Abstract

For content level access, very often database needs the query as a sample image. However, the image may contain private information and hence the user does not wish to reveal the image to the database. Private content based image retrieval (PCBIR) deals with retrieving similar images from an image database without revealing the content of the query image - not even to the database server. We propose algorithms for PCBIR, when the database is indexed using hierarchical index structure or hash based indexing scheme. Experiments are conducted on real datasets with popular features and state of the art data structures. It is observed that specialty and subjectivity of image retrieval (unlike SQL queries to a relational database) enables in computationally efficient yet private solutions.

Abstract

Document image segmentation algorithms primarily aim at separating text and graphics in presence of complex layouts. However, for many non-Latin scripts, segmentation becomes a challenge due to the characteristics of the script. In this paper, we empirically demonstrate that successful algorithms for Latin scripts may not be very effective for Indic and complex scripts. We explain this based on the differences in the spatial distribution of symbols in the scripts. We argue that the visual information used for segmentation needs to be enhanced with other information like script models for accurate results.

Abstract

Super-resolution reconstruction algorithms assume the availability of exact registration and blur parameters. Inaccurate estimation of these parameters adversely affects the quality of the reconstructed image. However, traditional approaches for image registration are either sensitive to image degradations such as variations in blur, illumination and noise, or are limited in the class of image transformations that can be estimated. We propose an accurate registration algorithm that uses the local phase information, which is robust to the above degradations. We derive the theoretical error rate of the estimates in presence of non-ideal band-pass behavior of the filter and show that the error converges to zero over iterations. We also show the invariance of local phase to a class of blur kernels. Experimental results on images taken under varying conditions clearly demonstrates the robustness of our approach.

Abstract

This paper presents a novel real-time algorithm to sequentially solve the triangulation problem. The problem addressed is estimation of 3D point coordinates given its images and the matrices of the respective cameras used in the imaging process. The algorithm has direct application to real time systems like visual SLAM. This article demonstrates the application of the proposed algorithm to the mapping problem in visual SLAM. Experiments have been carried out for the general triangulation problem as well as the application to visual SLAM. Results show that the application of the proposed method to mapping in visual SLAM outperforms the state of the art mapping methods.

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

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

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 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

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

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.