Showing papers on "Range segmentation published in 1991"

Volumetric shape description of range data using “Blobby Model”

Abstract: Recently in the field of computer vision, there have been many attempts to obtain a symbolic shape description of an object by fitting simple primitives to the range data of the object. In this paper, we introduce the "Blobby Model" for automatically generating a shape description from range data. This model can express a 3D surface as an isosurface of a scalar field which is produced by a number of field generating primitives. The fields from many primitives are blended with each other and can form a very complicated shape. To determine the number and distribution of primitives required to adequately represent a complex 3D surface, an energy function is minimized which measures the shape difference between the range data and the "Blobby Model". We start with a single primitive and introduce more primitives by splitting each primitive into two further primitives so as to reduce the energy value. In this manner, the shape of the 3D object is slowly recovered as the isosurface produced by many primitives. We have successfully applied this method to human face range data and typical results are shown. The method herein does not require any prior range segmentation.

Object-oriented motion estimation and segmentation in image sequences

Abstract: This contribution presents a method for segmenting video scenes hierarchically into several differently moving objects and subobjects. To this end, both contour and texture information from the single images and information from successive images is used to split up a scene into various objects. Furthermore, each of these objects is characterized by a transform h ( x,T ) with a parameter vector T which implicitely describes the surface shape and the three-dimensional motion of the objects in the scene. In order to estimate T of these transforms, an efficient algorithm is introduced. Thus, we obtain an object-oriented segmentation and a prediction of the image contents from one image to the next, which can be used in low bit-rate image coding.

High quality film image correction and duplication method and system

Abstract: A method and system for correcting a non-linear characteristic of an image, wherein an input image is digitized to produce digital image data representative of the image and a histogram of the digital image data is produced. At least a first pixel value characteristic of a variable under which the image was derived is extracted from the histogram. A plurality of non-linear correction curves relating original pixel values to corrected pixel values for different degrees of the variable are stored in a memory, and a particular one of the correction curves is selected based at least in part on the first extracted pixel value and a predetermined corrected pixel value. Corrected image data are then formed utilizing the selected correction curve by correcting each original pixel value of the image to a respective corrected pixel value based on the relationship therebetween defined by the selected correction curve, and a non-linearity corrected image is produced based on the corrected image data.

Fast segmentation of range images into planar regions

Abstract: A split-and-merge algorithm is proposed for the segmentation of the digitized surface of a range image into planar regions. The geometric data structure used is a triangular tessellation of image domain. This data structure, combined with an adaptive surface approximation technique, allows a better adaptation of the range image segmentation to the surface boundaries. It also provides an efficient neighborhood referencing mechanism, thus resulting in a fast algorithm. >

A new segmentation algorithm for videophone applications based on stereo image pairs

Abstract: The stereo image pair of a speaking person in front of a stationary background taken by two CCD-cameras is used as an input scene for a new segmentation algorithm. The algorithm is based on the phase correlation technique which provides a disparity vector field. A brightness adjustment procedure is performed to provide stereo image pairs suited for the segmentation procedure. Then, a coarse segmentation into background and speaking person is made to achieve a reliable segmentation result. Finally, finer segmentation using a coarse-to-fine control strategy is performed only at the object boundaries. An application is demonstrated by applying a lowpass filter selectively to the background of input sequences for low bit rate image coding algorithms. >

Knowledge-based 3D analysis from 2D medical images

Abstract: An anatomical knowledge-based system for image analysis that interprets CT/MR (computed tomography/magnetic resonance) images of the human chest cavity is reported. The approach utilizes a low-level image analysis system with the ability to analyze the data in bottom-up (or data-driven) and top-down (or model-driven) modes to improve the high-level recognition process. Several image segmentation algorithms, including K-means clustering, pyramid-based region extraction, and rule-based merging, are used for obtaining the segmented regions. To obtain a reasonable number of well-segmented regions that have a good correlation with the anatomy, a priori knowledge in the form of masks is used to guide the segmentation process. Segmentation of the brain is also considered. >

Method of and apparatus for generating image data representing integrated image

Abstract: An image plane (IP) is divided into a plurality of strip regions (R1 -R11). Inputted are image data of image elements to be integrated on the image plane, i.e., images of characters, diagrams and pictures. The strip regions are serially selected and it is determined what image elements are included in the selected strip region. When the selected region includes characters and/or diagrams only, these image elements are integrated in a high resolution and in 1 bit for each pixel. When the selected region includes pictures only, on the other hand, the images of pictures are integrated in a low resolution and in 8 bits for each pixel. Further, if the region includes all the types of the image elements, the integration of the image elements are conducted at a high resolution and in 8 bits for each pixel.

