8,191 research outputs found
Concurrent investigation of global motion and form processing in amblyopia: an equivalent noise approach
PURPOSE: Directly comparing the motion and form processing in neurologic disorders has remained difficult due to the limitations in the experimental stimulus. In the current study, motion and form processing in amblyopia was characterized using random dot stimuli in different noise levels to parse out the effect of local and global processing on motion and form perception. METHODS: A total of 17 amblyopes (8 anisometropic and 9 strabismic), and 12 visually normal subjects monocularly estimated the global direction of motion and global orientation in random dot kinematograms (RDK) and Glass patterns (Glass), whose directions/orientations were drawn from normal distributions with a range of means and variances that served as external noise. Direction/orientation discrimination thresholds were measured without noise first then variance threshold was measured at the multiples of the direction/orientation threshold. The direction/orientation and variance thresholds were modelled to estimate internal noise and sampling efficiency parameters. RESULTS: Overall, the thresholds for Glass were higher than RDK for all subjects. The thresholds for both Glass and RDK were higher in the strabismic eyes compared with the fellow and normal eyes. On the other hand, the thresholds for anisometropic amblyopic eyes were similar to the normal eyes. The worse performance of strabismic amblyopes was best explained by relatively low sampling efficiency compared with other groups (P < 0.05). CONCLUSIONS: A deficit in global motion and form perception was only evident in strabismic amblyopia. Contrary to the dorsal stream deficiency hypothesis assumed in other developmental disorders, deficits were present in both motion (dorsal) and form (ventral) processing
Asynchrony in image analysis: using the luminance-to-response-latency relationship to improve segmentation
We deal with the probiem of segmenting static images, a procedure known to be difficult in the case of very
noisy patterns, The proposed approach rests on the transformation of a static image into a data flow in which
the first image points to be processed are the brighter ones. This solution, inspired by human perception, in
which strong luminances elicit reactions from the visual system before weaker ones, has led to the notion of
asynchronous processing. The asynchronous processing of image points has required the design of a specific
architecture that exploits time differences in the processing of information. The results otained when very
noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of
the approach to other computer vision problem
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
Spectral-spatial classification of hyperspectral images has been the subject
of many studies in recent years. In the presence of only very few labeled
pixels, this task becomes challenging. In this paper we address the following
two research questions: 1) Can a simple neural network with just a single
hidden layer achieve state of the art performance in the presence of few
labeled pixels? 2) How is the performance of hyperspectral image classification
methods affected when using disjoint train and test sets? We give a positive
answer to the first question by using three tricks within a very basic shallow
Convolutional Neural Network (CNN) architecture: a tailored loss function, and
smooth- and label-based data augmentation. The tailored loss function enforces
that neighborhood wavelengths have similar contributions to the features
generated during training. A new label-based technique here proposed favors
selection of pixels in smaller classes, which is beneficial in the presence of
very few labeled pixels and skewed class distributions. To address the second
question, we introduce a new sampling procedure to generate disjoint train and
test set. Then the train set is used to obtain the CNN model, which is then
applied to pixels in the test set to estimate their labels. We assess the
efficacy of the simple neural network method on five publicly available
hyperspectral images. On these images our method significantly outperforms
considered baselines. Notably, with just 1% of labeled pixels per class, on
these datasets our method achieves an accuracy that goes from 86.42%
(challenging dataset) to 99.52% (easy dataset). Furthermore we show that the
simple neural network method improves over other baselines in the new
challenging supervised setting. Our analysis substantiates the highly
beneficial effect of using the entire image (so train and test data) for
constructing a model.Comment: Remote Sensing 201
Time--Distance Helioseismology Data Analysis Pipeline for Helioseismic and Magnetic Imager onboard Solar Dynamics Observatory (SDO/HMI) and Its Initial Results
The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory
(SDO/HMI) provides continuous full-disk observations of solar oscillations. We
develop a data-analysis pipeline based on the time-distance helioseismology
method to measure acoustic travel times using HMI Doppler-shift observations,
and infer solar interior properties by inverting these measurements. The
pipeline is used for routine production of near-real-time full-disk maps of
subsurface wave-speed perturbations and horizontal flow velocities for depths
ranging from 0 to 20 Mm, every eight hours. In addition, Carrington synoptic
maps for the subsurface properties are made from these full-disk maps. The
pipeline can also be used for selected target areas and time periods. We
explain details of the pipeline organization and procedures, including
processing of the HMI Doppler observations, measurements of the travel times,
inversions, and constructions of the full-disk and synoptic maps. Some initial
results from the pipeline, including full-disk flow maps, sunspot subsurface
flow fields, and the interior rotation and meridional flow speeds, are
presented.Comment: Accepted by Solar Physics topical issue 'Solar Dynamics Observatory
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Background suppressing Gabor energy filtering
In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%
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