9449451
The Tree Representation of the Graph Used in Binary Image Processing
Abstract A very useful structure, line adjacency graph, used in binary image processing is mapped onto an extended binary tree in this paper. The latter is more convenient for storing, traversing and pruning, and it occupies less memory space than the former.
41609314
A robust and fast skew detection algorithm for generic documents
Abstract A robust and fast skew detection algorithm based on hierarchical Hough transform is proposed. It is capable of detecting the skew angle for various document images, including technical articles, postal labels, handwritten text, forms, drawings and bar codes. The algorithm is robust even when black margins introduced by photocopying are present in the image and when the document is scanned at a low resolution of 50 dpi. The algorithm consists of two steps. In the first step we quickly extract the centroids of connected components using a graph data structure. Then, a hierarchical Hough transform (at two different angular resolutions) is applied to the selected centroids. The skew angle corresponds to the location of the highest peak in the Hough space. The performance of the algorithm is shown on a number of document images collected from various application domains. The algorithm is not very sensitive to algorithmic parameters. For an A4 size document image scanned at 50 dpi (typically 413 × 575 pixels), our algorithm is able to detect the skew angle with an accuracy of 0.1° in 0.4s of CPU time on a SunSparc 20 workstation.
34993677
Automatic text location in images and video frames
Abstract Textual data is very important in a number of applications such as image database indexing and document understanding. The goal of automatic text location without character recognition capabilities is to extract image regions that contain only text. These regions can then be either fed to an optical character recognition module or highlighted for a user. Text location is a very difficult problem because the characters in text can vary in font, size, spacing, alignment, orientation, color and texture. Further, characters are often embedded in a complex background in the image. We propose a new text location algorithm that is suitable in a number of applications, including conversion of newspaper advertisements from paper documents to their electronic versions, World Wide Web search, color image indexing and video indexing. In many of these applications, it is not necessary to extract all the text, so we emphasize on extracting important text with large size and high contrast. Our algorithm is very fast and has been shown to be successful in extracting important text in a large number of test images.
38916969
A Generic System for Form Dropout
Recent advances in intelligent character recognition are enabling us to address many challenging problems in document image analysis. One of them is intelligent form analysis. This paper describes a generic system for form dropout when the filled-in characters or symbols are either touching or crossing the form frames. We propose a method to separate these characters from form frames whose locations are unknown. Since some of the character strokes are either touching or crossing the form frames, we need to address the following three issues: 1) localization of form frames; 2) separation of characters and form frames; and 3) reconstruction of broken strokes introduced during separation. The form frame is automatically located by finding long straight lines based on the block adjacency graph. Form frame separation and character reconstruction are implemented by means of this graph. The proposed system includes form structure learning and form dropout. First, a form structure-based template is automatically generated from a blank form which includes form frames, preprinted data areas and skew angle. With this form template, our system can then extract both handwritten and machine-typed filled-in data. Experimental results on three different types of forms show the performance of our system. Further, the proposed method is robust to noise and skew that is introduced during scanning.
34534588
Lane boundary detection using a multiresolution Hough transform
Lane boundary detection is the problem of estimating the geometric structure of the lane boundaries of a road based on the images grabbed by a camera on board a vehicle. We use the Hough transform to detect lane boundaries with a parabolic model under a variety of road pavement types, lane structures and weather conditions. In the three-dimensional Hough space, a parabolic curve is represented as a straight line. To simplify the computation, the parametric space can be divided into (i) a two-dimensional space measured by the parameters which are shared by all the lane edges, and (ii) a one-dimensional space of the parameter which makes a distinction among different edges in an image. A multiresolution strategy is used to improve both the speed and accuracy of the Hough transform. Experimental results show that the proposed method is relatively less prone to the image noise and is computationally tractable.
46138594
Document Representation and Its Application to Page Decomposition
Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.
