the performance of any biometric authentication system, particularly when such systems are used for civilian applications. In this paper, we propose to improve the performance of a biometric authentication system by modeling the variation in the population participating in the process. We show that this technique is helpful when the number of users enrolled into the system is very large as compared to the number of users which actually participate in the process. We show results on aperiodically and periodically varying population using Markov models.

Abstract

In this paper, we present a new hybrid visual servoing algorithm for robot arm positioning task. Hybrid methods in visual servoing partially combine the 2D and 3D visual information to improve the performance of the traditional image-based and position-based visual servoing. Our algorithm is superior to the state of the art hybrid methods. The objective function has been designed to include the full 2D and 3D information available either from the CAD model or from the partial reconstruction process by decomposing the homography matrix between two views. Here, each of 2D and 3D error functions is used to control the six degrees of freedom. We call this method 5D visual servoing. The positioning task has been formulated as a minimization problem. Gradient decent as a first order approximation and Gauss-Newton as a second order approximation are considered in this paper. Simulation results show that these two methods provide an efficient solution to the camera retreat and features visibility problems. The camera trajectory in the Cartesian space is also shown to be satisfactory.

Abstract

Most robotic vision algorithms are proposed by envisaging robots operating in structured environments where the world is assumed to be rigid. These algorithms fail to provide optimum behavior when the robot has to be controlled with respect to active non-rigid targets. This paper presents a new framework for visual servoing that accomplishes the robot positioning task even in non-rigid environments. We introduce a space-time representation scheme for modeling the deformations of a non-rigid object and propose a model-free hybrid approach that exploits the two-view geometry induced by the space-time features to perform the servoing task. Our formulation can address a variety of non-rigid motions and can tackle large camera displacements without being affected by the degeneracies in the task space. Experimental results validate our approach and demonstrate the robust and stable behavior.

Abstract

A new probabilistic tracking framework for integrating information available from various visual cues is presented in this paper. The framework allows selection of “good” features for each cue, along with factors of their “goodness” to select the best combination form. Two particle filter based trackers, which use edge and texture features, run independently. The output of the master tracker is computed using democratic integration using the “goodness” weights. The final output is used as apriori for both tracker in the next iteration. Finally, particle filters are used to deal with non-Gaussian errors in feature extraction / prior computation. Results are shown for planar object tracking.

Abstract

Automatic annotation is an elegant alternative to explicit recognition in images. In annotation, the image is matched with keyword models, and the most relevant keywords are assigned to the image. Using existing techniques, the annotation time for large collections is very high, while the annotation performance degrades with increase in number of keywords. Towards the goal of large scale annotation, we present an approach called “Reverse Annotation”. Unlike traditional annotationwhere keywords are identified for a given image, in Reverse Annotation, the relevant images are identified for each keyword. With this seemingly simple shift in perspective, the annotation time is reduced significantly. To be able to rank relevant images, the approach is extended to Probabilistic Reverse Annotation. Our framework is applicable to a wide variety of multimedia documents, and scalable to large collections. Here, we demonstrate the framework over a large collection of 75,000 document images, containing 21 million word segments, annotated by 35000 keywords. Our image retrieval system replicates text-based search engines, in response time.

Abstract

In Africa around 2,500 languages are spoken. Some of these languages have their own indigenous scripts. Accordingly, there is a bulk of printed documents available in libraries, information centers, museums and offices. Digitization of these documents enables to harness already available language technologies to local information needs and developments. This paper presents an Optical Character Recognition (OCR) system for converting digitized documents in local languages. An extensive literature survey reveals that this is the first attempt that reports the challenges towards the recognition of indigenous African scripts and a possible solution for Amharic script. Research in the recognition of African indigenous scripts faces major challenges due to (i) the use of large number characters in the writing and (ii) existence of large set of visually similar characters. In this paper, we employ a novel feature extraction scheme using principal component and linear discriminant analysis, followed by a decision directed acyclic graph based support vector machine classifier. Recognition results are presented on real-life degraded documents such as books, magazines and newspapers to demonstrate the performance of the recognizer

Abstract

A large annotated corpus is critical to the development of robust optical character recognizers (OCRs). However, creation of annotated corpora is a tedious task. It is laborious, especially when the annotation is at the character level. In this paper, we propose an efficient hierarchical approach for annotation of large collection of printed document images. We align document images with independently keyed-in text. The method is model-driven and is intended to annotate large collection of documents, scanned in three different resolutions, at character level. We employ an XML representation for storage of the annotation information. APIs are provided for access at content level for easy use in training and evaluation of OCRs and other document understanding tasks.

Abstract

This paper explores the possibility of using convex optimization to address a class of problems in visual servoing. This work is motivated by the recent success of convex optimization methods in solving geometric inference problems in computer vision. We formulate the visual servoing problem with feature visibility constraints as a convex optimization of a function of the camera position i.e. the translation of the camera. First, the path is planned using potential field method that produces unconstrained but straight line path from the initial to the desired camera position. The problem is then converted to a constrained convex optimization problem by introducing the visibility constraints to the minimization problem. The objective of the minimization process is to find for each camera position the closest alternate position from which all features are visible. This algorithm ensures that the solution is optimal. This formulation allows the introduction of more constraints, like joint limits of the arm, into the visual servoing process. The results have been illustrated in a simulation framework.