Raster Based Region Growing

Abstract: In some image segmentation applications, region growing is more appropriate than simple thresholding or edge detection. One example of this is the location of intensity peaks in an image, where each peak is defined by the valley between it and any adjacent peaks. Conventional region growing techniques require a seed point for each region, and the region is grown by adding adjacent pixels which match the segmentation criterion. Some segmentation criteria are amenable to raster based region growing. Each pixel in the image is examined in turn. If it matches the inclusion criterion of a region associated with one of the pixels above it, it is allocated to that region and labelled accordingly. If not, a new region is started. Segmentation parameters for each region are held in a table indexed by the region label. In this way multiple regions are grown simultaneously. One disadvantage of this approach is that a single region may have multiple start pixels depending on the region shape. These individual regions eventually merge into a single region when they meet. Raster based region growing is fast since it only requires a single pass through the image to perform the initial segmentation, with an additional pass to relabel adjacent regions which have merged.

An overview of moving object segmentation in video images

Abstract: Moving object segmentation utilizes the temporal information inherent in a sequence of images to extract the moving objects from each image. Image registration, required in applications where the imaging sensor moves, quantifies the relative displacement between the images in the sequence. Three classes of image registration techniques are distinguished; correlation methods, Fourier methods, and feature point extraction. The moving object segmentation algorithms are divided into two categories. The image differencing algorithms presented are absolute differencing using an estimated reference, and temporal variance measurement. The selective segmentation algorithms presented are trajectory analysis, steadiness analysis, and shape analysis. The performance of the different moving object segmentation algorithms is evaluated and results are presented. >

Texture segmentation-based image coder incorporating properties of the human visual system

Abstract: A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image into texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes: perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. Good quality reconstructed images are obtained with 0.08 to 0.3 b/p for three different types of imagery. >

Data representation for subsequent image interpretation.

Abstract: Radiological scans acquired using either the X-ray CT or the NMR imaging techniques' provide a wealth of information about tissue behaviour under that imaging modality and contrast agent. To reason about the image in an interpretation stage the scans have to be converted from a pixel by pixel representation to a symbolic form. The technique used by us to generate such a description is region-based segmentation. Each region refers to a pixel or group of pixels having a common attribute. This work has provided a quantitative measure for the partial evaluation of the segmentation which can be applied independent of attribute or combination of attributes. From our initial studies of the behaviour of CT scans a precept for segmentation was developed. The segmentation employs a one-to-one map as an adaptive mechanism. The segmentation criterion at each point in the image therefore depends on the value at the corresponding point in the map. Any process can be used to generate this map, and so easily utilizes new...

Human visual system properties applied to image segmentation for image compression

Abstract: The authors describe a gray-level image segmentation method for use in segmentation-based image compression. The method consists of two steps: a variation of centroid-linkage region growing to perform the initial segmentation of the image, followed by nonlinear filtering to eliminate visually insignificant image segments. Both steps take advantage of human visual system properties to improve allocation of image segments. Subjective experiments have been conducted to determine the interactions and optimum balance between the steps. It is shown that the proposed two-step approach produces substantially better-quality segmented images than region growing used alone. >

Graph theoretic approach to segmentation of MR images

Abstract: A novel graph theoretic approach to image segmentation is presented, and its application to tissue segmentation in MR images of the human brain is demonstrated. An undirected adjacency graph G is used to represent the image with each vertex of G corresponding to a homogeneous component of the image. Each component may be a single pixel or a connected region which, under a suitable criterion, is homogeneous. All pairs of nodes corresponding to spatially connected pixels or regions in the image are linked by arcs in G. A flow capacity, assigned to each arc, is chosen to reflect the probability that the pair of linked vertices belong to the same region or tissue type. The segmentation is achieved through clustering vertices in G by removing arcs of G to form mutually exclusive subgraphs. The subgraphs formed by the clustering algorithm are optimal in the sense that the largest inter-subgraph maximum flow is minimized. Each of the resulting subgraphs then represents a homogeneous region of the image. Using a suitable choice of the arc capacity function, this approach can be used to segment the image either by searching for statistically homogeneous regions (texture segmentation) or by searching for closed region boundaries (edge detection). A direct implementation of the new segmentation algorithm requires the construction of a flow equivalent spanning tree for G. As the size of the graph G increases, constructing an equivalent tree becomes very inefficient. In order to overcome this problem, an algorithm for hierarchically constructing and partitioning a partially equivalent tree of much reduced size has been developed. This hierarchical algorithm results in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree of G.

