Abstract

Accurate segmentation of the cup region from retinal images is needed to derive relevant measurements for glaucoma assessment. A novel, depth discontinuity (in the retinal surface)-based approach to estimate the cup boundary is proposed in this paper. The proposed approach shifts focus from the cup region used by existing approaches to cup boundary. The given set of images, acquired sequentially, are related via a relative motion model and the depth discontinuity at the cup boundary is determined from cues such as motion boundary and partial occlusion. The information encoded by these cues is used to approximate the cup boundary with a set of best-fitting circles. The final boundary is found by considering points on these circles at different sectors using a confidence measure. Four different kinds of data sets ranging from synthetic to real image pairs, covering different multiview scenarios, have been used to evaluate the proposed method. The proposed method was found to yield an error reduction of 16% for cup-to-disk vertical diameter ratio (CDR) and 13% for cup-to-disk area ratio (CAR) estimation, over an existing monocular image-based cup segmentation method. The error reduction increased to 33% in CDR and 18% in CAR with the addition of a third view (image) which indicates the potential of the proposed approach.

Abstract

Graphics processing units (GPUs) provide large computational power at a very low price, which position GPUs well as an ubiquitous accelerator. However, GPUs are space constrained, and hence applications developed for GPUs are space sensitive. Space-constrained computational devices such as GPUs can greatly benefit from representations that reduce space consumption drastically. One such representation is the succinct representation of trees. Succinct representation of trees generally allows for operations such as parent queries, least common ancestor queries, and so on. Mapping such a robust representation to the GPU for targeted applications can lead to substantial improvement in problem sizes that are processed at a given point of time. Space-saving methods such as succinct data structures remain largely unexplored on the GPU. In this work, a succinct representation of ordered trees on the GPU is explored, with application to discrete range searching (DRS). Based on the succinct representations found applicable, a space--saving solution for DRS is presented here. In our method, DRS is mapped to a least common ancestor query on a Cartesian tree. For space-efficient DRS queries, we store the succinct representation of the Cartesian tree of an array. Our method uses a maximum of 7.5 bits of additional space per element. Furthermore, the speed-up achieved by our method is in the range of 20--25 for preprocessing and 25--35 for batch querying over a sequential implementation. Compared to an 8-threaded implementation, our preprocessing and querying methods obtain a speed-up of 6--8. We also study the applications of the DRS on the GPU. Efficient primitives expand the range of applications performed on the GPU. DRS is one such primitive with direct applications to string processing, document and text retrieval systems, and least common ancestor queries. We suggest that graph algorithms that use the least common ancestor, can be enabled on the GPU based on DRS primitive. We also show some applications of DRS in tree queries and string querying.

Abstract

This paper initiates formal analysis of a simple, distributed algorithm for community detection on networks. We analyze an algorithm that we calltextsc {Max-LPA}, both in terms of its convergence time and in terms of the" quality" of the communities detected.textsc {Max-LPA} is an instance of a class of community detection algorithms calledtextit {label propagation} algorithms. As far as we know, most analysis of label propagation algorithms thus far has been empirical in nature and in this paper we seek a theoretical understanding of label propagation algorithms. In our main result, we define a clustered version ofer random graphs with clusters where the probability , of an edge connecting nodes within a cluster is higher than , the probability of an edge connecting nodes in distinct clusters. We show that even with fairly general restrictions on and ( for any , , where is the number of nodes),textsc {Max-LPA} detects the clusters in just two rounds. Based on this and on empirical results, we conjecture thattextsc {Max-LPA} can correctly and quickly identify communities on clustereder graphs even when the clusters are much sparser, ie, with for some .

Abstract

In this work, we study the problem of reporting and counting maximal points in a query rectangle for a set of n integer points that lie on an n×n grid. A point is said to be maximal inside a query rectangle if it is not dominated by any other point inside the query rectangle. Our model of computation is unit-cost RAM model with word size of O(logn) bits. For the reporting version of the problem, we present a data structure of size words and support querying in time where k is the size of the output. For the counting version, we present a data structure of size words which supports querying in . Both the data structures are static in nature. The reporting version of the problem has been studied in [1] and [5]. To the best of our knowledge, this is the first sub-logarithmic result for the reporting version and the first work for the counting version of the problem.

