Nonlocal Co-occurrence for Image Downscaling

Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernel-based convolutional filters could be modified to develop efficient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image filtering concept. We propose to use pairwise co-occurrence similarity of the pixelpairs as the range kernel similarity in the filtering operation. The co-occurrence of the pixel-pair is learned directly from the input image. This co-occurrence learning is performed in a neighborhood based fashion all over the image. The proposed method can preserve the high-frequency structures, which were present in the input image, into the downscaled image. The resulting images retain visually important details and do not suffer from edge-blurring artifact. We demonstrate the effectiveness of our proposed approach with extensive experiments on a large number of images downscaled with various downscaling factors.Comment: 9 pages, 8 figure

Multiwavelength Intraday Variability of the BL Lac S5 0716+714

We report results from a 1 week multi-wavelength campaign to monitor the BL Lac object S5 0716+714 (on December 9-16, 2009). In the radio bands the source shows rapid (~ (0.5-1.5) day) intra-day variability with peak amplitudes of up to ~ 10 %. The variability at 2.8 cm leads by about 1 day the variability at 6 cm and 11 cm. This time lag and more rapid variations suggests an intrinsic contribution to the source's intraday variability at 2.8 cm, while at 6 cm and 11 cm interstellar scintillation (ISS) seems to predominate. Large and quasi-sinusoidal variations of ~ 0.8 mag were detected in the V, R and I-bands. The X-ray data (0.2-10 keV) do not reveal significant variability on a 4 day time scale, favoring reprocessed inverse-Compton over synchrotron radiation in this band. The characteristic variability time scales in radio and optical bands are similar. A quasi-periodic variation (QPO) of 0.9 - 1.1 days in the optical data may be present, but if so it is marginal and limited to 2.2 cycles. Cross-correlations between radio and optical are discussed. The lack of a strong radio-optical correlation indicates different physical causes of variability (ISS at long radio wavelengths, source intrinsic origin in the optical), and is consistent with a high jet opacity and a compact synchrotron component peaking at ~= 100 GHz in an ongoing very prominent flux density outburst. For the campaign period, we construct a quasi-simultaneous spectral energy distribution (SED), including gamma-ray data from the FERMI satellite. We obtain lower limits for the relativistic Doppler-boosting of delta >= 12-26, which for a BL\,Lac type object, is remarkably high.Comment: 16 pages, 15 figures, table 2; Accepted for Publication in MNRA

Empirical mode decomposition-based filter applied to multifocal electroretinograms in multiple sclerosis diagnosis

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

Recognizing emotion in faces is important in human interaction and survival, yet existing studies do not paint a consistent picture of the neural representation supporting this task. To address this, we collected magnetoencephalography (MEG) data while participants passively viewed happy, angry and neutral faces. Using time-resolved decoding of sensor-level data, we show that responses to angry faces can be discriminated from happy and neutral faces as early as 90 ms after stimulus onset and only 10 ms later than faces can be discriminated from scrambled stimuli, even in the absence of differences in evoked responses. Time-resolved relevance patterns in source space track expression-related information from the visual cortex (100 ms) to higher-level temporal and frontal areas (200–500 ms). Together, our results point to a system optimised for rapid processing of emotional faces and preferentially tuned to threat, consistent with the important evolutionary role that such a system must have played in the development of human social interactions