1,720,956 research outputs found
Modified Fuzzy-Anisotropic Gaussian Kernel and CRB in Denoising SAR Image
Get PDFRadar speckle noise is often modeled as multiplicative noise for such that higher the intensity higher the speckle noise. As a result, the brighter pixel values are having more noise. The presence of speckle not only complicates visual image interpretation but also the classification of automated image is difficult in corrupted SAR image. Therefore, speckle has to be reduced before analyzing the SAR image.Thus, speckle is the main problem (mingled) in Synthetic Aperture Radar (SAR) images. Speckle is existed due to constructive and destructive interference of coherent signal. In order to reduce it, we approach enhanced kernel based filter. Till there are so many techniques are developed to remove speckle content in SAR system. But no proper technique as been developed to remove speckle content completely. In our project MMSE based filter technique is used. We propose a new integrated Fuzzy Anisotropic Gaussian Kernel (FAGK) for denoising Synthetic Aperture Radar (SAR) Images. Here, texture information lies on principal orientation should be multiplied with fuzzy membership function through the anisotropic Gaussian kernel. It presents Cramer -Rao Bound (CRB) which can be estimated by taking ensemble of texture modeled covariance matrix for different denoising methods. Later, CRB can be found for an index of speckle suppression. Thus, developed filter gives good result in preservation of texture and in structure enhancement. It also presents evaluation of speckle suppression ability, where an index named SMPI (Speckle Suppression and Mean Preservation Index). It compares CRB for the evaluation of SMPI index with different denoising method
Compressed Domain Video Zoom Motion Analysis and Saliency Estimation
No full textThe work presented in the thesis is broadly in the domain of compressed domain video analysis. The thesis investigates the camera zoom motion analysis problem, the mixed camera classification problem, and one of the important video applications, namely saliency estimation. The work is motivated by the fact that the zoom motion analysis and the mixed camera classification are relatively less established since the major focus in the video processing community was on investigating the translational motions (pan and tilt) of the camera. Additionally, the saliency estimation in compressed videos is also an open problem that needed attention. The contributions of the thesis begin by investigating the zoom motion analysis problem, which comprises camera zoom motion detection and camera zoom motion classification sub¬problems. In zoom motion detection, the zooming frames are separated from the non¬zooming frames, while zoom motion classification deals with further separation of the zooming frames into zoom¬in and zoom¬out camera types. Towards this goal, the compressed domain block motion vector orientation is modeled utilizing traditional image texture descriptors. Two methods are proposed, the first in which the local ternary patterns are explored for both the zoom motion detection and classification problems and the second in which the local tetra patterns are utilized for the zoom motion detection problem. Such modeling is novel in the sense that the image texture descriptors, which found applications in face recognition and content¬based image retrieval applications, are being explored for the video zoom analysis research problem. Experimental results utilizing block motion vectors extracted from ESME and H.264 compressed videos showed good performance for both methods with a slight advantage to the local tetra patterns. However, the texture descriptors under¬performed when the input block motion vectors were noisy, calling for exploring other localized methods capable of countering motion vector noise. Zoom motion analysis problem is re¬looked by partitioning the inter¬frame block motion vector field into four representative quadrants, which enabled more localized analysis. Two methods are proposed, the first where histogram¬based features, specifically histogram intersection between quadrant histograms for the zoom motion detection problem and KL divergence between quadrant cumulative histograms for the zoom motion classification problem. The second method is on exploring the vector CURL to theoretically model the block motion vector orientation values, followed by extracting features like CURL magnitude for zoom vii motion detection and CURL direction for zoom motion classification problem. Experimental validation showed superior accuracy of detection for the two methods even in the presence of noise, with the CURL method achieving the best results compared to the texture descriptors. The focus in the latter part of the thesis shifted towards exploring the mixed camera motion problem, which consisted of recognizing complex motions, namely panning with tilting, which had not been explored earlier in literature. Inferences drawn from previous methods had suggested that the feature analysis could be improved if some representation scheme could be explored for the block motion vectors instead of directly utilizing the motion vector orientation values. It led to modeling both the orientation and the magnitude of the block motion vectors using the HSI color model. The premise was to pose the camera motion classification problem as a color recognizing task, which was carried out by assigning motion vector orientation to Hue, motion vector magnitude to Saturation while keeping Intensity unchanged. The HSI representation was converted to RGB images. These images were utilized for training a convolutional neural network to classify eleven camera patterns containing seven pure patterns and four mixed camera patterns. Experimental validation along with ablation study demonstrated good accuracy of recognition for the eleven camera patterns even in the presence of noise. The last part of the thesis looked into the compressed domain video saliency problem. Since the texture descriptors were successfully utilized earlier to model the motion vector orientation for the zoom motion analysis problem, an attempt was made to explore if such modeling could also aid in saliency determination. This premise led to exploring two texture descriptors, dual cross patterns and local derivative patterns, for saliency estimation. Two methods are investigated, the first utilizing the dual cross patterns while the second utilizing the local derivative patterns for temporal saliency determination. The spatial saliency was estimated in both methods by modeling the transform residuals using the lifting wavelet transform. The fusion of spatial and temporal saliency maps in both methods was carried out using the Dempster¬Shafer combination rule. Extensive experimental testing using eye tracking data¬set was carried out to benchmark the two proposed methods with state¬of¬the¬art method
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Compressed domain zoom motion detection and classification based on application of local ternary patterns on block motion vectors
No full textVariations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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