Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation

In this paper we provide rigorous proof for the convergence of an iterative voting-based image segmentation algorithm called Active Masks. Active Masks (AM) was proposed to solve the challenging task of delineating punctate patterns of cells from fluorescence microscope images. Each iteration of AM consists of a linear convolution composed with a nonlinear thresholding; what makes this process special in our case is the presence of additive terms whose role is to "skew" the voting when prior information is available. In real-world implementation, the AM algorithm always converges to a fixed point. We study the behavior of AM rigorously and present a proof of this convergence. The key idea is to formulate AM as a generalized (parallel) majority cellular automaton, adapting proof techniques from discrete dynamical systems

Automated classification of cricket pitch frames in cricket video

The automated detection of the cricket pitch in a video recording of a cricket match is a fundamental step in content-based indexing and summarization of cricket videos. In this paper, we propose visualcontent based algorithms to automate the extraction of video frames with the cricket pitch in focus. As a preprocessing step, we first select a subset of frames with a view of the cricket field, of which the cricket pitch forms a part. This filtering process reduces the search space by eliminating frames that contain a view of the audience, close-up shots of specific players, advertisements, etc. The subset of frames containing the cricket field is then subject to statistical modeling of the grayscale (brightness) histogram (SMoG). Since SMoG does not utilize color or domain-specific information such as the region in the frame where the pitch is expected to be located, we propose an alternative algorithm: component quantization based region of interest extraction (CQRE) for the extraction of pitch frames. Experimental results demonstrate that, regardless of the quality of the input, successive application of the two methods outperforms either one applied exclusively. The SMoG-CQRE combination for pitch frame classification yields an average accuracy of 98:6% in the best case (a high resolution video with good contrast) and an average accuracy of 87:9% in the worst case (a low resolution video with poor contrast). Since, the extraction of pitch frames forms the first step in analyzing the important events in a match, we also present a post-processing step, viz. , an algorithm to detect players in the extracted pitch frames

QUANTITATIVE DETERMINATION OF CRIZOTINIB IN HUMAN PLASMA WITH HIGHPERFORMANCE LIQUID CHROMATOGRAPHY AND ULTRAVIOLET DETECTION

Objective: A rapid, sensitive, selective, and reproducible reversed-phase high-performance liquid chromatographic method has been developed and validated for the determination of crizotinib (CRZ), a tyrosine kinase inhibitor for targeted therapy of anaplastic lymphoma kinase-positive non-small-cell lung cancer. Methods: The chromatographic separation was carried out in an isocratic mode on an YMC ODS C18 column with a mobile phase consisting of methanol and water containing 0.1% orthophosphoric acid in the ratio of 50:50 v/v at a flow rate of 0.6 ml/min. The run time was maintained for 10 min and detection was monitored at 267 nm. The method involved reproducible liquid-liquid extraction of drug from human plasma using diethyl ether as extracting solvent. Results: CRZ and internal standard retention times were 6.86 and 7.94 min, respectively. Calibration curves were linear over a concentration range of 20.41–2041.14 ng/ml with correlation coefficient 0.9994. The lower limit of quantification for CRZ in plasma was 20 ng/ml. No endogenous substances were found to interfere with the peaks of drug and internal standard. The intra- and inter-day precision was <9.0% and the accuracy ranged from 97% to 112% over the linear range. All stability studies showed that CRZ in plasma sample was stable. Conclusion: This method was found to be simple, selective, precise, accurate, and cost-effective. Hence, the method can be successfully applied to analyze the CRZ concentration in plasma samples for pharmacokinetic and bioequivalence studies

NELIS -Named Entity and Language Identification System: Shared Task System Description

ABSTRACT This paper proposes a simple and elegant solution for language identification and named entity (NE) recognition at a word level, as a part of Subtask-1: Query Word Labeling of FIRE 2015. Given any query q 1 :w 1 w 2 w 3 … w n in Roman script, the task calls for labeling words of the query as English (En) or a member of L, where L = {Bengali (Bn), Gujarati (Gu), Hindi (Hi), Kannada (Kn), Malayalam (Ml), Marathi (Mr), Tamil (Ta), Telugu (Te)}. The approach presented in this paper uses the combination of a dictionary lookup with a Naïve Bayes classifier trained over character n-grams. Also, we devise an algorithm to resolve ambiguities between languages, for any given word in a query. Our system achieved impressive f-measure scores of 85-90% in four languages and 74-80% in another four languages

