Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility is inadequate for continuous and automated tracking. In such applications, a sufficient overlap between FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. In addition, most of the existing algorithms employ discrete solution space, which suffers from limited solution accuracy and high computational complexity due to the high dimension of sampled solution space. In this paper, we propose to perform the optimisation of the camera parameters in a continuous solution space. In addition, to incorporate the optimisation of coverage and sufficient overlapped FOVs, a weighted sum approach is utilised to translate a multiple objective optimisation problem into a single objective optimisation process in our previous work. Significantly improved handoff success rates are illustrated via experiments using typical office floor plans in comparison with Erdem and Sclaroff's method. Improved accuracy, enhanced robustness, completeness of the solution set, and reduced computational complexity are accomplished in comparison with our previous algorithms.

In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.

Most existing performance evaluation methods concentrate on defining separate metrics over a wide range of conditions and generating standard benchmarking video sequences for examining the effectiveness of video tracking systems. In other words, these methods attempt to design a robustness margin or factor for the system. These methods are deterministic in which a robustness factor, for example, 2 or 3 times the expected number of subjects to track or the strength of illumination would be required in the design. This often results in over design, thus increasing costs, or under design causing failure by unanticipated factors. In order to overcome these limitations, we propose in this paper an alternative framework to analyze the physics of the failure process and, through the concept of reliability, determine the time to failure in automated video tracking systems. The benefit of our proposed framework is that we can provide a unified and statistical index to evaluate the performance of automated video tracking system for a task to be performed. At the same time, the uncertainty problem about a failure process, which may be caused by the system's complexity, imprecise measurements of the relevant physical constants and variables, or the indeterminate nature of future events, can be addressed accordingly based on our proposed framework.

In this paper, we present a new method to enhance depth images captured by a time-of-flight (TOF) depth sensor spatially and temporally. In practice, depth images obtained from TOF depth sensors have critical problems, such as optical noise existence, unmatched boundaries, and temporal inconsistency. In this work, we improve depth quality by performing a newly-designed joint bilateral filtering, color segmentation-based boundary refinement, and motion estimation-based temporal consistency. Experimental results show that the proposed method significantly minimizes the inherent problems of the depth images so that we can use them to generate a dynamic and realistic 3D scene.

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.

Due to the capacity of pan-tilt-zoom (PTZ) cameras to simultaneously cover a panoramic area and maintain high resolution imagery, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy, as compared with the work of Chen and Wang. This slightly decreased pixel accuracy can be compensated by consistent labeling approaches without added cost for the application of automated surveillance systems along with changing configurations and a larger number of PTZ cameras.

We propose a novel scene image segmentation algorithm based on Perceptual Organization. We develop a Perceptual Organization model by quantitatively incorporating a list of Gestalt laws. The Perceptual Organization model can capture the non-accidental structural relations among the constituent parts of an object. The experimental results show that our proposed method outperformed two competing image segmentation approaches and achieved good segmentation quality on various natural scene environments.

In this effort, a data-driven and application independent technique to combine focal information from different focal planes is presented. Input images, acquired by imaging systems with limited depth of field, are decomposed using a relatively new analysis tool called curvelets. The extracted curvelets are representative of polar `wedges' from the frequency domain. Fusion is performed on medial and peripheral curvelets by relevant fusion rules and the fused image combines information from different focal planes, while extending the depth of field of the scene. The main contribution in this effort is the direct use of curvelets in combining multifocal images. Several illustrative examples and objective comparisons are provided.

In order to achieve improved recognition performance in comparison with conventional broadband images, this paper addresses a new method that automatically specifies the optimal spectral range for multispectral face images according to given illuminations. The novelty of our method lies in the introduction of a distribution separation measure and the selection of the optimal spectral range by ranking these separation values. The selected spectral ranges are consistent with the physics analysis of the multispectral imaging process. The fused images from these chosen spectral ranges are verified to outperform the conventional broadband images by 3%-20%, based on a variety of experiments with indoor and outdoor illuminations using two well-recognized face-recognition engines. Our discovery can be practically used for a new customized sensor design associated with given illuminations for improved face-recognition performance over the conventional broadband images.

