Showing papers on "Segmentation-based object categorization published in 1985"

Rule-based image segmentation: A dynamic control strategy approach

Abstract: The structure and functional aspects of a rule-based image segmentation system are briefly described This paper focuses mainly on the control aspects of the system A set of measurements is defined that evaluates the quality of an image segmentation and thereby determines the control To accomplish this, focus of attention areas are created within the image These are processed in an order that depends on their properties, as reflected by the set of performance measures, computed for each area A dynamic control strategy within each area is also determined based on the individual characteristics of that area One aspect involves determining the spatial order in which the system will process the data entries inside the area In addition, an ordering must be established among all the rules included in the model Dynamic strategy setting is formulated as a fuzzy decision-making problem, whose solution depends on the performance parameters Because the individual characteristics of areas are reflected in different performance measurements, the resulting control strategies will vary from one area to the next In addition, the strategy within each area will vary with time to reflect the changing properties This spatial and temporal updating process is designed to ensure both efficiency and improved output

Image processing method including image segmentation

Abstract: An image signal is segmented into regions of constant contrast or variance by applying a generalized-likelihood-ratio test to an image difference signal.

Goal-Directed Textured-Image Segmentation

Abstract: The SLICE segmentation system identifies image regions that differ in gray-level distribution, color, spatial texture, or some other local property. This report concentrates on textured-image segmentation using local texture-energy measures and user delimited training regions. Knowledge of target textures or signatures is combined with knowledge of background textures by using histogram-similarity transforms to identify regions that are similar to a target texture and dissimilar to other textures.

Image Segmentation Algorithms

Abstract: The paper describes a sequence of algorithms used to perform segmentation of aerial images of natural terrain for the purpose of extracting features pertinent to cartographic applications. Topics include image filtering, labeling, automated editing and refinement of the segmentation within a resolution pyramid. These techniques are considered to be preprocessing activities which will, in general, require some editing by trained cartographers. The objective of this work is to minimize the tedium of feature extraction using algorithms that do not require excessive computational overhead.

Segmentation of clouds using cluster analysis

Abstract: The techniques of pattern recognition can provide unique information on cloud structures for cloud climatology programmes. We present a flexible automatic method for segmentation of meteorological satellite data using a multidimensional clustering procedure. The algorithm developed is described, together with its application to cloud data from the METEOSAT-1 satellite. If the data are transformed prior to segmentation by use of the Principal Components Transformation, the results compare favourably with those of alternative methods.

Image segmentation at contour intersections in human focal vision.

Abstract: It would be efficient for the visual image to be broken initially into small segments, each of which could be analyzed separately before being related to the others to build a representation of the whole. The places in the image where such segmentation should occur will depend on the image and will not be fixed points in retinal space. In this study, it is shown that high-precision-shape information cannot be integrated across contour intersections. It is suggested that contour intersections are a particular feature of the retinal image at which segmentation inevitably occurs.

Adaptive hierarchical algorithm for accurate image segmentation

Abstract: A conceptually new algorithm is presented for segmenting textured images into regions in each of which data is modelled as one of C 2-D Markov Random Field (MRF). The algorithm is designed to operate in real time when implemented on new parallel architecture. Gaussian MRF is used to model textures in visible light images of outdoor and indoor scenes. Image segmentation is realized as a maximum likelihood estimation. To simplify the segmentation algorithm, the image is partitioned into disjoint square windows in each of which there will be one or atmost two different texture regions. In any given window the segmentation algorithm is hierarchical and uses a pyramid-like structure. This paper is an extension to material introduced in [1,2] and concentrates on exploring the segmentation accuracy of the algorithm and addressing more fully the question of how the algorithm can operate in adaptive modes when the parameters of the texture field are partially or totally unknown.

Recognition system for run-on handwriting

Abstract: A method of processing a word with the segmentation and recognition steps combined into an overall scheme. This is accomplished by a three step procedure. First, potential or trial segmentation points are derived. This is done in a manner so as to ensure that essentially all true segmentation points are present but also includes extra or not true segmentation points. Second, all combinations of the segments that could reasonably be a character are sent to a character recognizer to obtain ranked choices and corresponding scores. Finally, the recognition results are sorted and combined so that the character sequences having the best cumulative scores are obtained as the best word choices. For a particular word choice there is a corresponding character segmentation, simply the segment combinations that resulted in the chosen characters. With this recognition scheme the initial character segmentation is not final and need not be highly accurate, but is subject to a lesser constraint of containing the true segmentation points.