3387694
Compression of cDNA microarray images
The cDNA microarray image technology is a powerful tool for monitoring the expressions of thousands of genes simultaneously. An experiment is comprised of hundreds of images, each image easily over 30MB. Since image processing and statistical analysis tools are still under development, the images are always kept. Current focus on the development of standards makes efficient data transmission an important problem. Though the cost of disk space for storage is decreasing, efficient transmission requires compression. We have developed a partially progressive compression scheme for microarray images, allowing for fast decoding and reprocessing of image subsets. The scheme also permits locally varying image distortion or loss. The degree of loss can be chosen on-line, or be based on local parameters such as the spot intensities, or signal-to-noise ratios. The minimum decodable bitrate depends on the initial choice of parameters. We find that a bitrate of 4.1 bpp (cmp 32 bpp uncompressed) is sufficient for most tasks, such as image segmentation, and gene expression level extraction with a variety of existing methods. The lossless bitrate is about 17.5 bpp, comparable to the state-of-the-art lossless schemes, yet with the added flexibility of a progressive scheme. Our scheme has been tested on microarray images from different labs, and on images of varying quality.
11512625
Hourglass multimedia content and service composition framework for smart room environments
Pervasive environments with their ubiquitous multimedia devices such as video-enabled cell-phones, laptops, PC tablets, digital cameras, HDTV plasma displays are becoming integral part of our workplaces and homes. Furthermore, these digital multimedia devices make it easier and easier to create and view volumes of content that can be stored throughout the Internet. The big challenge is to organize the distributed content, get the distributed multimedia content composed, produced and distributed to users in customized fashion according to users' preference, and their resource availabilities in pervasive environments. Basically, the challenge is to create Internet Interactive Television where the user/interest group, residing in a pervasive environment, corresponds to the TV program director who decides and/or influences what content(s) will be viewed, how it will be composed, presented and delivered. In this paper, we present a vision how this challenge can be solved. We propose a novel hourglass-based system that delivers composite multimedia content to users in pervasive environments, which considers both users' interest in the content and the presentation capabilities of various display devices. This hourglass-based system relies on our content and service composition framework that allows for customized production, distribution and presentation of multimedia content for pervasive environments. We present the hourglass design, possible solutions for some content composition scenarios and validate the solutions via selected experiments.
2105049
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE
We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d. samples. We study the performance of study the performance of thel1-regularized maximum likelihood estimator in the high-dimensional setting, where the number of nodes in the graph p, the number of edges in the graph s and the maximum node degree d, are allowed to grow as a function of the number of samples n. Our main result provides sufficient conditions on (n, p, d) for thel1-regularized MLE estimator to recover all the edges of the graph with high probability. Under some conditions on the model covariance, we show that model selection can be achieved for sample sizes n = Ω(d log(p)), with the error decaying as O(exp(−c log(p))) for some constant c. We illustrate our theoretical results via simulations and show good correspondences between the theoretical predictions and behavior in simulations.
35646601
Iterated logarithmic expansions of the pathwise code lengths for exponential families
Rissanen's (1978, 1986, 1989, 1996) minimum description length (MDL) principle is a statistical modeling principle motivated by coding theory. For exponential families we obtain pathwise expansions, to the constant order, of the predictive and mixture codelengths used in MDL. The results are useful for understanding different MDL forms.
30182996
Requirements Engineering Processes Improvement: A Systematic View
Requirement is the foundation for both software development and project management activities, so an effective requirements process which has a potential influence on the quality of the final software product must be committed by all stakeholders. However current process improvement maturity models pay little attention to requirements engineering process and they are “black-box test” for organizations’ competence. It is the precondition for our research to understand the fundamental issues and to identify the characteristic that requirement process possessed. We suggest a systematic view for requirement process improvement that includes measurement-based improvement for activities, methods, peoples as “white-box test”, emphasizing the people factor, such as communication and collaboration, etc. Our future work will involve structuring the platform or tools which integrate methods, technologies, checklists, templates, lessons-learned, and providing basic supporting for requirements process improvement and distributed requirements development and management.