Camera handoff is a crucial step to generate a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are matched across different cameras. However, most real-time object tracking systems see a decrease in the system's frame rate as the number of tracked objects increases. To address this issue, we propose to incorporate an adaptive resource management mechanism into camera handoff. In so doing, cameras¿ resources can be dynamically allocated to multiple objects according to their priorities and hence the required minimum frame rate can be maintained. Experimental results illustrate that the proposed camera handoff algorithm is capable of maintaining a constant frame rate and of achieving a substantially improved handoff success rate by approximately 20% in comparison with the algorithm presented by Khan and Shah.

The estimation of the fundamental matrix is the key step in feature-based camera ego-motion estimation for applications in scene modeling and vehicle navigation. In this paper, we present a new method of analyzing and further reducing the risk in the fundamental matrix due to the choice of a particular feature detector, the choice of the matching algorithm, the motion model, iterative hypothesis generation and verification paradigms. Our scheme makes use of model-selection theory to guide the switch to optimal methods for fundamental matrix estimation within the hypothesis-and-test architecture. We demonstrate our proposed method for vision-based robot localization in large-scale environments where the environment is constantly changing and navigation within the environment is unpredictable.

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera—s field of view (FOV). However, visibility, a fundamental requirement of object tracking, is insufficient for persistent and automated tracking. In such applications, a continuous and consistently labeled trajectory of the same object should be maintained across different cameras’ views. Therefore, a sufficient overlap between the cameras’ FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. The proposed sensor planning method improves existing algorithms by adding handoff rate analysis, which preserves necessary overlapped FOVs for an optimal handoff success rate. In addition, special considerations such as resolution and frontal view requirements are addressed using two approaches: direct constraint and adaptive weight. The resulting camera placement is compared with a reference algorithm by Erdem and Sclaroff. Significantly improved handoff success rate and frontal view percentage are illustrated via experiments using typical office floor plans.

Multispectral imaging and fusion of multispectral images in visible spectrum have been employed and proved to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. In this paper, we further our study toward the recognition performance of a smaller number of spectral band images. Given the illumination, we propose an automated method that specifies the optimal spectral ranges to achieve an improved recognition performance compared with conventional broad-band images. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images.

In this paper, we propose a series of techniques to enhance the computational performance of existing Belief Propagation (BP) based stereo matching that relies on automatic estimation of the Markov random field (MRF) parameters. First, we show how convergence in matching can be achieved faster than with the existing message comparison technique by skipping comparisons in early inferences. Second, assuming that a stereo pair is captured with identical cameras, we apply a hypothesis called noise equivalence to pre-estimate the likelihood parameters and thus, avoid costly nested inferences to reduce the computational time. The likelihood parameters and intensity information are used for accelerated message propagation in image regions lacking gradients. Third, the prior model parameters are estimated with a combination of maximum likelihood (ML) estimation and disparity gradient constraint to further reduce the computational time. Supporting experiments for the proposed algorithms show encouraging results on ground truth test images.

In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from each of the 300 3D face mesh models. The feature-subsets that we infer as the output establishes domain knowledge for the challenging problem of 3D face recognition with dense 3D gallery models and sparse or low resolution probes.

Here, a versatile data-driven application independent method to extend the depth of field is presented. The principal contribution in this effort is the use of features extracted by empirical mode decomposition, namely Intrinsic Mode Images, for fusion. The input images are decomposed into intrinsic mode images and fusion is performed on the extracted oscillatory modes, by means of weighing schemes that allow emphasis of focused regions in each input image. The fused image unifies information from all focal planes, while maintaining the verisimilitude of the scene. In order to validate the fusion performance of our method, we have compared our results with those of region-based and multiscale decomposition based fusion techniques. Several illustrative examples and objective comparisons are provided.

Belief propagation methods are the state-of-the-art with multisensor state localization problems. However, when localization applications have to deal with multimodality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a framework inspired from model selection theory and demonstrate results on real world multisensor robot state localization and multicamera target tracking applications.

Most existing sensor planning algorithms find it difficult to tackle the discrepancy between a PTZ camera¿s limited instant field of view (FOV) and panoramic achievable FOV. In this paper, we introduce the probability of camera overload to resolve this discrepancy and present a sensor planning algorithm for PTZ cameras under the same framework as static cameras. The resulting camera placement achieves the optimal balance between coverage and handoff success rate. Furthermore, our algorithm is able to incorporate the target¿s dynamics into sensor planning. As a result, the system¿s handoff success rate can be maintained in environments with various target densities. Experimental results and comparisons with a reference algorithm proposed by Erdem and Sclaroff verify the effectiveness of our algorithm via a significantly improved handoff success rate.

