The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated...
We present Deep Sparse-coded Network (DSN), a deep architecture based on multilayer sparse coding. It has been considered difficult to learn a useful feature hierarchy by stacking sparse coding layers in a straightforward manner. The primary reason is the modeling assumption for sparse coding that takes in a dense input and yields a sparse output vector. Applying a sparse coding layer on the output...
Recent advances in clustering have shown that ensuring a minimum separation between cluster centroids leads to higher quality clusters compared to those found by methods that explicitly set the number of clusters to be found, such as k-means. One such algorithm is DP-means, which sets a distance parameter λ for the minimum separation. However, without knowing either the true number of clusters or...
As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding...
Coherent change detection using paired synthetic aperture radar (SAR) images is often performed using a classical coherence estimator that is invariant to the true variances of the populations underlying each paired sample. While attractive, this estimator is biased and requires a significant number of samples to yield good performance. Increasing sample size often results in decreased image resolution...
Fine details revealed by synthetic aperture radar (SAR) coherent change detection (CCD), such as foot prints, require SAR imagery with both high resolution and precision. These large data requirements are at odds with the low bandwidths often available for SAR change detection systems such as those that utilize small unmanned aerial vehicles (UAVs). Here we investigate the interplay between SAR data...
Coherent change detection using paired synthetic aperture radar images is performed using a classical coherence estimator applied under an assumption of complex Gaussian data. The magnitudes of the resulting coherence estimates are plotted as an image and used to gauge changes in the observed scene. In this paper, a two-stage change statistic that combines non-coherent and coherent change detection...
In this paper, we present an algorithm for automatic vehicle track tracing in synthetic aperture radar coherent change detection (SAR CCD) images using search cues. The framework consists of two main steps. The first step uses a rotating matched filter that is modeled to characterize the appearance of vehicle tracks in SAR CCD imagery. For every pixel, the algorithm searches the orientations of the...
Coherent change detection using paired synthetic aperture radar images is typically performed using a classical estimator of coherence applied under an assumption of complex Gaussian data. The magnitudes of the resultant coherence estimates are plotted as an image and used to gauge changes in the observed scene. Here we investigate the suitability of an alternative coherence estimator that further...
This paper introduces a pattern recognition and computer vision approach to mitigating false alarms in synthetic aperture radar (SAR) coherence change detection (CCD) images. In this paper, we perform an automatic detection of roads in SAR CCD images. The approach is based on a curve tracing algorithm originally proposed by Steger with modifications to better suit the goal of curve detection in SAR...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.