Showing papers on "Range segmentation published in 1994"

Hierarchical morphological segmentation for image sequence coding

Abstract: This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach. >

Quad-tree segmentation for texture-based image query

Abstract: In this paper we propose a technique for segmenting images by texture content with application to indexing images in a large image database Using quad-tree decomposition, texture features are extracted from spatial blocks at a hierarchy of scales in each image The quad-tree is grown by iteratively testing conditions for splitting parent blocks based on texture content of children blocks While this approach does not achieve smooth identification of texture region borders, homogeneous blocks of texture are extracted which can be used in a database index Furthermore, this technique performs the segmentation directly using image spatial-frequency data In the segmentation reported here, texture features are extracted from the wavelet representation of the image This method however, can use other subband decompositions including Discrete Cosine Transform (DCT), which has been adopted by the JPEG standard for image coding This makes our segmentation method extremely applicable to databases containing compressed image data We show application of the texture segmentation towards providing a new method for searching for images in large image databases using “Query-by-texture”

Robust estimation for range image segmentation and reconstruction

Abstract: This correspondence presents a segmentation and fitting method using a new robust estimation technique. We present a robust estimation method with high breakdown point which can tolerate more than 80% of outliers. The method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type. From K samples, we choose one set of sample points that determines a best-fit equation for the largest homogeneous surface patch in the region. This choice is made by measuring a residual consensus (RESC), using a compressed histogram method which is effective at various noise levels. After we get the best-fit surface parameters, the surface patch can be segmented from the region and the process is repeated until no pixel left. The method segments the range image into planar and quadratic surfaces. The RESC method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus. A genetic algorithm is also incorporated to accelerate the random search. >

Method and apparatus for image compression, storage, and retrieval on magnetic transaction cards

Abstract: A transaction card is described in which an identifiable image can be stored in a limited amount of storage space. The image to be stored is converted to a pixel representation. The pixel representation is divided into a plurality of ordered image portions. Each image portion is compared with a set of reference pixel groups. Associated with each pixel group is a signal group. For each image portion, a signal group is chosen for the associated pixel group most closely associated with the pixel image portion. The signal groups are stored as physical patterns on the storage region of a transaction card. The physical patterns are read and converted to electrical signals, the signal groups can be identified. From the signal groups, a reference pixel group is identified and the reference pixel group is positioned with respect to the image at the same location as the image portion. Using this technique, a recognizable image can be constructed from images information compatible with the ISO-7811/2 standards.

Anisotropic diffusion pyramids for image segmentation

Abstract: We introduce the Anisotropic Diffusion Pyramid (ADP), a structure for multiresolution image processing. We also develop the ADP for use in region-based segmentation. The pyramid is constructed using the anisotropic diffusion equations, creating an efficient scale-space representation. Segmentation is accomplished using pyramid node linking. Since anisotropic diffusion preserves edge localization as the scale is increased, the region boundaries in the coarse-to-fine ADP segmentation are accurately delineated. An application to segmentation of remotely sensed data is provided. The results of ADP segmentation are compared to Gaussian-based pyramidal segmentation. The examples show that the ADP has a superior ability to subdivide the image into integral groupings, minimizing the error in boundary localization and in pixel intensity. >

Method of compressing a dynamic range for a radiation image

Abstract: A dynamic range compression method for a radiation image for obtaining processed image signal carrying an image having a dynamic range that is narrower than that of the original image, by processing original image signal representing an original image based on radiation image information transmitted through an object. The method is for obtaining unsharp mask signal by averaging the original image signal in a predetermined mask area containing each pixel point corresponding to each pixel point and for correcting original image signal by providing a correction value that is a function of the unsharp mask signal to obtain a processed image signal. Weighting is applied corresponding to an absolute value of a signal different between a central pixel and a peripheral pixel both within a mask area in the averaging process for obtaining the unsharp mask signal.

Initializing snakes [object delineation]

Abstract: We propose a snake-based approach that lets a user specify only the distant end points of the curve he wishes to delineate without having to supply an almost complete polygonal approximation. We achieve much better convergence properties than those of traditional snakes by using the image information around these end points to provide boundary conditions and by introducing an optimization schedule that allows the snake to take image information into account first only near its extremities and then, progressively, towards its center. These snakes could be used to alleviate the often repetitive task practitioners have to face when segmenting images by abolishing the need to sketch a feature of interest in its entirety, that is, to perform a painstaking, almost complete, manual segmentation. >

A region-region and region-edge cooperative approach of image segmentation

Abstract: In the task of segmentation of some complex pictures, it is often difficult to obtain satisfactory results using only one approach of image segmentation. The tendency toward the integration of several techniques seems to be the best solution. The authors introduce a new approach of image segmentation, which tends to combine several sources of knowledge about the image in a way to produce better segmentation results. First, they try to locate germs that are homogenous by means of a region-region cooperative process. A region growing process is then applied to these germs in order to find the region borders. This process is controlled, on the one hand, by the germs' parameters, and on the other hand by a gradient information obtained by a simple edge detector. Finally, a region merging step is applied on the extracted regions in order to reconstruct regions that have been split by the germs' extraction process. This method has given good segmentation results over several complex natural images. >

