A convolution-deconvolution method for improved storage and communication of remotely-sensed image data

An essential feature of remote sensing and digital photogrammetric processes is image compression and communication over digital links. This paper investigates the probability of using a convolution-deconvolution method as a pre-post-processing step in standard digital image compression and restoration. As such, the paper relates to image coding and compression systems whereby an original image can be transmitted or stored in a convolved (i.e. blurred) representation which renders it more compressible. The image is then thoroughly restored to its original state by reversing the convolution process. The compressibility of an image increases with blurring, whereby the relation between the compression ratio (CR) and the blurring scale is almost linear. Hence, by convolving by way of a localised response function (i.e. a linear kernel) and thereby blurring an image before compression, the CR will increase accordingly. In this novel process the response function is applied to a fractal one-dimensional representation of a given image. A blurred image is thus created, which can be shown to contain the details of the original image and thereby restored by reversing the blurring process. The implications of increased CR are examined in terms of the quality of the reconstructed images

Object recognition with pictorial structures

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 51-53).This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images.by Pedro F. Felzenszwalb.S.M

Scale-invariant image editing

We introduce a system architecture for image editing which decouples image filtering from the image size, resulting in a system which allows interactive editing with constant response times, independent of image sizes. Scale invariance means filters are designed to allow scaled rendering from a pre-computed image pyramid, approximating the result of the filter when rendered at full resolution and scaled afterwards. Our implementation of the proposed architecture allows interactive editing on all image sizes with minimal hardware requirements, the least powerful device tested with a 300 megapixel image was based on a dual core ARM Cortex A9 clocked at 1Ghz. The architecture is based on a graph based image editing approach, extended by scaled rendering for all filters. The filter graph is exploited to allow automatic configuration of filter properties and conversion between color spaces, which simplifies filter implementation and increases performance. The handling of image data is based on tiles and a tile cache allows to manage memory requirements and increase interactive performance. The implementation is provided as a portable library written in c and can provides interactive editing on device as slow as last generation smartphones, while at the same time exploiting the performance available to current multi core processors, using effective multithreading. In this work we explore both the architectural details that make this possible as well as the properties of common image editing filters, regarding the required scale invariance. We also examine possible approaches that can be followed to implement practical filters for such as system. Finally, the implemented architecture and filters are extensively tested for performance and accuracy and the results are examined

Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps

This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster analysis over ubiquitous data streams, where two complementary problems arise: first, to generate (local) SOM models over potentially unbounded multi-dimensional non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments. Towards this problematic, original contributions are made in terms of algorithms and methodologies. Two different methods are proposed regarding the first problem. By focusing on visual knowledge discovery, these methods fill an existing gap in the panorama of current methods for cluster analysis over data streams. Moreover, the original SOM capabilities in performing both clustering of observations and features are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies. Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use. This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future