Showing papers on "Real image published in 2007"

Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization

Abstract: Endmember extraction is a process to identify the hidden pure source signals from the mixture. In the past decade, numerous algorithms have been proposed to perform this estimation. One commonly used assumption is the presence of pure pixels in the given image scene, which are detected to serve as endmembers. When such pixels are absent, the image is referred to as the highly mixed data, for which these algorithms at best can only return certain data points that are close to the real endmembers. To overcome this problem, we present a novel method without the pure-pixel assumption, referred to as the minimum volume constrained nonnegative matrix factorization (MVC-NMF), for unsupervised endmember extraction from highly mixed image data. Two important facts are exploited: First, the spectral data are nonnegative; second, the simplex volume determined by the endmembers is the minimum among all possible simplexes that circumscribe the data scatter space. The proposed method takes advantage of the fast convergence of NMF schemes, and at the same time eliminates the pure-pixel assumption. The experimental results based on a set of synthetic mixtures and a real image scene demonstrate that the proposed method outperforms several other advanced endmember detection approaches

Fractional-Order Anisotropic Diffusion for Image Denoising

Abstract: This paper introduces a new class of fractional-order anisotropic diffusion equations for noise removal. These equations are Euler-Lagrange equations of a cost functional which is an increasing function of the absolute value of the fractional derivative of the image intensity function, so the proposed equations can be seen as generalizations of second-order and fourth-order anisotropic diffusion equations. We use the discrete Fourier transform to implement the numerical algorithm and give an iterative scheme in the frequency domain. It is one important aspect of the algorithm that it considers the input image as a periodic image. To overcome this problem, we use a folded algorithm by extending the image symmetrically about its borders. Finally, we list various numerical results on denoising real images. Experiments show that the proposed fractional-order anisotropic diffusion equations yield good visual effects and better signal-to-noise ratio.

Computer vision based touch screen

Abstract: A self-contained interactive video display system. A projector projects a visual image onto a screen for displaying the visual image, wherein the projector projects the visual image onto a back side of the screen for presentation to a user on a front side of the screen. An illuminator illuminates an object near the front side of the screen. A camera detects interaction of an illuminated object with the visual image, wherein the screen is at least partially transparent to light detectable to the camera, allowing the camera to detect the illuminated object through the screen. A computer system directs the projector to change the visual image in response to the interaction.

Interactive video window

Abstract: An interactive video window display system. A projector projects a visual image. A screen displays the visual image, wherein the projector projects the visual image onto a back side of the screen for presentation to a user on a front side of the screen, and wherein the screen is adjacent to a window. An illuminator illuminates an object on a front side of the window. A camera detects interaction of an illuminated object with the visual image, wherein the screen is at least partially transparent to light detectable by the camera, allowing the camera to detect the illuminated object through the screen. A computer system directs the projector to change the visual image in response to the interaction. The projector, the camera, the illuminator, and the computer system are located on the same side of the window.

Camera using multiple lenses and image sensors in a rangefinder configuration to provide a range map

Abstract: An electronic camera for producing an output image of a scene from a captured image signal includes a first imaging stage comprising a first image sensor for generating a first sensor output and a first lens for forming a first image of the scene on the first image sensor, and a second imaging stage comprising a second image sensor for generating a second sensor output and a second lens for forming a second image of the scene on the second image sensor, where the lenses have different focal lengths. A processing stage uses the sensor output from one of the imaging stages as the captured image signal and uses the images from both imaging stages to generate a range map identifying distances to the different portions of the scene.

Fundamentals of multiframe particle image velocimetry (PIV)

Abstract: A sophisticated strategy for the evaluation of time-resolved PIV image sequences is presented which takes the temporal variation of the particle image pattern into account. The primary aim of the method is to increase the accuracy and dynamic range by locally adopting the particle image displacement for each interrogation window to overcome the largest drawback of PIV. This is required in order to resolve flow phenomena which have so far remained inaccessible. The method locally optimizes the temporal separation between the particle image pairs by taking first and second order effects into account. The validation of the evaluation method is performed with synthetically generated particle image sequences based on the solution of a direct numerical simulation. In addition, the performance of the evaluation approach is demonstrated by means of a real image sequence measured with a time-resolved PIV system.

Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy

Abstract: In this paper we present a new approach for the non-rigid registration of multi-modality images. Our approach is based on an information theoretic measure called the cumulative residual entropy (CRE), which is a measure of entropy defined using cumulative distributions. Cross-CRE between two images to be registered is defined and maximized over the space of smooth and unknown non-rigid transformations. For efficient and robust computation of the non-rigid deformations, a tri-cubic B-spline based representation of the deformation function is used. The key strengths of combining CCRE with the tri-cubic B-spline representation in addressing the non-rigid registration problem are that, not only do we achieve the robustness due to the nature of the CCRE measure, we also achieve computational efficiency in estimating the non-rigid registration. The salient features of our algorithm are: (i) it accommodates images to be registered of varying contrast+brightness, (ii) faster convergence speed compared to other information theory-based measures used for non-rigid registration in literature, (iii) analytic computation of the gradient of CCRE with respect to the non-rigid registration parameters to achieve efficient and accurate registration, (iv) it is well suited for situations where the source and the target images have field of views with large non-overlapping regions. We demonstrate these strengths via experiments on synthesized and real image data.

A Hierarchical Approach for Fast and Robust Ellipse Extraction

Abstract: This paper presents a hierarchical approach for fast and robust ellipse extraction from images. At the lowest level, the image is described as a set of edge pixels, from which line segments are extracted. Then, line segments that are potential candidates of elliptic arcs are linked to form arc segments according to connectivity and curvature relations. After that, arc segments that belong to the same ellipse are grouped together. Finally, a robust statistical method, namely RANSAC, is applied to fit ellipses. This method does not need a high dimensional parameter space like Hough transform based algorithms, and so it reduces the computation and memory requirements. Experiments on both synthetic and real images demonstrate that the proposed method has excellent performance in handling occlusion and overlapping ellipses.

Projector type display apparatus

Abstract: There is provided a safe image display apparatus for projecting an image on a screen that can minimize a stimulus to eyes, retinae and optic nerves from projected light entering into the eyes directly. By comparing a displayed image captured by a camera with an input image signal, a changed area is detected as a difference area and, then, a projected image signal is generated wherein a picture area image signal in the input image signal that corresponds to this difference area is masked by a black signal. The obtained projected image signal is projected onto the screen.

Topology-Preserving Tissue Classification of Magnetic Resonance Brain Images

Abstract: This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations

Fast Crowd Segmentation Using Shape Indexing

Abstract: This paper presents a fast, accurate, and novel method for the problem of estimating the number of humans and their positions from background differenced images obtained from a single camera where inter-human occlusion is significant. The problem is challenging firstly because the state space formed by the number, positions, and articulations of people is large. Secondly, in spite of many advances in background maintenance and change detection, background differencing remains a noisy and imprecise process, and its output is far from ideal: holes, fill-ins, irregular boundaries etc. pose additional challenges for our "mid- level" problem of segmenting it to localize humans. We propose a novel example-based algorithm which maps the global shape feature by Fourier descriptors to various configurations of humans directly. We use locally weighted averaging to interpolate for the best possible candidate configuration. The inherent ambiguity resulting from the lack of depth and layer information in the background difference images is mitigated by the use of dynamic programming, which finds the trajectory in state space that best explains the evolution of the projected shapes. The key components of our solution are simple and fast. We demonstrate the accuracy and speed of our approach on real image sequences.

A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation

Abstract: In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model parameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data log-likelihood, where the penalty term is data-dependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are "smoothed" over pixels between each E- and M-step by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markov-based methods, and is in general faster

Dynamic Visual Tracking for Manipulators Using an Uncalibrated Fixed Camera

Abstract: This paper presents a new controller for controlling a number of feature points on a robot manipulator to trace desired trajectories specified on the image plane of a fixed camera. It is assumed that the intrinsic and extrinsic parameters of the camera are not calibrated. A new adaptive algorithm is developed to estimate the unknown parameters online, based on three original ideas. First, we use the pseudoinverse of the depth-independent interaction matrix to map the image errors onto the joint space of the manipulator. By eliminating the depths in the interaction matrix, we can linearly parameterize the closed-loop dynamics of the manipulator. Second, to guarantee the existence of the pseudoinverse, the adaptive algorithm introduces a potential force to drive the estimated parameters away from the values that result in a singular Jacobian matrix. Third, to ensure that the estimated parameters are convergent to their true values up to a scale, we combine the Slotine-Li method with an online algorithm for minimizing the error between the estimated projections and real image coordinates of the feature points. We have proved asymptotic convergence of the image errors to zero by the Lyapunov theory based on the nonlinear robot dynamics. Experiments have been carried out to verify the performance of the proposed controller.