A fuzzy logic approach to image segmentation

Abstract: A novel image segmentation algorithm derived in a fuzzy entropy framework is presented. First, the fuzzy entropy function is computed based on fuzzy region width and the Shannon's function of the image. Then all of the local entropy maxima are located in order to find the optimal partition for image segmentation scene local entropy maxima corresponding to the uncertainties among various regions in the image. This algorithm is very effective for the images whose histograms have no clear peaks and valleys, or the number of the segmentation classes is unknown, or the probabilistic model of the image and the different segmentation classes are unknown. A large number of experiments have been carried out on different kinds of images. Good performances of the proposed algorithm have been achieved.

A robust method for registration and segmentation of multiple range images

Abstract: Registration and segmentation of multiple range images are one of the most important problems in range image analysis. This problem has been investigated by a number of researchers, but most of existing methods are easily affected by outlying points (outliers) like noise and occlusion. We first propose a robust method of estimating rigid motion parameters from a pair of range images. This method is an integration of the iterative closest point (ICP) algorithm with the random sampling and the least median of squares (LMS) estimator. We then detect the outliers by thresholding the residuals in the LMS estimation, and finally we classify each pixel into one of five categories to obtain a segmentation. We experimented on real range images taken by two kinds of rangefinders, and observed that our method worked successfully even for noisy data. The proposed method has another advantage of reducing the computational cost. >

A new edge detection algorithm based on a statistical approach

Abstract: A method of generating edge maps thereby segmenting an image into regions based on the Student t-test is presented. By applying this test to compare the distribution functions of the intensities in the neighborhood of a given pixel, the pixel can be accurately classified as either an edge pixel on the boundary between regions, or as a pixel of a particular type of regions. The results show that using 5/spl times/5 window, this method robustly segments both synthetic and natural images, and maintains this performance in the presence of additive noise. >

Method and apparatus for improving motion analysis of fades

Abstract: A method and apparatus for improving motion analysis in fade regions where motion compensation systems based on block matching typically fail to accurately encode images. A previous image is selected from an image sequence. A base image is calculated by adjusting the brightness of the previous image. A displacement vector representing the magnitude and direction of the displacement between at least one region in a target image and a corresponding region in the base image is determined. The displacement vector is applied to a corresponding region in a previous reconstructed image to form a predicted image, and is then encoded. Pixel values in the predicted image are subtracted from corresponding pixel values in the target image to form an error image which is also encoded.

Boundary segmentation by detection of corner, inflection and transition points

Abstract: For future intelligent man-machine systems with vision, it is necessary to visualize the results of shape and motion and analysis of observed objects in the images. As for object recognition, there are at least three steps. The first is to detect edges which correspond to the boundaries of objects (edge detection). The second is to segment each boundary into simple fine or curve segments (image segmentation). The third is to match those features between the data and the model (feature extraction). The paper presents a new method for the second step: boundary segmentation. It can detect not only corners but inflection points on which the sign of the curvature changes and transitional points on which a line and a curve connect smoothly without any delicate threshold. It also calculates the curvature and the normal vector at each point on the boundary with good accuracy. The features extracted by the proposed method are useful for both machine vision and visualization. >

Joint contour-based and motion-based image sequence segmentation for TV image coding at very low bit rate

Abstract: This paper describes a spatio-temporal segmentation of image sequences for object-oriented low bit rate image coding. The spatio-temporal segmentation is received by merging the spatial homogeneous regions into motion homogeneous ones. Each of these regions is characterized by a motion parameters vector and structural information which represents a polygonal approximation of region border. The segmentation of the current frames in the sequence is obtained by prediction and refinement. A predictive coding scheme is developed. Some estimates of the quantity of information are given.

Segmentation based coding of textures using stochastic vector quantization

Abstract: In second generation image compression techniques the image to be compressed is first segmented. The pixels are divided into mutually exclusive spatial regions based on some criteria. After segmentation, the image consists of regions separated by contours. Then, the information is coded describing the shapes and interiors of the regions. The interiors of the regions are usually encoded using polynomials. The objective of this paper is to encode the interior of the regions by stochastic vector quantization techniques. If the segmentation process has been well defined and the obtained regions are homogeneous, then it is possible to design a specific codebook suited to the statistics of each region. The approach is to design the codebook according to some previously defined model for the regions of the image found in the segmentation process. If the approach is combined with efficient contour coding techniques, good visual results for high compression rates are obtained. >

Very low bit-rate video coding using multi-criterion segmentation

Abstract: A region-based segmentation algorithm by multi-criterion for image sequence coding is described in this paper. It is based on morphological segmentation and motion compensation techniques. The concept of multicriterion is introduced into the traditional morphological segmentation process, which takes into account the high temporal redundancy. The current image is segmented into two different kinds of regions: homogeneous gray-level regions coded by texture modeling method or homogeneous motion regions represented by motion compensation. No prediction error needs to be transmitted. Contours are simplified by a new nonlinear filter. Experimental results have demonstrated outstanding performance, with the combination power of contour-texture approach and the motion compensation technique, for very low bitrate image sequence coding. >

