Showing papers on "Range segmentation published in 2005"

Spectral segmentation with multiscale graph decomposition

Abstract: We present a multiscale spectral image segmentation algorithm. In contrast to most multiscale image processing, this algorithm works on multiple scales of the image in parallel, without iteration, to capture both coarse and fine level details. The algorithm is computationally efficient, allowing to segment large images. We use the normalized cut graph partitioning framework of image segmentation. We construct a graph encoding pairwise pixel affinity, and partition the graph for image segmentation. We demonstrate that large image graphs can be compressed into multiple scales capturing image structure at increasingly large neighborhood. We show that the decomposition of the image segmentation graph into different scales can be determined by ecological statistics on the image grouping cues. Our segmentation algorithm works simultaneously across the graph scales, with an inter-scale constraint to ensure communication and consistency between the segmentations at each scale. As the results show, we incorporate long-range connections with linear-time complexity, providing high-quality segmentations efficiently. Images that previously could not be processed because of their size have been accurately segmented thanks to this method.

Shape from symmetry

Abstract: We describe a technique for reconstructing probable occluded surfaces from 3D range images. The technique exploits the fact that many objects possess shape symmetries that can be recognized even from partial 3D views. Our approach identifies probable symmetries and uses them to attend the partial 3D shape model into the occluded space. To accommodate objects consisting of multiple parts, we describe a technique for segmenting objects into parts characterized by different symmetries. Results are provided for a real-world database of 3D range images of common objects, acquired through an active stereo rig

Combined morphological-spectral unsupervised image segmentation

Abstract: The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.

A region dissimilarity relation that combines feature-space and spatial information for color image segmentation

Abstract: This paper proposes a methodology that incorporates principles from cluster analysis and graph representation to achieve efficient image segmentation results. More specifically, a feature-based, inter-region dissimilarity relation is considered here in order to determine the dissimilarity matrix in a graph-based segmentation scheme. The calculation of the dissimilarity function between adjacent elementary image regions is based on the proximity of each region's feature vector to the main clusters that are formed by the image samples in the feature space. In contrast to typical segmentation approaches of the literature, the global feature space information is included in the spatial graph representation that was derived from the initial Watershed partitioning. A region grouping process is applied next to form the final segmentation results. The proposed approach was also compared to approaches that use feature-based, or spatial information exclusively, to indicate its effectiveness.

Multi-scale edge detection and image segmentation

Abstract: In this paper, we propose a novel multi-scale edge detection and vector field design scheme. We show that using multiscale techniques edge detection and segmentation quality on natural images can be improved significantly. Our approach eliminates the need for explicit scale selection and edge tracking. Our method favors edges that exist at a wide range of scales and localize these edges at finer scales. This work is then extended to multi-scale image segmentation using our anisotropic diffusion scheme.

Closely coupled object detection and segmentation

Abstract: We propose a closely coupled object detection and segmentation algorithm for enhancing both processes in a cooperative and iterative manner. Figure-ground segmentation reduces the effect of background clutter on template matching; the matched template provides shape constraints on segmentation. More precisely, we estimate the probability of each pixel belonging to the foreground by a weighted sum of the estimates based on shape and color alone. The weight on the shape-based estimate is related to the probability that a familiar object is present and is updated dynamically so that we enforce shape constraints only where the object is present. Experiments on detecting people in images of cluttered scenes demonstrate that the proposed algorithm improves both segmentation and detection. More accurate object boundaries are extracted; higher object detection rates and lower false alarm rates are achieved than performing the two processes separately or sequentially.

Automatic Segmentation of High-resolution Satellite Imagery by Integrating Texture, Intensity, and Color Features

Abstract: High spatial resolution satellite imagery has become an important source of information for geospatial applications. Automatic segmentation of high-resolution satellite imagery is useful for obtaining more timely and accurate information. In this paper, we develop a method and algorithmic framework for automatically segmenting imagery into different regions corresponding to various features of texture, intensity, and color. The central rationale of the method is that information from the three feature channels are adaptively estimated and integrated into a split-merge plus pixel-wise refinement framework. In the procedure for split-merge and refinement, segmentation is realized by comparing similarities between different features of sub-regions. The similarity measure is based on feature distributions. Without a priori knowledge of image content, the image can be segmented into different regions that frequently correspond to different land-use or other objects. Experimental results indicate that the method performs much better in terms of correctness and adaptation than using single feature or multiple features, but with constant weight for each feature. The method can potentially be applied within a broad range of image segmentation contexts.

System and method for multi-label image segmentation

Abstract: A system and method for multi-label image segmentation is provided. The method comprises the steps of: receiving image data including a set of labeled image elements; mapping a change in intensities of the image data to edge weights; determining potentials for each image element in the image data; and assigning a label, based upon the determined potentials, to each image element in the image data.

