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Cell shape identification using digital holographic microscopy  [PDF]
Johan Zakrisson,Staffan Schedin,Magnus Andersson
Physics , 2015, DOI: 10.1364/AO.54.007442
Abstract: We present a cost-effective, simple and fast digital holographic microscopy method based upon Rayleigh-Sommerfeld back propagation for identification of the geometrical shape of a cell. The method was tested using synthetic hologram images generated by ray-tracing software and from experimental images of semi-transparent spherical beads and living red blood cells. Our results show that by only using the real part of the back-reconstructed amplitude the proposed method can provide information of the geometrical shape of the object and at the same time accurately determine the axial position of the object under study. The proposed method can be used in flow chamber assays for pathophysiological studies where fast morphological changes of cells are studied in high numbers and at different heights.
Shape identification and classification in echolocation  [PDF]
Habib Ammari,Minh Phuong Tran,Han Wang
Computer Science , 2013,
Abstract: The paper aims at proposing the first shape identification and classification algorithm in echolocation. The approach is based on first extracting geometric features from the reflected waves and then matching them with precomputed ones associated with a dictionary of targets. The construction of such frequency-dependent shape descriptors is based on some important properties of the scattering coefficients and new invariants. The stability and resolution of the proposed identification algorithm with respect to measurement noise and the limited-view aspect are analytically and numerically quantified.
Automatic Identification of Diatoms with Circular Shape using Texture Analysis  [cached]
Qiaoqi Luo,Yahui Gao,Jinfei Luo,Changping Chen
Journal of Software , 2011, DOI: 10.4304/jsw.6.3.428-435
Abstract: Diatoms are unicellular microscopic algae found in practically any moist environment. Identification of diatom has application in many disciplines, including ecology, archaeology and forensic science. In recent years, the work has been undertaken for automatic identification of diatom. However, the diatom with circular shape has not yet been considered as the uttermost goal of the research. In this research, a method based on texture feature for automatic identification of circular diatom images is presented. We aimed in this research to find the exact location of diatom by using the image segmentation and segmentation adjustment first, and then obtain eigenvectors by Fourier spectrum features, and finally use a BP neural network to effectively classify the diatoms with circular shape.
Automated Feature Identification in Web Applications  [PDF]
Sarunas Marciuska,Cigdem Gencel,Pekka Abrahamsson
Computer Science , 2013,
Abstract: Market-driven software intensive product development companies have been more and more experiencing the problem of feature expansion over time. Product managers face the challenge of identifying and locating the high value features in an application and weeding out the ones of low value from the next releases. Currently, there are few methods and tools that deal with feature identification and they address the problem only partially. Therefore, there is an urgent need of methods and tools that would enable systematic feature reduction to resolve issues resulting from feature creep. This paper presents an approach and an associated tool to automate feature identification for web applications. For empirical validation, a multiple case study was conducted using three well known web applications: Youtube, Google and BBC. The results indicate that there is a good potential for automating feature identification in web applications.
SIFT Feature Matching Algorithm with Local Shape Context  [cached]
Gu Lichuan,Qiao Yulong,Cao Mengru,Guo Qingyan
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: SIFT (Scale Invariant Feature Transform) is one of the most effective local feature of scale, rotation and illumination invariant, which is widely used in the field of image matching. While there will be a lot mismatches when an image has many similar regions. In this study, an improved SIFT feature matching algorithm with local shape context is put forward. The feature vectors are computed by dominant orientation assignment to each feature point based on elliptical neighboring region and with local shape context and then the feature vectors are matched by using Euclidean distance and the X2 distance. The experiment indicates that the improved algorithm can reduce mismatch probability and acquire good performance on affine invariance, improves matching results greatly.
Iterated Diffusion Maps for Feature Identification  [PDF]
Tyrus Berry,John Harlim
Mathematics , 2015,
Abstract: Recently, the theory of diffusion maps was extended to a large class of local kernels with exponential decay which were shown to represent various Riemannian geometries on a data set sampled from a manifold embedded in Euclidean space. Moreover, local kernels were used to represent a diffeomorphism, H, between a data set and a feature of interest using an anisotropic kernel function, defined by a covariance matrix based on the local derivatives, DH. In this paper, we generalize the theory of local kernels to represent degenerate mappings where the intrinsic dimension of the data set is higher than the intrinsic dimension of the feature space. First, we present a rigorous method with asymptotic error bounds for estimating DH from the training data set and feature values. We then derive scaling laws for the singular values of the local linear structure of the data, which allows the identification the tangent space and improved estimation of the intrinsic dimension of the manifold and the bandwidth parameter of the diffusion maps algorithm. Using these numerical tools, our approach to feature identification is to iterate the diffusion map with appropriately chosen local kernels that emphasize the features of interest. We interpret the iterated diffusion map (IDM) as a discrete approximation to an intrinsic geometric flow which smoothly changes the geometry of the data space to emphasize the feature of interest. When the data lies on a product manifold of the feature manifold with an irrelevant manifold, we show that the IDM converges to the quotient manifold which is isometric to the feature manifold, thereby eliminating the irrelevant dimensions. We will also demonstrate empirically that if we apply the IDM to features that are not a quotient of the data space, the algorithm identifies an intrinsically lower-dimensional set embedding of the data which better represents the features.