Dual-camera systems have been widely used in surveillance because of the ability to explore the wide field of view (FOV) of the omnidirectional camera and the wide zoom range of the PTZ camera. Most existing algorithms require a priori knowledge of the omnidirectional camera's projection model to solve the nonlinear spatial correspondences between the two cameras. To overcome this limitation, two methods are proposed: 1) geometry and 2) homography calibration, where polynomials with automated model selection are used to approximate the camera's projection model and spatial mapping, respectively. The proposed methods not only improve the mapping accuracy by reducing its dependence on the knowledge of the projection model but also feature reduced computations and improved flexibility in adjusting to varying system configurations. Although the fusion of multiple cameras has attracted increasing attention, most existing algorithms assume comparable FOV and resolution levels among multiple cameras. Different FOV and resolution levels of the omnidirectional and PTZ cameras result in another critical issue in practical tracking applications. The omnidirectional camera is capable of multiple object tracking while the PTZ camera is able to track one individual target at one time to maintain the required resolution. It becomes necessary for the PTZ camera to distribute its observation time among multiple objects and visit them in sequence. Therefore, this paper addresses a novel scheme where an optimal visiting sequence of the PTZ camera is obtained so that in a given period of time the PTZ camera automatically visits multiple detected motions in a target-hopping manner. The effectiveness of the proposed algorithms is illustrated via extensive experiments using both synthetic and real tracking data and comparisons with two reference systems.

Multifocus fusion is the process of fusing focal information from a set of input images into one all-in-focus image. Here, a versatile multifocus fusion algorithm is presented for application-independent fusion. A focally connected region is a region or a set of regions in an input image that falls under the depth of field of the imaging system. Such regions are segmented adaptively under the predicate of focal connectivity and fused by partition synthesis. The fused image has information from all focal planes, while maintaining the visual verisimilitude of the scene. In order to validate the fusion performance of our method, we have compared our results with those of tiling and multiscale fusion techniques. In addition to performing a seamless fusion of the focally connected regions, our method out performs the competing methods regarding overall sharpness in all our experiments. Several illustrative examples of multifocus fusion are shown and objective comparisons are provided.

Multifocus fusion is the process of unifying focal information from a set of input images acquired with limited depth of field. In this effort, we present a general purpose multifocus fusion algorithm, which can be applied to varied applications ranging from microscopic to long range scenes. The main contribution in this paper is the segmentation of the input images into partitions based on focal connectivity. Focal connectivity is established by isolating regions in an input image that fall on the same focal plane. Our method uses focal connectivity and does not rely on physical properties like edges directly for segmentation. Our method establishes sharpness maps to the input images, which are used to isolate and attribute image partitions to input images. The partitions are mosaiced seamlessly to form the fused image. Illustrative examples of multifocus fusion using our method are shown. Comparisons against existing methods are made and the results are discussed.

Interest point detectors are the starting point in image analysis for depth estimation using epipolar geometry and camera ego-motion estimation. With several detectors defined in the literature, some of them outperforming others in a specific application context, we introduce multi-feature sample consensus (MuFeSaC) as an adaptive and automatic procedure to choose a reliable feature detector among competing ones. Our approach is derived based on model selection criteria that we demonstrate for mobile robot self-localization in outdoor environments consisting of both man-made structures and natural vegetation.

Our sensor selection algorithm targets the problem of global self-localization of multi-sensor mobile robots. The algorithm builds on the probabilistic reasoning using Bayes filters to estimate sensor measurement uncertainty and sensor validity in robot localization. For quantifying measurement uncertainty we score the Bayesian belief probability density using a model selection criterion, and for sensor validity, we evaluate belief on pose estimates from different sensors as a multi-sample clustering problem. The minimization of the combined uncertainty (measurement uncertainly score + sensor validity score) allows us to intelligently choose a subset of sensors that contribute to accurate localization of the mobile robot. We demonstrate the capability of our sensor selection algorithm in automatically switching pose recovery methods and ignoring non-functional sensors for localization on real-world mobile platforms equipped with laser scanners, vision cameras, and other hardware instrumentation for pose estimation.