A contour based image segmentation algorithm using morphological edge detection

Abstract: In this paper, a novel approach for edge-based image segmentation is proposed. Image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, it's always a tough task to partition an object in a graph-based image. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with region growing technique. The images are first enhanced by morphological closing operations, and then detect the edge of the image by morphological dilation residue edge detector. Moreover, we deploy growing seeds into the edge image that obtained by the edge detection procedure. By cross comparing the growing result and the detected edges, the partition lines of the image are generated. In this paper, we presented the theoretical backgrounds and procedure illustrations of the proposed algorithm. Furthermore, the proposed algorithm is implemented in C++ language and evaluate on several images with promising results.

Hill-Manipulation: An Effective Algorithm for Color Image Segmentation.

Abstract: In cluster-based image segmentation techniques, clusters may be viewed as hills in a histogram. These techniques may suffer from large hills that dominate the smaller hills and thus they result in a loss of image details in the segmentation process. In this paper, we propose a novel approach, called Hill-Manipulation algorithm, to solve the problems of the traditional cluster-based image segmentation methods. It starts by segmenting the 3D color histogram into hills according to the number of local maxima found, and then each hill is checked against defined criteria for possible splitting into more homogenous smaller hills. As a result, details of an image are distinguished and the details are captured in the segmentation. Finally, the resulting hills undergo a post-processing task that filters out the small non-significant regions.

Moving image coding apparatus

Abstract: An image coding apparatus determines an image pattern of image data and, based on the determined image pattern, selects a prediction mode for generating predicted pixel values by predicting pixel values in a frame using pixel values in the same frame. Alternatively, based on photographing information concerning input image data, an image coding apparatus selects a prediction mode for generating predicted pixel values by predicting pixel values in a frame using pixel values in the same frame.

Novel coding technique for depth images using quadtree decomposition and plane approximation

Abstract: An efficient way to transmit multi-view images is to send the texture image together with a corresponding depth image. The depth image specifies the distance between each pixel and the camera. With this information, arbitrary views can be generated at the decoder. In this paper, we propose a new algorithm for the coding of depth images that provides an efficient representation of smooth regions as well as geometric features such as object contours. Our algorithm uses a segmentation procedure based on a quadtree decomposition and models the depth image content with piecewise linear functions. We achieved a bit-rate as low as 0.33 bit/pixel, without any entropy coding. The attractivity of the coding algorithm is that, by exploiting specific properties of depth images, no degradations are shown along discontinuities, which is important for perceived depth.

Semi-automatic range to range registration: a feature-based method

Abstract: Our goal is the production of highly accurate photorealistic descriptions of the 3D world with a minimum of human interaction and increased computational efficiency. Our input is a large number of unregistered 3D and 2D photographs of an urban site. The generated 3D representations, after automated registration, are useful for urban planning, historical preservation, or virtual reality (entertainment) applications. A major bottleneck in the process of 3D scene acquisition is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. We have developed novel methods for the accurate and efficient registration of a large number of 3D range scans. The methods utilize range segmentation and feature extraction algorithms. We have also developed a context-sensitive user interface to overcome problems emerging from scene symmetry.

Method and system for segmenting image data

Abstract: Methods and system for segmenting image data are provided The method includes fitting a topological atlas to acquired image data and generating spatially varying priors based on the fitted topological atlas The method further includes segmenting the acquired image data based on the spatially varying priors

Using relative spatial relationships to improve individual region recognition

Abstract: As well as words in text processing, image regions are polysemic and need some disambiguation If the set of representations of two different objects are close or intersecting, a region that is in the intersection will be recognized as being possibly both objects We propose here a way to disambiguate regions using some knowledge on relative spatial positions between these regions Given a segmented image with a list of possible objects for each region, the objective is to find the best set of objects that fits the knowledge A consistency function is constructed that attributes a score to a spatial arrangement of objects in the image The proposed algorithm is demonstrated on an example where we try to recognize backgrounds (sky, water, snow, trees, grass, sand, ground, buildings) in images An evaluation over a database of 10000 images shows that we can reduce the number of false positive while keeping almost the same recognition rate

Technique for detecting a defect of an object by area segmentation of a color image of the object

Abstract: The present invention provides a technique to prevent increase in defect detection processing volume due to the shape of an object subject to the defect detection. An image of an object under inspection with a plurality of color regions is divided into areas by area segmentation according to colors. From the result of the segmentation, an inspection image representing a shape of a specific region among the plurality of color regions is obtained. The inspection image and a comparison image which is comparable at least in part with the inspection image are then compared to detect defects relating to the specific region.

Combination of Multiple Pixel Classifiers for Microscopic Image Segmentation

Abstract: The combination of classifiers has been proposed as a method allowing to improve the quality of recognition sy stems as compared to a single classifier. This paper describes a segmentation scheme based on a combination of pixel classifications. The aim of this paper is to show the influence of the neighborhood information and of the number of classifier used in several combination processes. In the first part, we detail the ground of our study for a microscopic application . Then, we name the different steps of the new segmentation scheme. In the third and fourth part, we detail the different rules that can be used to combine classifiers and the classific tions results obtained on colour microscopic images. Final ly, we draw a conclusion on the improvement of the quality of the segmentation at the end of treatment.

