Showing papers on "Segmentation-based object categorization published in 1997"

Market Segmentation: Conceptual and Methodological Foundations

Abstract: Part 1: Introduction. 1. The Historical Development of the Market Segmentation Concept. 2. Segmentation Bases. 3. Segmentation Methods. 4. Tools for Market Segmentation. Part 2: Segmentation Methodology. 5. Clustering Methods. 6. Mixture Models. 7. Mixture Regression Models. 8. Mixture Unfolding Models. 9. Profiling Segments. 10. Dynamic Segmentation. Part 3: Special Topics in Market Segmentation. 11. Joint Segmentation. 12. Market Segmentation with Tailored Interviewing. 13. Model-Based Segmentation Using Structural Equation Models. 14. Segmentation Based on Product Dissimilarity Judgements. Part 4: Applied Market Segmentation. 15. General Observable Bases: Geo-demographics. 16. General Unobservable Bases: Values and Lifestyles. 17. Product-specific observable Bases: Response-based Segmentation. 18. Product-Specific Unobservable Bases: Conjoint Analysis. Part 5: Conclusions and Directions for Future Research. 19. Conclusions: Representations of Heterogeneity. 20. Directions for Future Research. References. Index.

A methodology for evaluation of boundary detection algorithms on medical images

Abstract: Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images.

Document segmentation system

Abstract: A method and apparatus for segmenting a document which has both text and image regions. The method and apparatus implement a technique in which large text pixels and image pixels are identified in a document having a relatively low resolution. The method and apparatus then detect dark text pixels on a light background region of a document and assign segmentation labels to each pixel. The pixel labels are post-processed using a plurality of syntactic rules to correct mislabeling of pixels. This process does not change the visual perception of the image regions in the document. Pixels identified as being in the background region of the document are assigned a white label and pixels identified as being in the text region are assigned a black label. The resulting processed document contains sharp black text and white background, resulting in improved perceptual quality and efficient ink utilization during a printing process.

Robust, object-based high-resolution image reconstruction from low-resolution video

Abstract: We propose a robust, object-based approach to high-resolution image reconstruction from video using the projections onto convex sets (POCS) framework. The proposed method employs a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the quality of the reconstructed image. Procedures for the computation of the validity map and segmentation map are presented. Experimental results demonstrate the improvement in image quality that can be achieved by the proposed methods.

A Trainable Rule-Based Algorithm for Word Segmentation

Abstract: This paper presents a trainable rule-based algorithm for performing word segmentation. The algorithm provides a simple, language-independent alternative to large-scale lexical-based segmenters requiring large amounts of knowledge engineering. As a stand-alone segmenter, we show our algorithm to produce high performance Chinese segmentation. In addition, we show the transformation-based algorithm to be effective in improving the output of several existing word segmentation algorithms in three different languages.

NeTra-V: toward an object-based video representation

Abstract: There is a growing need for new representations of video that allow not only compact storage of data but also content-based functionalities such as search and manipulation of objects. We present here a prototype system, called NeTra-V, that is currently being developed to address some of these content related issues. The system has a two-stage video processing structure: a global feature extraction and clustering stage, and a local feature extraction and object-based representation stage. Key aspects of the system include a new spatio-temporal segmentation and object-tracking scheme, and a hierarchical object-based video representation model. The spatio-temporal segmentation scheme combines the color/texture image segmentation and affine motion estimation techniques. Experimental results show that the proposed approach can handle large motion. The output of the segmentation, the alpha plane as it is referred to in the MPEG-4 terminology, can be used to compute local image properties. This local information forms the low-level content description module in our video representation. Experimental results illustrating spatio- temporal segmentation and tracking are provided.

Segmentation of Expository Texts by Hierarchical Agglomerative Clustering

Abstract: We propose a method for segmentation of expository texts based on hierarchical agglomerative clustering. The method uses paragraphs as the basic segments for identifying hierarchical discourse structure in the text, applying lexical similarity between them as the proximity test. Linear segmentation can be induced from the identified structure through application of two simple rules. However the hierarchy can be used also for intelligent exploration of the text. The proposed segmentation algorithm is evaluated against an accepted linear segmentation method and shows comparable results.

Using encoding cost data for segmentation of compressed image sequences

Abstract: A method for processing compressed digital data derived from an original image sequence, the data being organized as a set of image frames, each image frame comprising a set of blocks, each block including a string of bits corresponding to an area of the original image frame in the original image sequence. A cost function is derived as a number related to the amount of bits spent to encode a block, sets of blocks, an image frame, or sets of image frames. A segmentation technique is applied to the map with cost functions. Temporal segmentation is performed by analyzing cost functions associated with each image frame. In both cases auxiliary functions can be used to improve the segmentation quality. The segmented regions of a image frame or sets of image frames can be identified, replaced, printed, or processed in special manners.