SCFIA: a statistical corresponding feature identification algorithm for LC/MS
Jian Cui, Xuepo Ma, Long Chen, Jianqiu Zhang
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-439
Abstract: We test SCFIA on publicly available datasets. We first compare its performance with that of warping function based methods, and the results show significant improvements. The performance of SCFIA on replicates datasets and fractionated datasets is also evaluated. In both cases, the accuracy is above 90%, which is near optimal. Finally the coverage of SCFIA is evaluated, and it is shown that SCFIA can find corresponding features in multiple datasets for over 90% peptides identified by Tandem MS.SCFIA can be used for accurate corresponding feature identification in LC-MS. We have shown that peak shape correlation can be used effectively for improving the accuracy. SCFIA provides high coverage in corresponding feature identification in multiple datasets, which serves the basis for integrating multiple LC-MS measurements for accurate peptide quantification.Liquid Chromatography-Mass Spectrometry/Tandem Mass Spectrometry (LC-MS/MS) is a powerful tool for protein identification and quantification [1]. One important task in LC-MS/MS processing is the identification of corresponding features (peaks registered by identical peptides) in multiple datasets, which is critical for the integration of quantification information to reduce measurement variation [2].Before other discussions, we first introduce some definitions that are used throughout the paper. A feature is the two dimensional (retention/elution time - m/z) signal registered by a single charge variant of a peptide. When we consider extracted-ion-chromatograms (XICs), a feature is represented by its LC elution peak in an LC-MS/MS run. If a peptide is picked up by Tandem MS, then its LC elution peak can be located exactly in LC-MS. We refer to such LC peaks as "features with identity". If a peptide is not picked up by Tandem MS, then its elution peak location would be unknown, and its LC peak is called "a feature with unknown identity".If several datasets are collected in an experiment, then each dataset has an associa
Capturing the Surface Texture and Shape of Pollen: A Comparison of Microscopy Techniques  [PDF]
Mayandi Sivaguru, Luke Mander, Glenn Fried, Surangi W. Punyasena
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0039129
Abstract: Research on the comparative morphology of pollen grains depends crucially on the application of appropriate microscopy techniques. Information on the performance of microscopy techniques can be used to inform that choice. We compared the ability of several microscopy techniques to provide information on the shape and surface texture of three pollen types with differing morphologies. These techniques are: widefield, apotome, confocal and two-photon microscopy (reflected light techniques), and brightfield and differential interference contrast microscopy (DIC) (transmitted light techniques). We also provide a first view of pollen using super-resolution microscopy. The three pollen types used to contrast the performance of each technique are: Croton hirtus (Euphorbiaceae), Mabea occidentalis (Euphorbiaceae) and Agropyron repens (Poaceae). No single microscopy technique provided an adequate picture of both the shape and surface texture of any of the three pollen types investigated here. The wavelength of incident light, photon-collection ability of the optical technique, signal-to-noise ratio, and the thickness and light absorption characteristics of the exine profoundly affect the recovery of morphological information by a given optical microscopy technique. Reflected light techniques, particularly confocal and two-photon microscopy, best capture pollen shape but provide limited information on very fine surface texture. In contrast, transmitted light techniques, particularly differential interference contrast microscopy, can resolve very fine surface texture but provide limited information on shape. Texture comprising sculptural elements that are spaced near the diffraction limit of light (~250 nm; NDL) presents an acute challenge to optical microscopy. Super-resolution structured illumination microscopy provides data on the NDL texture of A. repens that is more comparable to textural data from scanning electron microscopy than any other optical microscopy technique investigated here. Maximizing the recovery of morphological information from pollen grains should lead to more robust classifications, and an increase in the taxonomic precision with which ancient vegetation can be reconstructed.
Multifinger Feature Level Fusion Based Fingerprint Identification
Praveen N,Tessamma Thomas
International Journal of Advanced Computer Sciences and Applications , 2012,
Abstract: Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Featurelevel fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification
Feature Fusion Based Audio-Visual Speaker Identification Using Hidden Markov Model under Different Lighting Variations  [PDF]
Md. Rabiul Islam,Md. Abdus Sobhan
Applied Computational Intelligence and Soft Computing , 2014, DOI: 10.1155/2014/831830
Abstract: The aim of the paper is to propose a feature fusion based Audio-Visual Speaker Identification (AVSI) system with varied conditions of illumination environments. Among the different fusion strategies, feature level fusion has been used for the proposed AVSI system where Hidden Markov Model (HMM) is used for learning and classification. Since the feature set contains richer information about the raw biometric data than any other levels, integration at feature level is expected to provide better authentication results. In this paper, both Mel Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) are combined to get the audio feature vectors and Active Shape Model (ASM) based appearance and shape facial features are concatenated to take the visual feature vectors. These combined audio and visual features are used for the feature-fusion. To reduce the dimension of the audio and visual feature vectors, Principal Component Analysis (PCA) method is used. The VALID audio-visual database is used to measure the performance of the proposed system where four different illumination levels of lighting conditions are considered. Experimental results focus on the significance of the proposed audio-visual speaker identification system with various combinations of audio and visual features. 1. Introduction Human speaker identification is bimodal in nature [1, 2]. In a face-to-face conversation, we listen to what others say and at the same time observe their lip movements, facial expressions, and gestures. Especially, if we have a problem in listening due to environmental noise, the visual information plays an important role for speech understanding [3]. Even in the clean environment, speech recognition performance is improved when the talking face is visible [4]. Generally, it is true that audio-only speaker identification system is not sufficiently adequate to meet the variety of user requirements for person identification. The AVSI system promises to alleviate some of the drawbacks encountered by audio-only identification. Visual speech information can play an important role in the improvement of natural and robust human-computer interaction [5, 6]. Indeed, various important human-computer components, such as speaker identification, verification [7], localization [8], speech event detection [9], speech signal separation [10], coding [11], video indexing and retrieval [12], and text-to-speech [13], have been shown to benefit from the visual channel [14]. Audio-visual identification system can significantly improve the performance of
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