A New Pairing-Free Certificateless Signcryption Scheme

Signcryption is a cryptographic primitive which provides unforgeability and confidentiality for digital communications. Many signcryption schemes have been constructed in the literature for secure communication between smart objects. But, many of these existing schemes are not secure and inefficient for resource constrained applications like WSNs, Mobile computing, VANETs and IoT applications. To enrich the security and efficiency issues, in this paper, we propose a new signcryption scheme in certificateless based framework and prove its security under the CDHP and ECDLP assumptions. The efficiency analysis indicates that our scheme is more efficient than other existing signcryption schemes and is well suitable for resource-constrained applications

A multiresolution approach to automated classification of protein subcellular location images

<p>Abstract</p> <p>Background</p> <p>Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.</p> <p>Results</p> <p>We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR) decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%.</p> <p>Conclusion</p> <p>We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.</p

Automated Classification of Cricket Pitch Frames in Cricket Video

Automated detection of the cricket pitch is a fundamental step in content-based indexing and summarization of cricketvideos. In this paper, we propose visual-content based algorithms to automate the extraction of video frames with thecricket pitch in focus from input cricket videos. As a preprocessing step, we first select a subset of frames with a viewof the cricket field. This reduces the search space by eliminating frames that contain a view of the audience, close-upshots of specific players, advertisements, etc. The subset of frames containing the cricket field is then processed using astatistical modeling of the grayscale (brightness) histogram (SMoG). Since, in the present day, most videos are shot incolor and SMoG does not utilize this information, we propose an alternative: color quantization based region of interestextraction (CQRE). Experimental results demonstrate that successive application of the two methods outperforms eitherone applied exclusively, regardless of the quality of the input. The SMoG-CQRE combination for cricket pitch detectionyields an average accuracy of 98:6% in the best case (a high resolution video with good contrast) and an average accuracyof 87:9% in the worst case (a low resolution video with poor contrast). Since, the extraction of pitch frames only formsthe first step in analyzing key action frames in a match, we also present an an algorithm for player detection in theseframes

Automated classification of cricket pitch frames in cricket video

The automated detection of the cricket pitch in a video recording of a cricket match is a fundamental step in content-based indexing and summarization of cricket videos. In this paper, we propose visualcontent based algorithms to automate the extraction of video frames with the cricket pitch in focus. As a preprocessing step, we first select a subset of frames with a view of the cricket field, of which the cricket pitch forms a part. This filtering process reduces the search space by eliminating frames that contain a view of the audience, close-up shots of specific players, advertisements, etc. The subset of frames containing the cricket field is then subject to statistical modeling of the grayscale (brightness) histogram (SMoG). Since SMoG does not utilize color or domain-specific information such as the region in the frame where the pitch is expected to be located, we propose an alternative algorithm: component quantization based region of interest extraction (CQRE) for the extraction of pitch frames. Experimental results demonstrate that, regardless of the quality of the input, successive application of the two methods outperforms either one applied exclusively. The SMoG-CQRE combination for pitch frame classification yields an average accuracy of 98:6% in the best case (a high resolution video with good contrast) and an average accuracy of 87:9% in the worst case (a low resolution video with poor contrast). Since, the extraction of pitch frames forms the first step in analyzing the important events in a match, we also present a post-processing step, viz., an algorithm to detect players in the extracted pitch frames

CONVERGENCE BEHAVIOR OF THE ACTIVE MASK SEGMENTATION ALGORITHM

We study the convergence behavior of the Active Mask (AM) framework, originally designed for segmenting punctate image patterns. AM combines the flexibility of traditional active contours, the statistical modeling power of region-growing methods, and the computational efficiency of multiscale and multiresolution methods. Additionally, it achieves experimental convergence to zero-change (fixedpoint) configurations, a desirable property for segmentation algorithms. At its a core lies a voting-based distributing function which behaves as a majority cellular automaton. This paper proposes an empirical measure correlated to the convergence behavior of AM, and provides sufficient theoretical conditions on the smoothing filter operator to enforce convergence. Index Terms — active mask, cellular automata, convergence, segmentation 1