Building a B-rep from a segmented range image

Abstract: A segmented range image contains a set of regions representing the projections of the surfaces imaged. Many methods have been proposed to perform the segmentation and surface fitting processes; that is not what this paper is about. Instead, we consider a problem very little analysis has been given to: finding boundary relationships among the regions/surfaces. Since a segmented range image gives us a set of surface patches, it is natural to search for the topology of the 3D surface patches (that is, how the surface patches are connected together). A solution to this problem gives a true 3D boundary representation of the scene. >

Ridge flow models for image segmentation

Abstract: In this paper we introduce a new algorithm for segmentation of medical images of any dimension. Thesegmentation is based on geometric methods and multiscale analysis. A sequence of increasingly blurredimages is created by Gaussian blurring. Each blurred image is segmented by locating its ridges, decom-posing the ridges into curvilinear segments and assigning a unique label to each, and constructing a regionfor each ridge segment based on a flow model which uses vector fields naturally associated with the ridgefinding. The regions from the initial image are leaf nodes in a tree. The regions from the blurred imagesare interior nodes of the tree. Arcs of the tree are constructed based on how regions at one scale mergevia blurring into regions at the next scale. Objects in the image are represented by unions and differences of subtrees of the full tree. The tree is used as input to a visualization program which allows the user to interactively explore the hierarchy and define objects. Some results are provided for a 3—dimensionalmagnetic resonance image of a head.

A hierarchical method for detection of moving objects

Abstract: This paper deals with segmentation and estimation of motion for two successive images in a sequence whatever the camera situation is (static or mobile). A hierarchical method is described. The model energy is composed of two main parts, one is defined at pixel scale, and the other one on regions. These two terms are optimized alternatively. >

Compression of still images using segmentation-based approximation

Abstract: This paper describes a segmentation-based approach for image compression. The image to be compressed is represented as regions and a contour map, with each coded separately. The proposed method is applicable to monochrome, color, and mixed image data.

Method and apparatus for converting N-value image to M-value image, for < NM

Abstract: An image processing method converts an N-value image into an M-value image (N

Detecting multiple image motions by exploiting temporal coherence of apparent motion

Abstract: Motion-based image segmentation becomes inherently ambiguous when apparent motions of different objects are locally or globally similar during a period. To disambiguate the segmentation, temporal coherence between the local image motion at each edge point and the apparent motion of every object is examined over a long sequence. The point is grouped into that segment of the object whose apparent motion is temporally most coherent with the local image motion at the point. >

A hybrid method for range image segmentation

Abstract: This paper presents a hybrid algorithm that combines edge detection and region growing approaches to range image segmentation. Edges detected by this method possess good localization properties. This aids in steering the region growing process towards accurate border partitioning. In addition, the incorporation of the region growing process eliminates internal microedges and provides for missing border reconstruction because it is able to detect weak edges. It is believed that the edge and segmentation maps produced may prove to be valuable for CAD-based modeling purposes and may also help to provide a descriptive syntax at the object identification level.

Segmentation-based image compression with enhanced treatment of textured regions

Abstract: This paper presents a method to preprocess an image so that when segmented it yields a partitioning in which textured regions are approximated with a substantially reduced number of uniform regions (which is desirable for the coding). The segmentation method used to form this representation combines a Gaussian texture model and Gibbs-Markov contour model in order to find regions with boundaries which correspond closely to the objects in the image. Given the image segmentation, an approximation to the original image is generated by filling each region with its mean value. If higher quality reconstruction is desired, the quantized approximation error is also encoded. In order to exploit the reduced sensitivity of the human visual system to the error around edges (visual masking), the error is quantized using three nonlinear quantizers corresponding to the smoothly varying, textured, and remaining areas of the image, respectively. >

Finding thresholds for image segmentation

Abstract: Segmentation methods for images often have cost functions which evaluate the (dis)similarity between pixels or segments. Thresholds on cost values are then used to decide whether or not to grow, join or split segments. The results for a given image critically depend on the selection of the threshold values. In remote sensing, a too low threshold will split up regions of constant ground cover and a too high threshold will join adjacent regions of different ground cover. Optimal thresholds can be determined using different classes of methods: generating cost value distributions from the original image; obtaining statistical distributions from segmented images; comparing a 'true' segmentation with the results of segmentation using a range of thresholds. A so-called 'true' segmentation can be derived from human expert segmentations or from maps obtained by ground surveys or segmentation of higher resolution images. Also artificial images can be generated having the advantage that the segmentation is known to sub-pixel level. Several methods for threshold determination are described for a hybrid segmentation method developed by us. Measures are described for comparison of two segmentations. Results are evaluated using several (parts of) LANDSAT images and artificial generated images.