A classification approach for anatomical regions segmentation

Abstract: In this paper, a supervised pixel-based classifier approach for segmenting different anatomical regions in abdominal computed tomography (CT) studies is presented. The approach consists of three steps: texture extraction, classifier creation, and anatomical regions identification. First, a set of co-occurrence texture descriptors is calculated for each pixel from the image data sample; second, a decision tree classifier is built using the texture descriptors and the names of the tissues as class labels. At the conclusion of the classification process, a set of decision rules is generated to be used for classification of new pixels and identification of different anatomical regions by joining adjacent pixels with similar classifications. It is expected that the proposed approach will also help automate different semi-automatic segmentation techniques by providing initial boundary points for deformable models or seed points for split and merge segmentation algorithms. Preliminary results obtained for normal CT studies are presented.

Multi-object segmentation using shape particles

Abstract: Deformable template models, in which a shape model and its corresponding appearance model are deformed to optimally fit an object in the image, have proven successful in many medical image segmentation tasks. In some applications, the number of objects in an image is not known a priori. In that case not only the most clearly visible object must be extracted, but the full collection of objects present in the image. We propose a stochastic optimization algorithm that optimizes a distribution of shape particles so that the overall distribution explains as much of the image as possible. Possible spatial interrelationships between objects are modelled and used to steer the evolution of the particle set by generating new shape hypotheses that are consistent with the shapes currently observed. The method is evaluated on rib segmentation in chest X-rays.

Color image segmentation using acceptable histogram segmentation

Abstract: In this paper, a new method for the segmentation of color images is presented. This method searches for an acceptable segmentation of 1D-histograms, according to a “monotone” hypothesis. The algorithm uses recurrence to localize all the modes in the histogram. The algorithm is applied on the hue, saturation and intensity histograms of the image. As a result, an optimal and accurately segmented image is obtained. In contrast to previous state of the art methods uses exclusively the image color histogram to perform segmentation and no spatial information at all.

Apparatus and method for prediction modes selection based on image formation

Abstract: An image coding apparatus determines an image pattern of image data and, based on the determined image pattern, selects a prediction mode for generating predicted pixel values by predicting pixel values in a frame using pixel values in the same frame. Alternatively, based on photographing information concerning input image data, an image coding apparatus selects a prediction mode for generating predicted pixel values by predicting pixel values in a frame using pixel values in the same frame.

Similarity-based and perception-based image segmentation

Abstract: In this paper, we present a segmentation method that provides perceptually relevant partitions without any a priori knowledge of the image content: first a local homogeneity analysis detects the image areas that have to be segmented. Then segmentation using a similarity criterion is locally performed. At last, segmented regions are grouped using Gestalt criteria. The whole method is presented in a hierarchical framework.

Optimized mean shift algorithm for color segmentation in image sequences

Abstract: The application of the mean shift algorithm to color image segmentation has been proposed in 1997 by Comaniciu and Meer. We apply the mean shift color segmentation to image sequences, as the first step of a moving object segmentation algorithm. Previous work has shown that it is well suited for this task, because it provides better temporal stability of the segmentation result than other approaches. The drawback is higher computational cost. For speed up of processing on image sequences we exploit the fact that subsequent frames are similar and use the cluster centers of previous frames as initial estimates, which also enhances spatial segmentation continuity. In contrast to other implementations we use the originally proposed CIE LUV color space to ensure high quality segmentation results. We show that moderate quantization of the input data before conversion to CIE LUV has little influence on the segmentation quality but results in significant speed up. We also propose changes in the post-processing step to increase the temporal stability of border pixels. We perform objective evaluation of the segmentation results to compare the original algorithm with our modified version. We show that our optimized algorithm reduces processing time and increases the temporal stability of the segmentation.

Non-photorealistic sketching

Abstract: A non-photorealistic technique is described for transforming an original image into a sketch image. The technique includes: segmenting the original image into plural regions to produce a segmented image, wherein the regions are demarcated by respective boundaries; shrinking a boundary of at least one of the plural regions in the segmented image to produce a boundary-shrunk image; and modifying at least one color in the boundary-shrunk image to produce the sketch image.

Segmentation of laparoscopic images: integrating graph-based segmentation and multistage region merging

Abstract: This paper presents a method that combines graph-based segmentation and multistage region merging to segment laparoscopic images. Starting with image preprocessing, including Gaussian smoothing, brightness and contrast enhancement, and histogram thresholding, we then apply an efficient graph-based method to produce a coarse segmentation of laparoscopic images. Next, regions are further merged in a multistage process based on features like grey-level similarity, region size and common edge length. At each stage, regions are merged iteratively according to a merging score until convergence. Experimental results show that our approach can achieve good spatial coherence, accurate edge location and appropriately segmented regions in real surgical images.

Methods and systems for improving quality of an image

Abstract: A method for improving quality of an image is described. The method includes reconstructing a first image of a sample volume, segmenting the first image to generate a first region and a second region, reconstructing a second image of the sample volume, and generating a final image from a combination of the segmentation, the first image, and the second image.