An automatic technique for visual quality classification for MPEG-1 video

The Centre for Digital Video Processing at Dublin City University developed Fischlar [1], a web-based system for recording, analysis, browsing and playback of digitally captured television programs. One major issue for Fischlar is the automatic evaluation of video quality in order to avoid processing and storage of corrupted data. In this paper we propose an automatic classification technique that detects the video content quality in order to provide a decision criterion for the processing and storage stages

A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

Foregrounds for observations of the cosmological 21 cm line: II. Westerbork observations of the fields around 3C196 and the North Celestial Pole

In the coming years a new insight into galaxy formation and the thermal history of the Universe is expected to come from the detection of the highly redshifted cosmological 21 cm line. The cosmological 21 cm line signal is buried under Galactic and extragalactic foregrounds which are likely to be a few orders of magnitude brighter. Strategies and techniques for effective subtraction of these foreground sources require a detailed knowledge of their structure in both intensity and polarization on the relevant angular scales of 1-30 arcmin. We present results from observations conducted with the Westerbork telescope in the 140-160 MHz range with 2 arcmin resolution in two fields located at intermediate Galactic latitude, centred around the bright quasar 3C196 and the North Celestial Pole. They were observed with the purpose of characterizing the foreground properties in sky areas where actual observations of the cosmological 21 cm line could be carried out. The polarization data were analysed through the rotation measure synthesis technique. We have computed total intensity and polarization angular power spectra. Total intensity maps were carefully calibrated, reaching a high dynamic range, 150000:1 in the case of the 3C196 field. [abridged]Comment: 20 pages, 22 figures, accepted for publication in A&A. A version with full resolution figures is available at http://www.astro.rug.nl/~bernardi/NCP_3C196/bernardi.pd

Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

Detection of Signals from Cosmic Reionization using Radio Interferometric Signal Processing

Observations of the HI 21cm transition line promises to be an important probe into the cosmic dark ages and epoch of reionization. One of the challenges for the detection of this signal is the accuracy of the foreground source removal. This paper investigates the extragalactic point source contamination and how accurately the bright sources ($\gtrsim 1$ ~Jy) should be removed in order to reach the desired RMS noise and be able to detect the 21cm transition line. Here, we consider position and flux errors in the global sky-model for these bright sources as well as the frequency independent residual calibration errors. The synthesized beam is the only frequency dependent term included here. This work determines the level of accuracy for the calibration and source removal schemes and puts forward constraints for the design of the cosmic reionization data reduction scheme for the upcoming low frequency arrays like MWA,PAPER, etc. We show that in order to detect the reionization signal the bright sources need to be removed from the data-sets with a positional accuracy of $\sim 0.1$ arc-second. Our results also demonstrate that the efficient foreground source removal strategies can only tolerate a frequency independent antenna based mean residual calibration error of $\lesssim 0.2$ in amplitude or $\lesssim 0.2$ degree in phase, if they are constant over each days of observations (6 hours). In future papers we will extend this analysis to the power spectral domain and also include the frequency dependent calibration errors and direction dependent errors (ionosphere, primary beam, etc).Comment: accepted by ApJ; 12 pages, 10 figure