A Production System For Scene Analysis And Semantically Guided Segmentation

Abstract: A forward-chaining production system has been constructed to analyze sequences of forward-looking infrared (FLIR) images. The system exploits relationships between various scene elements to aid in the identification of small objects. The system also integrates semantic considerations into the process of object/background segmentation. Parameters are first set in a standard edge-based segmenter so as to produce an over-segmentation of the image. A set of production rules is used to merge segments and adjust parameters depending on the initial interpretations of the segments, relationships between segments, and on interpretations of the final segments. Results compare favorably with those obtained from the same standard segmenter used with parameters optimized for interpretation-blind operation.

Segmentation of digitized images through label propagation method

Abstract: A comprehensive region clustering and region labelling algorithm based on label propagation is described. The segmentation algorithms developed can provide, in addition to the features of the image, the boundaries of each feature, a label map (each region is associated with a specific name) and the region adjacency matrix for identifying the inter-regional relations. Using this algorithm, an efficient microprocessor implementation can be realized since the algorithm does not involve any computational complexities. Performance criteria for effective segmentation are developed and tested extensively on a variety of images.

Aspects of SAR Image Segmentation.

Abstract: : An expression for the speckle structure seen on Synthetic Aperture Radar (SAR) images is derived. This model is used to investigate the applicability of the simple segmentation operators usually used for image analysis to the problem of the Segmentation of SAR images. These simple operators are shown to be incapable of segmenting raw SAR images satisfactorily. However they can be applied to successfully to a area averaged SAR images provided certain constraints are met. Possible segmentation approaches are examined in the light of these constraints.

Segmentation of three dimensional range data

Abstract: A new approach to segmentation of range image data into quadric surfaces is treated here. The segmentation consists of three stages. First, an initial segmentation produces coarse surface regions classed as plane, cylinder, or sphere. The algorithm operates on contours, or 2D slices, of the data rather than the 3D range image data. Second, constrained surface fitting is used to obtain surface parameter estimates for each segmented region. And third, a high accuracy segmentation results in both a high resolution boundary between surface regions and accurate quadric surface parameters. The algorithm is hierarchical and works in stages from low to high image resolution while updating surface parameter estimates at each stage. Experimental results with synthetic and real range images are given.

A Quantization Approach To Image Segmentation

Abstract: S t a t e Univers i t of New York a t Buffalo 201 B e l l H a l l Buffalo, NY 14260 The techniques descr ibed i n [2 , 3, 41 can be exterded t o m u l t i l e v e l th reshold ing , bu t with considerable d i f f i c u l t y . I n addi t ion , a l l these techniques are g l o b a l i n na ture . A s a r e s u l t , t h e s e methods f a i l t o provide any information regarding the objec t d e t a i l . Local th reshold ing techniques ( f o r a review see [ 5 ] ) have been proposed i n which threshold a t a poin t depends on the l o c a l p r o p e r t i e s of t he p i x e l o r i t s p o s i t i o n . A new approach us ing a f a s t search method h a s been proposed by Reddy e t a1 [ 6 ] . This method uses one condi t ion of o p t i m a l i t y of t he Lloyd-Max algor i thm ( the re a r e two condi t ions i n the Lloyd-Max algori thm) and computes the threshold i t e r a t i v e l y . Bu t t h e i r a lgori thm f a i l s to see the similarity between the threshold s e l e c t i o n problem and the optimal quant izer design problem, and s t i l l r e q u i r e s histogram computation. ABSTRACT

Fast object segmentation in textured backgrouds

Abstract: In this paper, we deal with the problem of detecting and segmenting objects in textured darkfield digital imagery for automated visual inspection applications. The technique we will follow is based on a sequential application of local operators which serves the purpose of clustering the object and the background gray levels. This procedure can be considered as an extension of average-thresholding type techniques. This algorithm has fast implementations in general purpose image processing pipeline architectures and therefore, it is appealing to real-time computer vision applications. Computational examples showing the effectiveness of the segmentation technique will be discussed.