Jagath Samarabandu

Multiscale Edge-Based Text Extraction from Complex Images

Text that appears in images contains important and useful information. Detection and extraction of text in images have been used in many applications. In this paper, we propose a multiscale edge-based text extraction algorithm, which can automatically detect and extract text in complex images. The proposed method is a general-purpose text detection and extraction algorithm, which can deal not only with printed document images but also with scene text. It is robust with respect to the font size, style, color, orientation, and alignment of text and can be used in a large variety of application fields, such as mobile robot navigation, vehicle license detection and recognition, object identification, document retrieving, page segmentation, etc

Recent advances in simultaneous localization and map-building using computer vision

Simultaneous localization and map-building (SLAM) continues to draw considerable attention in the robotics community due to the advantages it can offer in building autonomous robots. It examines the ability of an autonomous robot starting in an unknown environment to incrementally build an environment map and simultaneously localize itself within this map. Recent advances in computer vision have contributed a whole class of solutions for the challenge of SLAM. This paper surveys contemporary progress in SLAM algorithms, especially those using computer vision as main sensing means, i.e., visual SLAM. We categorize and introduce these visual SLAM techniques with four main frameworks: Kalman filter (KF)-based, particle filter (PF)-based, expectation-maximization (EM)-based and set membership-based schemes. Important topics of SLAM involving different frameworks are also presented. This article complements other surveys in this field by being current as well as reviewing a large body of research in the area of vision-based SLAM, which has not been covered. It clearly identifies the inherent relationship between the state estimation via the KF versus PF and EM techniques, all of which are derivations of Bayes rule. In addition to the probabilistic methods in other surveys, non-probabilistic approaches are also covered.

An Intrusion Detection System for IEC61850 Automated Substations

This paper proposes the use of an intrusion detection system (IDS) tailored to counter the threats to an IEC61850-automated substation based upon simulated attacks on intelligent electronic devices (IEDs). Intrusion detection (ID) is the process of detecting a malicious attacker. It is an effective and mature security mechanism. However, it is not harnessed when securing IEC61850-automated substations. The IDS of this paper is developed by using data collected by launching simulated attacks on IEDs and launching packet sniffing attacks using forged address resolution protocol (ARP) packets. The detection capability of the system is then tested by simulating attacks and through genuine user activity. A new method for evaluating the temporal risk of an intrusion for an electric substation based upon the statistical analysis of known attacks is also proposed.

Three‐dimensional ultrasound of carotid atherosclerosis: Semiautomated segmentation using a level set‐based method

PURPOSE Three-dimensional ultrasound (3D US) of the carotid artery provides measurements of arterial wall and plaque [vessel wall volume (VWV)] that are complementary to the one-dimensional measurement of the carotid artery intima-media thickness. 3D US VWV requires an observer to delineate the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) of the carotid artery. The main purpose of this work was to develop and evaluate a semiautomated segmentation algorithm for delineating the MAB and LIB of the carotid artery from 3D US images. METHODS To segment the MAB and LIB, the authors used a level set method and combined several low-level image cues with high-level domain knowledge and limited user interaction. First, the operator initialized the algorithm by choosing anchor points on the boundaries, identified in the images. The MAB was segmented using local region- and edge-based energies and an energy that encourages the boundary to pass through anchor points from the preprocessed images. For the LIB segmentation, the authors used local and global region-based energies, the anchor point-based energy, as well as a constraint promoting a boundary separation between the MAB and LIB. The data set consisted of 231 2D images (11 2D images per each of 21 subjects) extracted from 3D US images. The image slices were segmented five times each by a single observer using the algorithm and the manual method. Volume-based, region-based, and boundary distance-based metrics were used to evaluate accuracy. Moreover, repeated measures analysis was used to evaluate precision. RESULTS The algorithm yielded an absolute VWV difference of 5.0% +/- 4.3% with a segmentation bias of -0.9% +/- 6.6%. For the MAB and LIB segmentations, the method gave absolute volume differences of 2.5% +/- 1.8% and 5.6% +/- 3.0%, Dice coefficients of 95.4% +/- 1.6% and 93.1% +/- 3.1%, mean absolute distances of 0.2 +/- 0.1 and 0.2 +/- 0.1 mm, and maximum absolute distances of 0.6 +/- 0.3 and 0.7 +/- 0.6 mm, respectively. The coefficients of variation of the algorithm (5.1%) and manual methods (3.9%) were not significantly different, but the average time saved using the algorithm (2.8 min versus 8.3 min) was substantial. CONCLUSIONS The authors generated and tested a semiautomated carotid artery VWV measurement tool to provide measurements with reduced operator time and interaction, with high Dice coefficients, and with necessary required precision.

2D-3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy.