We propose a simple nonparametric linear regression tool, known as kernel regression (KR), to estimate the illumination chromaticity. We design a Gaussian kernel whose bandwidth is selected empirically. Previously, nonlinear techniques like neural networks (NN) and support vector machines (SVM) are applied to estimate the illumination chromaticity. However, neither of the techniques was compared with linear regression tools. We show that the proposed method performs better chromaticity estimation compared to NN, SVM, and linear ridge regression (RR) approach on the same data set.

In this effort, we propose a new image fusion technique, utilizing empirical mode decomposition (EMD), for improved face recognition. EMD is a non-parametric data-driven analysis tool that decomposes non-linear non-stationary signals into intrinsic mode functions (IMFs). In this method, we decompose images from different imaging modalities into their IMFs. Fusion is performed at the decomposition level and the fused IMFs are reconstructed to form the fused image. The effect of fusion on face recognition is measured by obtaining the cumulative match characteristics (CMCs) between galleries and probes. Apart from conducting face recognition tests on visible and infrared raw datasets, we use datasets fused by averaging, principal component (PCA) fusion, wavelet based fusion and our method, for comparison. The face recognition rate due to EMD fused images is higher than the face recognition rates due to raw visible, raw infrared and other fused images. Examples of the fused images and illustrative CMC comparison charts are shown.

This paper presents a tactical path planning algorithm for following ridges or valleys across a 3D terrain. The intent is to generate a path that enables an unmanned vehicle to surveil with maximum observability by traversing the ridges of a terrain or to operate with maximum covertness by navigating the valleys. The input to the algorithm is a 3D triangle mesh model for the terrain of interest. This mesh may be non-uniform and non-regular. Thus, the algorithm leverages research from computer graphics and computer vision to identify ridge-valley features on the terrain. These features serve as "obstacles" for an artificial potential field algorithm. The valleys are obstacles for a surveillance path, or the ridges are obstacles for a covert path. We incorporate geodesic-rather than Euclidean-distances into the potential field formulation to extend path planning to 3D surfaces. We present the theory of our proposed algorithm and provide experimental results

In this paper we introduce a new 3D automatic registration method based on Gaussian fields and energy minimization. The method defines a simple C/sup /spl infin// energy function, which is convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. We show that the size of the region of convergence can be significantly extended reducing the need for close initialization and overcoming local convergence problems of the standard iterative closest point (ICP) algorithms. Furthermore, the Gaussian criterion can be evaluated with linear computational complexity using fast Gauss transform methods, allowing for an efficient implementation of the registration algorithm. Experimental analysis of the technique using real world datasets shows the usefulness as well as the limits of the approach.

We present a new multiresolution analysis framework for irregular meshes with attributes based on the lifting scheme. We introduce a surface prediction operator to compute the detail coefficients for the geometry and the attributes of the model. Attribute analysis gives appearance information to complete the geometrical analysis of the model. A set of experimental results are given to show the efficiency of our framework. We present two applications to adaptive visualization and denoising.

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

In this paper, we describe an algorithm called fast marching watersheds that segments a triangle mesh into visual parts. This computer vision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hill-climbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the proposed algorithm.

In this paper, we describe an algorithm to measure the shape complexity for discrete approximations of planar curves in 2D images and manifold surfaces for 3D triangle meshes. We base our algorithm on shape curvature, and thus we compute shape information as the entropy of curvature. We present definitions to estimate curvature for both discrete curves and surfaces and then formulate our theory of shape information from these definitions. We demonstrate our algorithm with experimental results.

This paper proposes a new, efficient surface representation method for surface matching. A feature carrier for a surface point, which is a set of two-dimensional (2-D) contours that are the projections of geodesic circles on the tangent plane, is generated. The carrier is named point fingerprint because its pattern is similar to human fingerprints and plays a role in discriminating surface points. Corresponding points on surfaces from different views are found by comparing their fingerprints. The point fingerprint is able to carry curvature, color, and other information which can improve matching accuracy, and the matching process is faster than 2-D image comparison. A novel candidate point selection method based on the fingerprint irregularity is introduced. Point fingerprint is successfully applied to pose estimation of real range data.

