Abstract

Most of the state-of-the-art segmentation algorithms are de-signed to handle complex document layouts and backgrounds, while assuming a simple script structure such as in Roman script. They perform poorly when used with Indian languages, where the components are not strictly collinear. In this paper, we propose a document segmentation algorithm that can handle the complexity of Indian scripts in large document image collections. Segmentation is posed as a graph cut problem that incorporates the a prior information from script structure in the objective function of the cut. We show that this information can be learned automatically and be adapted within a collection of documents (a book)and across collections to achieve accurate segmentation. We show the results on Indian language documents in Telugu script. The approach is also applicable to other languages with complex scripts such as Bangla,Kannada, Malayalam, and Urdu

Abstract

Annotated datasets of handwriting are a prerequisite to attempt a variety of problems such as building recognizers, developing writer identification algorithms, etc. However, the annotation of large datasets is a tedious and expensive process, especially at the character or stroke level. In this paper we propose a novel, automated method for annotation at the character level, given a parallel corpus of online handwritten data and the corresponding text. The method employs a model-based handwriting synthesis unit to map the two corpora to the same space and the annotation is propagated to the word level and then to the individual characters using elastic matching. The initial results of annotation are used to improve the handwriting synthesis model for the user under consideration, which in turn refines the annotation. The method can take care of errors in the handwriting such as spurious and missing strokes or characters. The output is stored in the UPXInkML format

Abstract

Automatic identification of the author of a document has a variety of applications for both online and offline handwritten data such as facilitating the use of write dependent recognizers, verification of claimed identity for security, enabling personalized HCI and countering repudiations for legal purposes. Most of the existing writer identification techniques require the data to be from a specific text or a recognizer be available, which is not always feasible. Text-independent approaches often require large amount of data to be confident of good results. In this work, we propose a text-independent writer identification framework that uses a specified set of primitives of online handwritten data to ascertain the identity of the writer. The framework allows us to learn the properties of the script and the writers simultaneously and hence can be used with multiple languages or scripts. We demonstrate the applicability of our framework by choosing shapes of curves as primitives and show results on five different scripts and on different data sets.

Abstract

We present in this paper a methodology for computing the maximum velocity profile over a trajectory planned for a mobile robot. Environment and robot dynamics as well as the constraints of the robot sensors determine the profile. The planned profile is indicative of maximum speeds that can be possessed by the robot along its path without colliding with any of the mobile objects that could intercept its future trajectory. The mobile objects could be arbitrary in number and the only information available regarding them is their maximum possible velocity. The velocity profile also enables one to deform planned trajectories for better trajectory time. The methodology has been adopted for holonomic and nonholonomic motion planners. An extension of the approach to an online real-time scheme that modifies and adapts the path as well as velocities to changes in the environment such that both safety and execution time are not compromised is also presented for the holonomic case. Simulation and experimental results demonstrate the efficacy of this methodology.

Abstract

RS paths for a robot with both front and rear wheel steer. We call such robots as FR steer. The occurrence of such paths is due to the additional maneuver possible in such a robot which we call parallel steer, in addition to the ones already present in a vehicle with only front wheel steering. Hence we extend the optimal path set, containing only a single element to a set, containing n elements, thereby extending its configuration set along the optimal path from the initial to the final configuration. This extension of the set to set is made possible by introducing a special set, which we call the Parallel Steer (PS) Set. Such an extension of the configuration set would increase the size of the final configuration set achievable by a path that is optimal in free space. In the following discussion, we shall term all paths whose length is equal to an RS path as optimal.

Abstract

Different methodologies exist to direct the motion of sensors to detect targets moving across an environment in various scenarios. However, some of these do not model that navigation scenario in an environment in which obstacles are present. We extend our earlier algorithm for optimal target detection, as well as other algorithms reported in literature, to this case, and make a detailed comparison of their performance. This makes clear that the current algorithm is competitive in applications where target statistics are known in advance; otherwise, a heuristic technique by Sukhatme and Jung performs best.

Abstract

We present a strategy for resolving multiple hypotheses of a robot's state during global localization. The strategy operates in two stages. In the first stage a unique direction of the motion is sought that resolves or eliminates maximum number of hypotheses. In the second stage, among the frontier areas arising from the multiple hypotheses states, that frontier is chosen which resolves the maximum number of the hypotheses. The two stages are alternated till a unique hypothesis emerges. Simulation and experimental results verify the efficacy of this method. A comparison with other methods based on entropy minimization, and minimum distance travel portrays the advantage of the current methodology. A convergence proof for the algorithm is also presented.

Abstract

Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data

Abstract

Digital Libraries have received wide attention in the recent years allowing access to digital information from anywhere across the world. They have become widely accepted and even preferred information sources in areas of education, science and others. When the full potential of digital libraries is realized, any citizen will for the first time be able to access all human knowledge immediately from any location. For a successful and useful Digital Library, the assurance of quality of the product from digitization process is quite essential. In this paper we discuss the major quality concerns of data from the digitization process, and how we assure for better quality before they are web enabled for the end-user to use. Also we discuss a Quality Management Framework that we applied for the improvement of both the Quality of the digital output and the efficiency of the processes.

Abstract

Many of the human activities such as Jumping, Squatting have a correlated spatiotemporal structure. They are composed of homogeneous units. These units, which we refer to as actions, are often common to more than one activity. Therefore, it is essential to have a representation which can capture these activities effectively. To develop this, we model the frames of activities as a mixture model of actions and employ a probabilistic approach to learn their low-dimensional representation. We present recognition results on seven activities performed by various individuals. The results demonstrate the versatility and the ability of the model to capture the ensemble of human activities.