Analysis of weighting windows for image deformation methods in PIV

Abstract: The use of a weighting window (WW) in the evaluation of the cross-correlation coefficient and in the iterative procedure of image deformation method for particle image velocimetry (PIV) applications can be used to both stabilise the process and to increase the spatial resolution. The choice of the WW is a parameter that influences the complete PIV algorithm. Aim of this paper is to examine the influence of this aspect on both the accuracy and spatial resolution of the PIV algorithm. Results show an overall accordance between the theoretical approach and the simulation both with synthetic and real images. The choice of the combination of WW influences significantly the spatial resolution and accuracy of the PIV algorithm.

Generating images combining real and virtual images

Abstract: A method for combining a real space image (205) with a virtual image, includes: causing an imaging unit to capture an image of a real space; generating an image covering a predetermined space based on a plurality of real images in the captured real space; extracting position information of a light source based on the generated image; and adding a light source or a shadow on the virtual image based on the extracted position information of the light source.

Light field microscope with lenslet array

Abstract: A light field microscope incorporating a lenslet array at or near the rear aperture of the objective lens. The microscope objective lens may be supplemented with an array of low power lenslets which may be located at or near the rear aperture of the objective lens, and which slightly modify the objective lens. The result is a new type of objective lens, or an addition to existing objective lenses. The lenslet array may include, for example, 9 to 100 lenslets (small, low-power lenses with long focal lengths) that generate a corresponding number of real images. Each lenslet creates a real image on the image plane, and each image corresponds to a different viewpoint or direction of the specimen. Angular information is recorded in relations or differences among the captured real images. To retrieve this angular information, one or more of various dense correspondence techniques may be used.

Compound eye digital camera

Abstract: An image management method which creates and records two or more real images and two or more thumbnail images from two or more images by viewpoints photographed by two or more image pickup devices corresponding to the viewpoints, wherein the real images include a stereoscopic image including the images by viewpoints, a common image range cut from the images by viewpoints and a whole image synthesized from the images by viewpoints, and the thumbnail images include two or more thumbnail images y each viewpoint corresponding to each of images by viewpoints, a 3D thumbnail image corresponding to the stereoscopic image, and a whole thumbnail image corresponding to the whole image.

New Features to Identify Computer Generated Images

Abstract: Discrimination of computer generated images from real images is becoming more and more important. In this paper, we propose the use of new features to distinguish computer generated images from real images. The proposed features are based on the differences in the acquisition process of images. More specifically, traces of demosaicking and chromatic aberration are used to differentiate computer generated images from digital camera images. It is observed that the former features perform very well on high quality images, whereas the latter features perform consistently across a wide range of compression values. The experimental results show that proposed features are capable of improving the accuracy of the state-of-the-art techniques.

Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression

Abstract: This article develops an evolutional fuzzy particle swarm optimization (FPSO) learning algorithm to self extract the near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy particle swarm optimization vector quantization (FPSOVQ) learning schemes, combined advantages of the adaptive fuzzy inference method (FIM), the simple VQ concept and the efficient particle swarm optimization (PSO), are considered at the same time to automatically create near optimum codebook to achieve the application of image compression. The FIM is known as a soft decision to measure the relational grade for a given sequence. In our research, the FIM is applied to determine the similar grade between the codebook and the original image patterns. In spite of popular usage of Linde–Buzo–Grey (LBG) algorithm, the powerful evolutional PSO learning algorithm is taken to optimize the fuzzy inference system, which is used to extract appropriate codebooks for compressing several input testing grey-level images. The proposed FPSOVQ learning scheme compared with LBG based VQ learning method is presented to demonstrate its great result in several real image compression examples.

