Abstract

Image-based navigation paradigms have recently emerged as an interesting alternative to conventional modelbased methods in mobile robotics. In this paper, we augment the existing image-based navigation approaches by presenting a novel image-based exploration algorithm. The algorithm facilitates a mobile robot equipped only with a monocular pan-tilt camera to autonomously explore a typical indoor environment. The algorithm infers frontier information directly from the images and displaces the robot towards regions that are informative for navigation. The frontiers are detected using a geometric context-based segmentation scheme that exploits the natural scene structure in indoor environments. In the due process, a topological graph of the workspace is built in terms of images which can be subsequently utilised for the tasks of localisation, path planning and navigation. Experimental results on a mobile robot in an unmodified laboratory and corridor environments demonstrate the validity of the approach.

Abstract

In this paper we present a novel approach to robust visual servoing. This method removes the feature tracking step from a typical visual servoing algorithm. We do not need correspondences of the features for deriving the control signal. This is achieved by modeling the image features as a Mixture of Gaussians in the current as well as desired images. Using Lyapunov theory, a control signal is derived to minimize a distance function between the two Gaussian mixtures. The distance function is given in a closed form, and its gradient can be efficiently computed and used to control the system. For simplicity, we first consider the 2D motion case. Then, the general case is presented by introducing the depth distribution of the features to control the six degrees of freedom. Experiments are conducted within a simulation framework to validate our proposed method.

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

Restoration of documents has critical applications in document understanding as well as in digital libraries (for example as in book readers). This paper presents a method for restoration of document images, using a Maximum a Posteriori formulation. The advantage of our method is that the prior need not be learned from the training images. The extraction of a single high-quality enhanced text image from a set of degraded images can benefit from a strong prior knowledge, typical of text images. The restoration process should allow for discontinuities but at the same time discourage oscillations. These properties were represented in a total variation based prior model. Results indicate that our method is appropriate for document image restoration, where resolution enhancement is an added gain.

Abstract

This paper presents an edge-directed super-resolution algorithm for document images without using any training set. This technique creates an image with smooth regions in both the foreground and the background, while allowing sharp discontinuities across and smoothness along the edges. Our method preserves sharp corners in text images by using the local edge direction, which is computed first by evaluating the gradient field and then taking its tangent. Super-resolution of document images is characterized by bimodality, smoothness along the edges as well as subsampling consistency. These characteristics are enforced in a Markov Random Field (MRF) framework by defining an appropriate energy function. In our method, subsampling of super-resolution image will return the original lowresolution one, proving the correctness of the method. The super-resolution image, is generated by iteratively reducing this energy function. Experimental results on a variety of input images, demonstrate the effectiveness of our method for document image super-resolution.

Abstract

As large quantity of document images is getting archived by the digital libraries, there is a need for an efficient search strategies to make them available as per users information need. In this paper, we propose an effective word image matching scheme that achieves high performance in the presence of script variability, printing variation, degradation and word-form variants. A novel partial matching algorithm is designed for morphological matching of word form variants in a language. We formulate feature extraction scheme that extracts local features by scanning vertical strips of the word image and combining them automatically based on their discriminatory potential. We present detailed performance analysis of the proposed approach on English, Amharic and Hindi documents.

Abstract

The problem of character recognition in a book should be formulated significantly different from that of a single page or word. An ideal approach to design such a recognizer is to adapt the classifier to the font and style of the collection. In this paper, we propose an adaptation framework to recognize characters in a book with a learning framework. In the proposed system, the post processor verifies the output of the recognition module, which is further used for learning and thus to improve the performance over iteration. Experiments are conducted on about 500,000 annotated symbols from five books in Malayalam (an Indian language). We achieve an average improvement of 14% in classification accuracy.

Abstract

Image based methods are a new approach for solving problems in mobile robotics. Instead of building a metric (3D) model of the environment, these methods work directly in the sensor (image) space. The environment is represented as a topological graph in which each node contains an image taken at some pose in the workspace, and edges connect poses between which a simple path exists. This type of representation is highly scalable and is also well suited to handle the data association problems that effect metric model based methods. In this paper, we present an efficient, adaptive method for qualitative localization using content based image retrieval techniques. In addition, we demonstrate an algorithm which can convert this topological graph into a metric model of the environment by incorporating information about loop closures

Abstract

State of art document segmentation algorithms employ adhoc solutions which use some document properties and iteratively segment the document image. These solutions need to be adapted frequently and sometimes fail to perform well for complex scripts. This calls for a generalized solution that achieves a one shot segmentation that is globally optimal. This paper describes one such solution based on the optimization problem of spectral partitioning which makes the decision of proper segmentation based on the spectral properties of the pairwise similarity matrix. The solution described in the paper is shown to be general, global and closed form. The claims have been demonstrated on 142 page images from a Telugu book, in a script set in both poetry and prose layouts. This particular class of scripts has been proved to be challenging for the existing state of the art algorithms, where the proposed solution achieves significant results.

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.