Three-dimensional CT image segmentation by volume growing

Abstract: The research reported in this paper is aimed at locating, identifying, and quantifying internal (anatomical or physiological) structures, by 3-D image segmentation. Computerized tomography (CT) images of an object are first processed on a slice-by-slice basis, generating a stack of image slices that have been smoothed and pre-segmented. The image smoothing operation is executed by a spatially adaptive filter, and the 2-D pre-segmentation is achieved by a thresholding process whereby each individual pixel in the input image space is consistently assigned a label, according to its CT number, i.e., the gray-level value. Given a sequence of pre-segmented images as 3-D input scene (a stack of image slices), the spatial connectivity that exists among neighboring image pixels is utilized in a volume growing process which generates a number of well-defined volumetric regions or image solides, each representing an individual anatomical or physiological structure in the input scene. The 3-D segmentation is implemented using a volume growing process so that the aspect of pixel spatial connectivity is incorporated into the image segmentation procedure. To initialize the volume growing process for each volumetric region in the input 3-D scene, a seed location for a region is defined and loaded into a queue data structure called seed queue. The volume growing process consists of a set of procedures that perform different operations on the volumetric data of a CT image sequence. Examples of experiment of the described system with CT image data of several hardwood logs are given to demonstrate usefulness and flexibility of this approach. This allows solutions to industrial web inspection, as well as to several problems in medical image analysis where low-level image segmentation plays an important role toward successful image interpretation tasks.

Segmentation of x ray images using probabilistic relaxation labeling

Abstract: Segmentation is a process of separating objects of interest from their background or from other objects in an image. Without a suitable segmentation scheme, it is very difficult to detect contraband in X-rays images. In this paper, a Probabilistic Relaxation Labeling (PRL) segmentation scheme is presented and compared with other segmentation methods. PRL segmentation is an interative algorithm that labels each pixel in an image by cooperative use of two information sources: the pixel probability and the degree of certainty of its probability supported by the neighboring pixels. The practical implementation and results of the PRL segmentation on X-ray baggage images are also discussed and compared with other segmentation methods. 13 refs., 12 figs.

Combined edge- and region-based method for range image segmentation

Abstract: This paper introduces a method for range image segmentation which combines the useful properties of edge and region-based approaches. In the region-based approach the partial derivatives are first estimated at every point on the image by fitting a quadnc model to a small neighborhood of pixels. Pixels are classified into 10 surface types according to the spatial properties in the neighborhood of each pixel. Then pixels of the same surface type are grouped into geometrically coherent regions. Edge detection employs a two stage method which detects both step and roof edges. Finally the edge and region-based segmentations are combined to form the final segmentation and a set of geometrical features is calculated for each region to be used in further analysis.

Segmentation Of Range Images Using Morphological Operators

Abstract: In this paper we present an algorithm for segmentation of range images using the digitisable operations of morphology. The range image is segmented into planar primitives based on their orientations. The concept of Neighborhood Plane Sets (NPS) introduced by Mukherjee et al [10] has been used. The NPS proposed in [ 10] has been augmented by considering four more orientations. A straightforward technique for the extraction of NPS has been proposed. NPS extraction is followed by a smoothing step. The final segments are extracted using a particle marking scheme.

Integration of edge- and region-based techniques for range image segmentation

Abstract: Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks such as 3-D object recognition a representation of the object(s) present in the image is derived and then matched with stored models to determine the object(s) identity. Surface based representation is the most widely used representation in range image analysis. To generate surface based representation of an object a segmentation of the object into a number of surfaces is needed. In this paper we present an approach for the segmentation of range images into a number of surfaces that in turn can be used to generate surface based representation. The approach integrates both edge detection and region growing techniques to achieve the segmentation. We start by detecting jump edges. Jump edge map is processed and regions surrounded by jump edges are isolated. Next fold edges are detected iteratively using normals and residual. Fold edge map is processed to obtain the final segmented image. Jump and fold edge maps are processed using a Bayesian approach. The apriori knowledge is modeled using Markov Random Field. For jump edges we have used a coupled line and depth process. For fold edges we have combined line residuals and normals to process the fold edge map. The performance of the algorithm on a number of range images is presented.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Low-level image segmentation via texture recognition

Abstract: In this paper, we propose a method for low-level unsupervised image segmentation via texture recognition and feature space clustering. The texture measure is based on the computation of n-tuple features of gray level values within the co-occurrence operator. These features are extracted from small local areas of the image. The strategy results in a feature vector transformation of the image. Self-evolving clustering is then used to group these feature vectors into clusters of homogeneous textured regions. The method as presented is applied to, and shown to be capable of, segmenting natural texture image composites. The method is computationally simple and can be implemented in hardware for real-time operation.