Synthetic aperture radar image segmentation by a detail preserving Markov random field approach

Abstract: A multichannel image segmentation method is imposed that utilizes Markov random fields (MRFs) with adaptive neighborhood (AN) systems. Bayesian inference is applied to realize the combination of evidence from different knowledge sources. In such a way, optimization of the shape of a neighborhood system is achieved by following a criterion that makes use of the Markovian property exploiting the local image content. The MRF segmentation approach with AN systems (MRF-AN) makes it possible to better preserve small features and border areas. The purpose of the paper is to show the usefulness of the concept of MRF-AN for SAR image segmentation.

Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation

Abstract: This paper addresses the estimation of fuzzy Gaussian distribution mixture with applications to unsupervised statistical fuzzy image segmentation. In a general way, the fuzzy approach enriches the current statistical models by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several thematic classes on the same site. We introduce a new procedure for estimating of fuzzy mixtures, which is an adaptation of the iterative conditional estimation (ICE) algorithm to the fuzzy framework, We first describe the blind estimation, i.e., without taking into account any spatial information, valid in any context of independent noisy observations. Then we introduce, in a manner analogous to classical hard segmentation, the spatial information by two different approaches: contextual segmentation and adaptive blind segmentation. In the first case, the spatial information is taken into account at the segmentation step level, and in the second case it is taken into account at the parameter estimation step level. The results obtained with the iterative conditional estimation algorithm are compared to those obtained with expectation-maximization (EM) and the stochastic EM algorithms, on both parameter estimation and unsupervised segmentation levels, via simulations. The methods proposed appear as complementary to the fuzzy C-means algorithms.

A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation

Abstract: A multi-scale segmentation technique for line-like structures in 2D and 3D medical images is presented It is based on normalized second derivatives and on the eigenvector analysis of the Hessian matrix The method allows for the estimation of the local diameter, the longitudinal direction and the contrast of line-structures and for the distinction between edge-like and line-like structures The characteristics of the method in respect to several analytic line-profiles as well as the influence of neighboring structures and line-bending is discussed The method is applied to 3D medical images

Automatic Segmentation and Classification of Outdoor Images Using Neural Networks

Abstract: The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perceptron is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.

Unsupervised Image Segmentation Using Markov Random Field Models

Abstract: We present an unsupervised segmentation algorithm based on a Markov Random Field model for noisy images The algorithm finds the the most likely number of classes, their associated model parameters and generates a corresponding segmentation of the image into these classes This is achieved according to the MAP criterion To facilitate this, an MCMC algorithm is formulated to allow the direct sampling of all the above parameters from the posterior distribution of the image To allow the number of classes to be sampled, a reversible jump is incorporated into the Markov Chain The jump enables the possible splitting and combining of classes and consequently, their associated regions within the image Experimental results are presented showing rapid convergence of the algorithm to accurate solutions

Combining region splitting and edge detection through guided Delaunay image subdivision

Abstract: In this paper, an adaptive split-and-merge segmentation method is proposed. The splitting phase of the algorithm employs the incremental Delaunay triangulation competent of forming grid edges of arbitrary orientation, and position. The tessellation grid, defined by the Delaunay triangulation, is adjusted to the semantics of the image data by combining similarity and difference information among pixels. Experimental results on synthetic images show that the method is robust to different object edge orientations, partially weak object edges and very noisy homogeneous regions. Experiments on a real image indicate that the method yields good segmentation results even when there is a quadratic sloping of intensities particularly suited for segmenting natural scenes of man-made objects.

Image segmentation by a deformable contour model incorporating region analysis

Abstract: Deformable contour models are useful tools for image segmentation. However, many models depend mainly on local edge-based image features to guide the convergence of the contour. This makes the models sensitive to noise and the initial estimate. Our model incorporates region-based image features to improve its convergence and to reduce its dependence on initial estimation. Computational efficiency is achieved by an optimization strategy, modified from the greedy algorithm of Williams and Shah. The model allows a simultaneous optimization of multiple contours, making it useful for a large variety of segmentation problems.

Volumetric object reconstruction using the 3D-MRF model-based segmentation [magnetic resonance imaging]

Abstract: A number of segmentation algorithms have been developed, but those algorithms are not effective on volume reconstruction because they are limited to operating only on two-dimensional (2-D) images Here, the authors propose the volumetric object reconstruction method using the three-dimensional Markov random field (3D-MRF) model-based segmentation The 3D-MRF model is known to be one of the most efficient ways to model spatial contextual information The method is compared with the 2-D region growing scheme under three types of interpolation The results show that the proposed method is better in terms of image quality than the other methods

Segmentation of Medical Image Objects Using Deformable Shape Loci

Abstract: Robust segmentation of normal anatomical objects in medical images requires (1) methods for creating object models that adequately capture object shape and expected shape variation across a population, and (2) methods for combining such shape models with unclassified image data to extract modeled objects. Described in this paper is such an approach to model-based image segmentation, called deformable shape loci (DSL), that has been successfully applied to 2D MR slices of the brain ventricle and CT slices of abdominal organs. The method combines a model and image data by warping the model to optimize an objective function measuring both the conformation of the warped model to the image data and the preservation of local neighbor relationships in the model. Methods for forming the model and for optimizing the objective function are described.