Mishra M.K.,KIIT University |
Tarasia N.,KIIT University |
Parida B.R.,STAR |
Communications in Computer and Information Science | Year: 2011
Filtering method is applied to the images corrupted at the time of transmission due to several noises, with varying strengths and different noise probability. Neural network based image filter is one of the most important example of adaptive image filter. Adaptive neural network filter remove various types of noise such as Gaussian noise and impulsive noise. Neural networks are based on the concept of training or learning by examples and have already been applied in several domains of image processing including image filtering. But training of those neural networks consume much time before it is actually tested on such as image filtering. Applying parallelism to image processing is increasingly practical and necessary, as our desktops are becoming multicore machines replacing single core. Therefore, this paper proposes a parallel approach named image decomposition parallel approach to train FLANN (Functional Link Artificial Neural Network). Well trained FLANN is used for rectifying the corrupted pixels to restore the image. Experimental results obtained through SPMD(Single Program Multiple Data) simulation environment show that the proposed parallel approach to train the FLANN is feasible as it substantially reduces the training period and also make it an efficient filter to restore the image fairly well maintaining the quality of the filtered image. Hence, this method is suitable for real time image restoration applications. © 2011 Springer-Verlag Berlin Heidelberg.
Kiss Cs.,Research Center for Astronomy and Earth science |
Muller T.G.,Max Planck Institute for Extraterrestrial Physics |
Kidger M.,European Space Agency |
Mattisson P.,STAR |
Marton G.,Research Center for Astronomy and Earth science
Astronomy and Astrophysics | Year: 2015
The thermal emission of comet C/2013 A1 (Siding Spring) was observed on March 31, 2013, at a heliocentric distance of 6.48 au using the PACS photometer camera of the Herschel Space Observatory. The comet was clearly active, showing a coma that could be traced to a distance of ∼1000″, i.e. ∼50 000 km. Analysis of the radial intensity profiles of the coma provided a dust mass and dust production rate and the derived grain size distribution characteristics indicate an overabundance of large grains in the thermal emission. We estimate that activity started about 6 months before these observations at a heliocentric distance of ∼8 au. © ESO 2015.
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2005
This Small Business Innovation Research (SBIR) Phase I project examines the feasibility of automatically assigning keywords to new images using instance-based learning methods, content analysis and pre-annotated images. This project will provide a new color quantization method that describes images using a common set of representative and discriminatory colors. This results in a reduction of the number of calculations required by the distance metrics employed by instance-based methods in determining which keywords to assign. This project will also investigate the utility of transforming the low-level color domain into a new feature domain, effectively removing the correlations between color features. This reduces the potential for annotation error when the instance-based methods employ distance metrics that does not account for correlations. Users implicitly refine these initial annotations when they perform searches using relevance feedback. By examining the user feedback, the relative importance of the keywords assigned to an image is modified. The more relevant keywords are assigned greater weights and the less relevant smaller weights. This permits erroneously assigned keywords to be effectively ignored. Hence, this system provides an efficient means for automatically assigning keywords to images and allows for automatic corrections by incorporating the results of user searches. This project will provide organizations that are involved in image generation and/or collection to easily and inexpensively annotate new images. Savings are achieved in both the monetary and efficiency arenas. By automating the annotation process, the need for a staff dedicated to examining and categorizing raw data is greatly reduced, resulting in reduction of costs. Furthermore, by automating the process, the speed at which annotations can be assigned is enhanced, allowing for greater throughput. In addition, by annotating the images, keyword-based search and retrieval systems can now be used on the organization's image collection, allowing for greater leverage of existing software products and permitting greater exposure and utilization of the collection.
Star | Date: 1956-04-17
Star | Date: 1998-01-06
antiseptic, namely an antimicrobial and antifungal topical liquid or powder for use on human skin and as sanitizing agent for application to various goods.