Anisotropic mean shift based fuzzy c-means segmentation of deroscopy images

Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptions, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. © 2008 IEEE

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the crosscorrelation and the magnitude of the corresponding the crosspower spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported

Automated supervised classification of variable stars II. Application to the OGLE database

We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable objects of unknown class. The results are compared to published classification results based on the so-called extractor methods.Two complementary analyses are carried out in parallel. In both cases, the original time series of OGLE observations of the Galactic bulge and Magellanic Clouds are processed in order to identify and characterize the frequency components. In the first approach, the classifiers are applied to the data and the results analyzed in terms of systematic errors and differences between the definition samples in the training set and in the extractor rules. In the second approach, the original classifiers are extended with colour information and, again, applied to OGLE light curves. We have constructed a classification system that can process huge amounts of time series in negligible time and provide reliable samples of the main variability classes. We have evaluated its strengths and weaknesses and provide potential users of the classifier with a detailed description of its characteristics to aid in the interpretation of classification results. Finally, we apply the classifiers to obtain object samples of classes not previously studied in the OGLE database and analyse the results. We pay specific attention to the B-stars in the samples, as their pulsations are strongly dependent on metallicity.Comment: 42 pages, 39 figures. Accepted for publication in Astronomy and Astrophysic

Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented