Showing papers on "Range segmentation published in 2007"

Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration

Abstract: We present a parameter free approach that utilizes multiple cues for image segmentation. Beginning with an image, we execute a sequence of bottom-up aggregation steps in which pixels are gradually merged to produce larger and larger regions. In each step we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using a mixture of experts formulation. This probabilistic approach is integrated into a graph coarsening scheme providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. We test our method on a variety of gray scale images and compare our results to several existing segmentation algorithms.

Color Image Segmentation Based on Mean Shift and Normalized Cuts

Abstract: In this correspondence, we develop a novel approach that provides effective and robust segmentation of color images. By incorporating the advantages of the mean shift (MS) segmentation and the normalized cut (Ncut) partitioning methods, the proposed method requires low computational complexity and is therefore very feasible for real-time image segmentation processing. It preprocesses an image by using the MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of the image. The segmented regions are then represented by using the graph structures, and the Ncut method is applied to perform globally optimized clustering. Because the number of the segmented regions is much smaller than that of the image pixels, the proposed method allows a low-dimensional image clustering with significant reduction of the complexity compared to conventional graph-partitioning methods that are directly applied to the image pixels. In addition, the image clustering using the segmented regions, instead of the image pixels, also reduces the sensitivity to noise and results in enhanced image segmentation performance. Furthermore, to avoid some inappropriate partitioning when considering every region as only one graph node, we develop an improved segmentation strategy using multiple child nodes for each region. The superiority of the proposed method is examined and demonstrated through a large number of experiments using color natural scene images.

Video segmentation using statistical pixel modeling

Abstract: A method for segmenting video data into foreground and background (324) portions utilizes statistical modeling of the pixels Λ statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared (326) with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system The method for segmenting video data may be implemented in hardware.

Soft Color Segmentation and Its Applications

Abstract: We propose an automatic approach to soft color segmentation, which produces soft color segments with an appropriate amount of overlapping and transparency essential to synthesizing natural images for a wide range of image-based applications. Although many state-of-the-art and complex techniques are excellent at partitioning an input image to facilitate deriving a semantic description of the scene, to achieve seamless image synthesis, we advocate a segmentation approach designed to maintain spatial and color coherence among soft segments while preserving discontinuities by assigning to each pixel a set of soft labels corresponding to their respective color distributions. We optimize a global objective function, which simultaneously exploits the reliability given by global color statistics and flexibility of local image compositing, leading to an image model where the global color statistics of an image is represented by a Gaussian mixture model (GMM), whereas the color of a pixel is explained by a local color mixture model where the weights are defined by the soft labels to the elements of the converged GMM. Transparency is naturally introduced in our probabilistic framework, which infers an optimal mixture of colors at an image pixel. To adequately consider global and local information in the same framework, an alternating optimization scheme is proposed to iteratively solve for the global and local model parameters. Our method is fully automatic and is shown to converge to a good optimal solution. We perform extensive evaluation and comparison and demonstrate that our method achieves good image synthesis results for image-based applications such as image matting, color transfer, image deblurring, and image colorization.

Generation of depth map for an image

Abstract: An image processing unit comprises a first processing unit (101) which generates a depth indication map for an image. The depth indication map may be, for example, an image object separation mask or a predetermined depth profile or background depth map. A second processing unit (103) generates a modified depth indication map by filtering the depth indication map in response to image characteristics of the image. The image adaptive filtering may, for example, provide a more accurate separation mask and/or may modify the predetermined depth profile to reflect the specific image. A third processing unit (105) generates an image depth map for the image in response to the modified depth indication map. The image depth map comprises data representing a depth of at least one image region of the image. The invention leads to the generation of an improved depth map for an image.

An Image Segmentation Algorithm in Image Processing Based on Threshold Segmentation

Abstract: Image segmentation is a key technology in image processing, and threshold segmentation is one of the methods used frequently. Aimed at that only one threshold or several thresholds are set in traditional threshold-based segmentation algorithm, it is difficult to extract the complex information in an image, a new segmentation algorithm that each pixel in the image has its own threshold is proposed. In this algorithm, the threshold of a pixel in an image is estimated by calculating the mean of the grayscale values of its neighbor pixels, and the square variance of the grayscale values of the neighbor pixels are also calculated as an additional judge condition, so that the result of the proposed algorithm is the edge of the image. In fact the proposed algorithm is equal to an edge detector in image processing. Experimental results demonstrate that the proposed algorithm could produce precise image edge, while it is reasonable to estimate the threshold of a pixel through the statistical information of its neighbor pixels.

Segmentation-based image processing system

Abstract: A digital image can be processed by an image processing method that calculates a gradient map for the digital image, calculates a density function for the gradient map, calculates a modified gradient map using the gradient map, the density function and the selected scale level, and segments the modified gradient map. Prior to segmenting the modified gradient map, a sub-image of the digital image can be segmented at the selected scale level to determine if the selected scale level will give the desired segmentation.

Apparatus and Method for Image Labeling

Abstract: An apparatus for labelling images comprises a segmentation processor ( 103 ) which segments an image into image segments. A segment label processor ( 105 ) assigns segment labels to the image segments and a relation processor ( 107 ) determines segment relations for the image segments. A CRP model processor ( 109 ) generates a Constraint Reasoning Problem model which has variables corresponding to the image segments and constraints reflecting the image segment relations. Each variable of the model has a domain comprising image segment labels assigned to an image segment of the variable. A CRP processor ( 111 ) then generates image labelling for the image by solving the Constraint Reasoning Problem model. The invention may allow improved automated labelling of images.

2D-To-3D Stereoscopic Conversion : Depth-Map Estimation in a 2D Single-View Image

Abstract: With increasing demands of 3D contents, conversion of many existing two-dimensional contents to three-dimensional contents has gained wide interest in 3D image processing. It is important to estimate the relative depth map in a single-view image for the 2D-To-3D conversion technique. In this paper, we propose an automatic conversion method that estimates the depth information of a single-view image based on degree of focus of segmented regions and then generates a stereoscopic image. Firstly, we conduct image segmentation to partition an image into homogeneous regions. Then, we construct a higher-order statistics (HOS) map, which represents the spatial distribution of high-frequency components of the input image. the HOS is known to be well suited for solving detection and classification problems because it can suppress Gaussian noise and preserve some of non-Gaussian information. We can estimate a relative depth map with these two cues and then refine the depth map by post-processing. Finally, a stereoscopic image is generated by calculating the parallax values of each region using the generated depth-map and the input image.

Hand Segmentation Using 2D/3D Images

Abstract: This paper describes a fast and robust segmentation technique based on the fusion of 2D/3D images for gesture recognition. These images are provided by the novel 3D Time-of-Flight (TOF) camera which has been implemented in our research center (ZESS). Using modulated infrared lighting, this camera generates an intensity image with the range information for each pixel of a Photonic Mixer Device (PMD) sensor. The intensity and range data are fused to be used as the input information for the segmentation algorithm. Our proposed segmentation technique is based on the combination of two unsupervised clustering approaches: K-Means and Expectation Maximization (EM). They both attempt to find the centers of natural clusters in the fused data. The experimental results show that the proposed gesture segmentation technique can successfully segment the hand from user’s body, face, arm or other objects in the scene under variant illumination conditions in real time.

Image segmentation using topological persistence

Abstract: This paper presents a new hybrid split-and-merge image segmentation method based on computational geometry and topology using persistent homology. The algorithm uses edge-directed topology to initially split the image into a set of regions based on the Delaunay triangulations of the points in the edge map. Persistent homology is used to generate three types of regions: p-persistent regions, p-transient regions, and d-triangles. The p-persistent regions correspond to core objects in the image, while p-transient regions and d-triangles are smaller regions that may be combined in the merge phase, either with p-persistent regions to refine the core or with other p-transient and d-triangles regions to potentially form new core objects. Performing image segmentation based on topology and persistent homology guarantees several nice properties, and initial results demonstrate high quality image segmentation.

Arrangement and method for three-dimensional depth image construction

Abstract: Three-dimensional image data is generated. According to an example embodiment, three-dimensional depth information is estimated from a still image. A set of monocular images and their corresponding ground-truth depth maps are used to determine a relationship between monocular image features and the depth of image points. For different points in a particular image, the determined relationship is used together with local and global image features including monocular cues to determine relative depths of the points.

Localization of nodules in a radiographic image

Abstract: A method for determining a location of an object in a radiographic image by segmentation of a region in the image comprises the steps of: determining a first image intensity that is characteristic of high image intensities in the region; determining a second image intensity that is characteristic of low image intensities outside of the region; and determining if a pixel is added to or removed from the region based on the similarity of the pixel's intensity to the first and second intensity.

Range Image Segmentation for Modeling and Object Detection in Urban Scenes

Abstract: We present fast and accurate segmentation algorithms of range images of urban scenes. The utilization of these algorithms is essential as a pre-processing step for a variety of tasks, that include 3D modeling, registration, or object recognition. The accuracy of the segmentation module is critical for the performance of these higher-level tasks. In this paper, we present a novel algorithm for extracting planar, smooth non-planar, and non-smooth connected segments. In addition to segmenting each individual range image, our methods also merge registered segmented images. That results in coherent segments that correspond to urban objects (such as facades, windows, ceilings, etc.) of a complete large scale urban scene. We present results from experiments of one exterior scene (Cooper Union building, NYC) and one interior scene (Grand Central Station, NYC).

Automatic Image Segmentation by Tree Pruning

Abstract: The Image Foresting Transform (IFT) is a tool for the design of image processing operators based on connectivity, which reduces image processing problems into an optimum-path forest problem in a graph derived from the image. A new image operator is presented, which solves segmentation by pruning trees of the forest. An IFT is applied to create an optimum-path forest whose roots are seed pixels, selected inside a desired object. In this forest, object and background are connected by optimum paths (leaking paths), which cross the object's boundary through its "most weakly connected" parts (leaking pixels). These leaking pixels are automatically identified and their subtrees are eliminated, such that the remaining forest defines the object. Tree pruning runs in linear time, is extensible to multidimensional images, is free of ad hoc parameters, and requires only internal seeds, with little interference from the heterogeneity of the background. These aspects favor solutions for automatic segmentation. We present a formal definition of the obtained objects, algorithms, sufficient conditions for tree pruning, and two applications involving automatic segmentation: 3D MR-image segmentation of the human brain and image segmentation of license plates. Given that its most competitive approach is the watershed transform by markers, we also include a comparative analysis between them.

Novel classification and segmentation techniques with application to remotely sensed images

Abstract: The article deals with some new results of investigation, both theoretical and experimental, in the area of image classification and segmentation of remotely sensed images. The article has mainly four parts. Supervised classification is considered in the first part. The remaining three parts address the problem of unsupervised classification (segmentation). The effectiveness of an active support vector classifier that requires reduced number of additional labeled data for improved learning is demonstrated in the first part. Usefulness of various fuzzy thresholding techniques for segmentation of remote sensing images is demonstrated in the second part. A quantitative index of measuring the quality of classification/segmentation in terms of homogeneity of regions is introduced in this regard. Rough entropy (in granular computing framework) of images is defined and used for segmentation in the third part. In the fourth part a homogeneous region in an image is defined as a union of homogeneous line segments for image segmentation. Here Hough transform is used to generate these line segments. Comparative study is also made with related techniques.

An Algorithm for 3D Image Segmentation

Abstract: We propose a new 3D image segmentation method based on prediction, block-matching and partial 3D constraint in this paper. The algorithm only needs to set a few key points in the first image. We use intensity information and block-matching to optimize the initial condition, and consider the 3D object's characteristics at the same time. By using the partial 3D constraints we can get the result of 2D and 3D smoothness. Experimental results validate its usefulness in 3D image segmentation.

Robust Image Segmentation Using Resampling and Shape Constraints

Abstract: Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. We propose an integrated approach for image segmentation based on a generative clustering model combined with coarse shape information and robust parameter estimation. The sensitivity of segmentation solutions to image variations is measured by image resampling. Shape information is included in the inference process to guide ambiguous groupings of color and texture features. Shape and similarity-based grouping information is combined into a semantic likelihood map in the framework of Bayesian statistics. Experimental evidence shows that semantically meaningful segments are inferred even when image data alone gives rise to ambiguous segmentations.

Planar feature extraction in terrestrial laser scans using gradient based range image segmentation

Abstract: The paper presents a new segmentation algorithm, to be applied to terrestrial lasers scans of urban environments. The algorithm works directly in a range image. This is the fundamental laser scan data structure as a laser recording can be regarded as a 2dimensional grid of range measurements. The horizontal and vertical axes of the grid denote the horizontal and vertical angles at which the scanner emits the laser beam, receives the reflections, and measures the distance (the range) between the instrument and the reflecting surface at those angles. The presented algorithm estimates for each measurement (pixel) in the range image three parameters of the 3D plane that contains the pixel: two angles (horizontal and vertical) and the distance between the plane and the origin. The estimates are based on the scan angles (the horizontal and vertical angles at which the laser beam was emitted from the scanner) and the image gradients, i.e. the rate of change in the distance that is observed between adjacent measurements. Since the three estimated parameters characterize a plane in 3D space, regions of adjacent pixels with similar parameter values are likely to be part of the same plane. Such pixels are grouped into segments by a region-growing image segmentation step, which takes the three parameters into account simultaneously. The overall algorithm uses two complementary strategies to deal with the measurement noise affecting the gradients, during the gradient calculation and the region growing steps respectively.

Dynamic range compensation by filter cascade

Abstract: A local method uses a cascade of multiple nonlinear edge preserving filters, and nonlinear pixel point operations, to calculate the pixel gain. Multiple low-pass filters are used, being applied to surrounding neighborhoods of the current pixel of narrow and of wide extent. The number of filter stages may be determined based on the image content. The coefficients used to combine a gray level image extracted from the input image with the ascending scale regions can be automatically extracted from high-pass filtered images of the ascending scale regions. Multiplying each color component of the input image by one or more pixel dependent gain or attenuation factors, using a nonlinear mapping function that can lighten shadow regions as well as darken bright regions, generates the output image.

Detecting Redeye Defects in Digital Images

Abstract: A method for detecting a redeye defect in a digital image containing an eye comprises converting the digital image into an intensity image, and segmenting the intensity image into segments each having a local intensity maximum. Separately, the original digital image is thresholded to identify regions of relatively high intensity and a size falling within a predetermined range. Of these, a region is selected having substantially the highest average intensity, and those segments from the segmentation of the intensity image whose maxima are located in the selected region are identified.

Review of satellite image segmentation for an optimal fusion system based on the edge and region approaches

Abstract: Summary Image analysis, usually, refers to a process of images provided by a computer in order to find the objects within the image. Image segmentation is one of the most critical tasks in automatic image analysis. It consists of subdividing an image into its constituent parts as well as extracting them. A great variety of segmentation algorithms have been developed in the last few decades; but more of these algorithms can be really applied to all images. Some of them are not suitable for some particular situations, especially in satellite images which, often, contain different textured regions or varying background, and are often subjected to illumination changes or environmental effects. Searching for more precision, we propose, in this work a fusion of the edge approach with the region approach for satellite image segmentation. Indeed, this paper presents an overview on image fusion techniques applied to satellite image segmentation. The aim is to exploit the advantages of the two approaches in order to know closed contours and homogeneous areas for optimal image segmentation.

Computing the α-channel with probabilistic segmentation for image colorization

Abstract: We propose a gray scale image colorization method based on a Bayesian segmentation framework in which the classes are established from scribbles made by a user on the image. These scribbles can be considered as a multimap (multilabels map) that defines the boundary conditions of a probability measure field to be computed in each pixel. The components of such a probability measure field express the degree of belonging of each pixel to spatially smooth classes. In a first step we obtain the probability measure field by computing the global minima of a positive definite quadratic cost function with linear constraints. Then color is introduced in a second step through a pixelwise operation. The computed probabilities (memberships) are used for defining the weights of a simple linear combination of user provided colors associated to each class. An advantage of our method is that it allows us to re-colorize part or the whole image in an easy way, without need of recomputing the memberships (or /sp alpha/-channels).

Image processing for change detection

Abstract: Image data defining a reference image and each input image in a sequence of images is processed to detect changes in the images. A value is calculated for each pixel defining the spatial rate of change of homogeneity of the image data at that pixel. Different regions of pixels in each image are selected and the values within each region are concatenated to define a vector for each region. The vectors are then processed to compare corresponding regions in each image. The results of the comparison define a correlation map identifying areas in the images in which change has occurred.

Image analysis method, medium and apparatus and moving image segmentation system

Abstract: An image analysis method, medium and apparatus for segmentation of a moving image, and a moving image segmentation system. The image analysis method includes receiving an image signal representing an image, detecting features of the image by calculating a difference between the current frame of the image signal and its previous frame, analyzing the image signal based on the detected features of the image, and performing segmentation on the image signal according to the analysis result, thereby separately performing segmentation on all types of moving images. In other words, by using an appropriate segmentation method according to a feature of an image, effective segmentation can be achieved.

Method of image segmentation

Abstract: A method of segmenting a digital image comprising the steps of performing a preliminary segmentation of the image into sub objects, defining a model object by selecting sub objects that define the model object, providing sub-object and model object features, using a fuzzy logic inference system to calculate segmentation parameters based on at least one of the sub object and model object features, and performing segmentation of the image using the segmentation parameters.

Method for acquiring a digital image with a large dynamic range with a sensor of lesser dynamic range

Abstract: A method is provided for obtaining an image with a large dynamic range. An image is acquired such that each image pixel is represented by a plurality of values obtained at the same time but for different integration levels (effective exposures). For each pixel, a representative value is selected among those available, such that it is neither saturated nor blackened.