PURPOSE Three-dimensional (3D) transrectal ultrasound (TRUS)-guided systems have been developed to improve targeting accuracy during prostate biopsy. However, prostate motion during the procedure is a potential source of error that can cause target misalignments. The authors present an image-based registration technique to compensate for prostate motion by registering the live two-dimensional (2D) TRUS images acquired during the biopsy procedure to a preacquired 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. METHODS The authors implemented an intensity-based 2D-3D rigid registration algorithm optimizing the normalized cross-correlation (NCC) metric using Powells method. The 2D TRUS images acquired during the procedure prior to biopsy gun firing were registered to the baseline 3D TRUS image acquired at the beginning of the procedure. The accuracy was measured by calculating the target registration error (TRE) using manually identified fiducials within the prostate; these fiducials were used for validation only and were not provided as inputs to the registration algorithm. They also evaluated the accuracy when the registrations were performed continuously throughout the biopsy by acquiring and registering live 2D TRUS images every second. This measured the improvement in accuracy resulting from performing the registration, continuously compensating for motion during the procedure. To further validate the method using a more challenging data set, registrations were performed using 3D TRUS images acquired by intentionally exerting different levels of ultrasound probe pressures in order to measure the performance of our algorithm when the prostate tissue was intentionally deformed. In this data set, biopsy scenarios were simulated by extracting 2D frames from the 3D TRUS images and registering them to the baseline 3D image. A graphics processing unit (GPU)-based implementation was used to improve the registration speed. They also studied the correlation between NCC and TREs. RESULTS The root-mean-square (RMS) TRE of registrations performed prior to biopsy gun firing was found to be 1.87 ± 0.81 mm. This was an improvement over 4.75 ± 2.62 mm before registration. When the registrations were performed every second during the biopsy, the RMS TRE was reduced to 1.63 ± 0.51 mm. For 3D data sets acquired under different probe pressures, the RMS TRE was found to be 3.18 ± 1.6 mm. This was an improvement from 6.89 ± 4.1 mm before registration. With the GPU based implementation, the registrations were performed with a mean time of 1.1 s. The TRE showed a weak correlation with the similarity metric. However, the authors measured a generally convex shape of the metric around the ground truth, which may explain the rapid convergence of their algorithm to accurate results. CONCLUSIONS Registration to compensate for prostate motion during 3D TRUS-guided biopsy can be performed with a measured accuracy of less than 2 mm and a speed of 1.1 s, which is an important step toward improving the targeting accuracy of a 3D TRUS-guided biopsy system.

Graph cut with ordering constraints on labels and its applications

In the last decade, graph-cut optimization has been popular for a variety of pixel labeling problems. Typically graph-cut methods are used to incorporate a smoothness prior on a labeling. Recently several methods incorporated ordering constraints on labels for the application of object segmentation. An example of an ordering constraint is prohibiting a pixel with a ldquocar wheelrdquo label to be above a pixel with a ldquocar roofrdquo label. We observe that the commonly used graph-cut based alpha-expansion is more likely to get stuck in a local minimum when ordering constraints are used. For certain models with ordering constraints, we develop new graph-cut moves which we call order-preserving moves. Order-preserving moves act on all labels, unlike alpha-expansion. Although the global minimum is still not guaranteed, optimization with order-preserving moves performs significantly better than alpha-expansion. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as ldquoskyrdquo, ldquogrounrdquo, etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graphcut segmentation, which is a novel contribution in itself.

Order-Preserving Moves for Graph-Cut-Based Optimization

In the last decade, graph-cut optimization has been popular for a variety of labeling problems. Typically, graph-cut methods are used to incorporate smoothness constraints on a labeling, encouraging most nearby pixels to have equal or similar labels. In addition to smoothness, ordering constraints on labels are also useful. For example, in object segmentation, a pixel with a “car wheel” label may be prohibited above a pixel with a “car roof” label. We observe that the commonly used graph-cut \alpha-expansion move algorithm is more likely to get stuck in a local minimum when ordering constraints are used. For a certain model with ordering constraints, we develop new graph-cut moves which we call order-preserving. The advantage of order-preserving moves is that they act on all labels simultaneously, unlike \alpha-expansion. More importantly, for most labels \alpha, the set of \alpha-expansion moves is strictly smaller than the set of order-preserving moves. This helps to explain why in practice optimization with order-preserving moves performs significantly better than \alpha-expansion in the presence of ordering constraints. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as “sky,” “ground,” etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graph-cut segmentation, which is a novel contribution in itself.

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

A new biologically inspired optimization algorithm

This paper proposes a new biologically inspired algorithm for optimization. The algorithm, called the Paddy Field Algorithm (PFA) operates by initially scattering seeds at random in the parameter space. The number of seeds of each plant depend on the function value such that a plant closer to the optimum solution produces the most seeds. Out of these, depending on the number of neighbors of the plant, only a percentage will become viable due to pollination. In order to prevent getting stuck in local minima, the seeds of each plant are dispersed. This algorithm is tested on four sample functions along side other conventional algorithms. The effect of various parameters on the performance of the algorithm is also investigated. Its performance is also tested with a hybrid algorithm. The results show that the algorithm performs well.

Investigating the Performance of Corridor and Door Detection Algorithms in Different Environments

The capability of identifying physical structures in an unknown environment is important for autonomous mobile robot navigation and scene understanding. A methodology for detecting corridor and door structures in an indoor environment is proposed, and the performances of the corridor detection algorithm and door detection algorithm applied in different environments are evaluated. In the proposed algorithms, we utilize a feedback mechanism based hypothesis generation and verification (HGV) method to detect corridor and door structures using low level line features in video images. The proposed method consists of low, intermediate, and high level processing stages which correspond to the extraction of low-level features, the formation of hypotheses, and the verification of hypotheses using a feedback mechanism, respectively. The system has been tested on a large number of real corridor images captured by a moving robot in a corridor. The experimental results validated the effectiveness and robustness of the proposed methods with respect to different viewpoints, different robot moving speed, under different illumination conditions and reflection variations.