Tracking and recognizing non-rigid objects in video image sequences are complex tasks of increasing importance to many applications. In this paper, we present a hierarchical realization of an enhanced active shape model for color video tracking and we study the performance of hierarchical and non-hierarchical implementations in the RGB, YUV, and HSI color spaces.

We propose a novel algorithm to smooth and simplify simultaneously range images and also triangle meshes derived from those images. These data sets often suffer from noise and over-sampling. To overcome these issues, smoothing from image processing and simplification from computer graphics attempt to minimize noise and reduce complexity, respectively. Typically, these algorithms are separate and distinct steps, but we combine them into one algorithm. We employ surface normal voting to generate robust orientation estimates and then extend the quadric error metric framework to smooth noise while simplifying the surface. We demonstrate the capabilities of this algorithm with both synthetic and real data. The proposed algorithm provides significant noise smoothing improvement when compared to the standard Garland and Heckbert (1998) quadric simplification algorithm.

Triangle meshes are widely used in representing surfaces in computer vision and computer graphics. Although 2D image processing-based edge detection techniques have been popular in many application areas, they are not well developed for surfaces represented by triangle meshes. This paper proposes a robust edge detection algorithm for triangle meshes and its applications to surface segmentation and adaptive surface smoothing. The proposed edge detection technique is based on eigen analysis of the surface normal vector field in a geodesic window. To compute the edge strength of a certain vertex, the neighboring vertices in a specified geodesic distance are involved. Edge information are used further to segment the surfaces with the watershed algorithm and to achieve edge-preserved, adaptive surface smoothing. The proposed algorithm is novel in robustly detecting edges on triangle meshes against noise. The 3D watershed algorithm is an extension from previous work. Experimental results on surfaces reconstructed from multi-view real range images are presented.

This paper presents a simple and efficient algorithm for part decomposition of compound objects based on Gaussian curvature analysis. The proposed algorithm consists of three major steps, Gaussian curvature estimation, boundary detection, and region growing. Boundaries between two articulated parts are composed of points with highly negative curvature based on the transversality regularity. These boundaries are therefore detected by thresholding estimated Gaussian curvatures for each vertex. A component labeling operation is then performed to grow non-boundary vertices into parts. The original contributions of this paper include: (i) novel, curvature analysis-based decomposition of 3-D models represented by triangle meshes into functional parts instead of surfaces and (ii) large mesh (over 100,000 triangles) handling capability with low computational cost and easy implementation. Experiments were conducted on a large number of both synthetic and real 3-D models. Experimental results demonstrated the performance and efficiency of the proposed algorithm.

In this paper, we describe a robust method for the estimation of curvature on a triangle mesh, where this mesh is a discrete approximation of a piecewise smooth surface. The proposed method avoids the computationally expensive process of surface fitting and instead employs normal voting to achieve robust results. This method detects crease discontinuities on the surface to improve estimates near those creases. Using a voting scheme, the algorithm estimates both principal curvatures and principal directions for smooth patches. The entire process requires one user parameter-the voting neighborhood size, which is a function of sampling density, feature size, and measurement noise. We present results for both synthetic and real data and compare these results to an existing algorithm developed by Taubin (1995).

A new method is presented for the localization and recognition of three-dimensional objects using color information. In the first processing step, we estimate depth information by either applying a chromatic block matching method to color stereo images or acquiring a range image from a laser scanner. Second, the computed depth maps are segmented to distinguish between the image background and the objects that should be recognized. Assuming that the segmented regions represent single objects in the three-dimensional scene, feature vectors are generated based on color histograms. The Euclidean distance is used as well as the scalar product to measure the similarity between the feature vectors computed from the color image and the feature vectors stored in a database.

Stereo is a well-known technique for obtaining depth information from digital images. Nevertheless, this technique still suffers from a lack in accuracy and/or long computation time needed to match stereo images. A new hierarchical algorithm using an image pyramid for obtaining dense depth maps from color stereo images is presented. We show that matching results of high quality are obtained when using the new hierarchical chromatic block matching algorithm. Most stereo matching algorithms can not compute correct dense depth maps in homogenous image regions. This paper shows that using an active color illumination will considerably improve the quality of the matching results. We present results for synthetic and for real images.