Abstract

In the present study, three large ecological plots of 3 ha each were surveyed in 3 different forest types of North Andaman Islands. Each 3-ha plot (30 sub-plots of 0.1 ha each) was classified into different classes based on site quality as either excellent, good, moderate or poor using an index developed by utilizing vegetation parameters such as species richness, diversity, density, among others. Analysis revealed most of the area to be under the good category in three forest communities, indicating that forests of North Andaman are potential sites of species richness and diversity. The “general limit of species assemblage” with respect to higher angiosperm taxa in North Andaman was observed based on two independent approaches of sampling: stratified random plots and the large area ecological plots. The general limit of species assemblage was in the range of 14-33 species. The present analysis provides a base …

Abstract

The current research focused on the issues related to human-bird conflicts and subsequent alternatives adopted by the migratory birds at Uppalapadu Lake (India). The study collates the information from field observations and literature surveys and draws conclusions based on the observation data. The study highlights the ways the local community, Forest Department, activist groups and other organizations participate in protecting the birds from harm. It also focuses on some of the management initiatives taken by the Forest Department that are helping the birds during their breeding periods. Overall, the research identifies the ecological importance of the lake with respect to birds and stresses the need for its protection and further expansion.

Abstract

In this paper, we explore the use of Bessel features derived from speech utterances, to develop Gaussian mixture speaker models for text independent Speaker Identification. The proposed approach to speaker identification is based on existing methods that employ Gaussian mixtures for the modeling of speakers. However, we have developed the speaker models from the Bessel features derived from the speech utterances, as an alternative to Mel-frequency cepstral coefficients for developing the speaker models. The proposed approach is tested on two databases of ten and twenty speakers respectively and their performance is evaluated. Finally, we have made some suggestions for future work involving the use of Bessel features for text independent speaker identification

Abstract

Perfectly reliable message transmission (PRMT) is one of the fundamental problems in distributed computing. It allows a sender to reliably transmit a message to a receiver in an unreliable network, even in the presence of a computationally unbounded adversary. In this article, we study the inherent trade-off between the three important parameters of the PRMT protocols, namely, the network connectivity (n), the round complexity (r), and the communication complexity by considering the following generic question (which can be considered as the holy grail problem) in the context of the PRMT protocols. Given an n-connected network, a message of size ℓ (to be reliably communicated) and a limit c for the total communication allowed between the sender and the receiver, what is the minimum number of communication rounds required by a PRMT protocol to send the message, such that the communication complexity of the protocol is O(c)? We answer this interesting question by deriving a nontrivial lower bound on the round complexity. Moreover, we show that the lower bound is tight in the amortized sense, by designing a PRMT protocol whose round complexity matches the lower bound. The lower bound is the first of its kind, that simultaneously captures the inherent tradeoff between the three important parameters of a PRMT protocol.

Abstract

Recognition based graphical passwords are inherently vulnerable to shoulder surfing attacks because of their visual mode of interaction. In this paper, we propose and evaluate two novel shoulder-surfing defense techniques for recognition based graphical passwords. These techniques are based on WYSWYE (Where You See is What You Enter) strategy, where the user identifies a pattern of password images within a presented grid of images and replicates it onto another grid. We conducted controlled laboratory experiments to evaluate the usability and security of the proposed techniques. Both the schemes had high login success rates with no failures in authentication. More than seventy percent of participants successfully logged on to the system in their first attempt in both the schemes. The participants were satisfied with the schemes and were willing to use it in public places. In addition, both the schemes were significantly secure against shoulder surfing than normal unprotected recognition based graphical passwords. The login efficiency improved with practice in one of the proposed scheme. We believe, WYSWYE strategy has considerable potential and can easily be extended to other types of authentication systems such as text passwords and PINS.

Abstract

In this paper we are proposing neural network based feature transformation framework for developing emotion independent speaker identification system. Most of the present speaker recognition systems may not perform well during emotional environments. In real life, humans extensively express emotions during conversations for effectively conveying the messages. Therefore, in this work we propose the speaker recognition system, robust to variations in emotional moods of speakers. Neural network models are explored to transform the speaker specific spectral features from any specific emotion to neutral. In this work, we have considered eight emotions namely, Anger, Sad, Disgust, Fear, Happy, Neutral, Sarcastic and Surprise. The emotional databases developed in Hindi, Telugu and German are used in this work for analyzing the effect of proposed feature transformation on the performance of speaker identification system. In this work, spectral features are represented by mel-frequency cepstral coefficients, and speaker models are developed using Gaussian mixture models. Performance of the speaker identification system is analyzed with various feature mapping techniques. Results have demonstrated that the proposed neural network based feature transformation has improved the speaker identification performance by 20 %. Feature transformation at the syllable level has shown the better performance, compared to sentence level.