Segmentation of orientation maps by an integration of edge- and region-based methods

Abstract: In this work we propose a segmentation algorithm working on surface normal information. An edge-based coarse segmentation map is computed by the detection of discontinuities in surface orientations. A region-based segmentation is generated by the analysis of surface curvature. A decision making process produces the final segmentation.

Segmentation Of Ventricles In Short Axis NMR Black Blood Images

Abstract: Cardiac NMR imaging experiments yield massive amounts of data, thus eliminating manual processing as a practical analysis approach. We describe a novel, fast, and reliable statistical segmentation algorithm for automatically determining left and right ventricular volume. The algorithm first locates that portion of the image containing the ventricles, analyzes the histogram via unsupervised parametric clustering to determine suitable intensity ranges for blood and tissue, and then applies a region growing to determine ventricular area. A similar procedure is followed for each of the numerous anatomic and temporal slices obtained in the experiment, with past results used to guide segmentation of the remaining slices.

Image Segmentation by Minimum Information Loss

Abstract: The grey colour in a picture is a tool to convey information about the subject. The transition from the original image to its digitized version, by thresholding its grey levels, yields a loss of information- From a “natural” set of axiomatic properties a mathematical representation is first derived for the information loss. This takes into account the grey level histogram of the original picture and that of its digitized thresholded version. This is indeed the only relevant information one has when dealing with a global point-dependent method for image representation by thresholding. It is shown that by using a more sophisticated measure of the information loss one can achieve substantial improvements over previous results.

Segmentation via fusion of edge and needle map

Abstract: This paper presents an integrated image segmentation method using edge and needle map which compensates deficiencies of using either edge-based approach or region-based approach. Segmentation of an image is the first and most difficult step toward symbolic transformation of a raw image, which is essential in image understanding. In industrial applications, the task is further complicated by the ubiquitous presence of specularity in most industrial parts. Three images taken from three different illumination directions were used to separate specular and Lambertian components in the images. Needle map is generated from Lambertian component images using photometric stereo technique. In one channel, edges are extracted and linked from the averaged Lambertian images providing one source of segmentation. The other channel, Gaussian curvature and mean curvature values are estimated at each pixel from least square local surface fit of needle map. Labeled surface type image is then generated using the signs of Gaussian and mean curvatures, where one of ten surface types is assigned to each pixel. Connected regions of identical surface type pixels provide the first level grouping, a rough initial segmentation. Edge information and initial segmentation of surface type are fed to an integration module which interprets the edges and regions in a consistent way. During interpretation regions are merged or split, edges are discarded or generated depending upon global surface fit error and consistency with neighboring regions. The output of integrated segmentation is an explicit description of surface type and contours of each region which facilitates recognition, localization and attitude determination of objects in the image.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Parallel segmentation of range images on a hypercube-connected distributed computer

Abstract: This dissertation develops techniques for the segmentation of three-dimensional range images in parallel environment. An iterative parallel range segmentation process is used to create a view-independent description of the input range data. The system characterizes the underlying scene by using a hierarchical approach where surfaces are segmented using low-order characteristics, under the assumption that most object volumes are bound by relatively smooth surfaces. A parallel connected-component analyzer, implemented by a new algorithm for coarse-grained distributed-memory multiprocessors, is the central element of the system. Furthermore, a general model for distributed-memory algorithms using n-cube topologies is developed and used in the analysis of algorithms necessary for low-level computer vision. This model explicitly relates the concepts of speed-up, efficiency, and load balancing, and provides criteria for the optimum assignment of cube size. To minimize the communication overhead and balance computation with communication, low-level computer vision functions are classified as local or global and parallelization strategies developed for each case.

Image segmentation based on ULCS color difference

Abstract: In studying high efficiency color image coding and image processing, it is important to segment several regions that represent real objects. By performing this region segmentation, we can establish the structural description using the characteristic information of regions. To segment the image into several characteristic regions, we adopt the clustering algorithm in the Uniform Lightness Chromaticness Scale System and the merging process based on the measure of Godlove's color difference.