A Fusion Scheme for Joint Retrieval of Urban Height Map and Classification From High-Resolution Interferometric SAR Images

Abstract: The retrieval of 3-D surface models of the Earth is a major issue of remote sensing. Some nice results have already been obtained at medium resolution with optical and radar imaging sensors. For instance, missions such as the Shuttle Radar Topography Mission (SRTM) or the SPOT HRS have provided accurate digital terrain models. The computation of a digital surface model (DSM) over urban areas is the new challenging issue. Since the recent improvements in radar image resolution, synthetic aperture radar (SAR) interferometry, which had already proved its efficiency at low resolution, has provided an accurate tool for urban 3-D monitoring. However, the complexity of urban areas and high-resolution SAR images prevents the straightforward computation of an accurate DSM. In this paper, an original high-level processing chain is proposed to solve this problem, and some results on real data are discussed. The processing chain includes three main steps, namely: (1) information extraction; (2) fusion; and (3) correction. Our main contribution addresses the merging step, where we aim at retrieving both a classification and a DSM while imposing minimal constraint on the building shapes. The joint derivation of height and class enables the introduction of more contextual information. As a consequence, more flexibility toward scene architecture is possible. First, the initial images (interferogram, amplitude, and coherence images) are converted into higher-level information mapping with different approaches (filtering, object recognition, or global classification). Second, these new images are merged into a Markovian framework to jointly retrieve an improved classification and a height map. Third, DSM and classification are improved by computing layover and shadow from the estimated DSM. Comparison between shadow/layover and classification allows some corrections. This paper mainly addresses the second step, while the two others are briefly explained and referred to already published papers. The results obtained on real images are compared to ground truth and indicate a very good accuracy in spite of limited image resolution. The major limit of DSM computation remains the initial spatial and altimetric resolutions that need to be made more precise

Edge Grouping Combining Boundary and Region Information

Abstract: This paper introduces a new edge-grouping method to detect perceptually salient structures in noisy images. Specifically, we define a new grouping cost function in a ratio form, where the numerator measures the boundary proximity of the resulting structure and the denominator measures the area of the resulting structure. This area term introduces a preference towards detecting larger-size structures and, therefore, makes the resulting edge grouping more robust to image noise. To find the optimal edge grouping with the minimum grouping cost, we develop a special graph model with two different kinds of edges and then reduce the grouping problem to finding a special kind of cycle in this graph with a minimum cost in ratio form. This optimal cycle-finding problem can be solved in polynomial time by a previously developed graph algorithm. We implement this edge-grouping method, test it on both synthetic data and real images, and compare its performance against several available edge-grouping and edge-linking methods. Furthermore, we discuss several extensions of the proposed method, including the incorporation of the well-known grouping cues of continuity and intensity homogeneity, introducing a factor to balance the contributions from the boundary and region information, and the prevention of detecting self-intersecting boundaries.

FPGA-based Real-time Optical Flow Algorithm Design and Implementation

Abstract: Optical flow algorithms are difficult to apply to robotic vision applications in practice because of their extremely high computational and frame rate requirements. In most cases, traditional general purpose processors and sequentially executed software cannot compute optical flow in real time. In this paper, a tensor-based optical flow algorithm is developed and implemented using field programmable gate array (FPGA) technology. The resulting algorithm is significantly more accurate than previously published FPGA results and was specifically developed to be implemented using a pipelined hardware structure. The design can process 640 × 480 images at 64 fps, which is fast enough for most real-time robot navigation applications. This design has low resource requirements, making it easier to fit into small embedded systems. Error analysis on a synthetic image sequence is given to show its effectiveness. The algorithm is also tested on a real image sequence to show its robustness and limitations. The resulting limitations are analyzed and an improved scheme is then proposed. It is then shown that the performance of the design could be substantially improved with sufficient hardware resources.

Moving Target Detection in the Presence of Parallax

Abstract: A method for detecting a moving target is disclosed that receives a plurality of images from at least one camera; receives a measurement of scale from one of a measurement device and a second camera; calculates the pose of the at least one camera over time based on the plurality of images and the measurement of scale; selects a reference image and an inspection image from the plurality of images of the at least one camera; and detects a moving target from the reference image and the inspection image based on the orientation of corresponding portions in the reference image and the inspection image relative to a location of an epipolar direction common to the reference image and the inspection image; and displays any detected moving target on a display. The measurement of scale can derived from a second camera or, for example, a wheel odometer. The method can also detect moving targets by combining the above epipolar method with a method based on changes in depth between the inspection image and the reference image and based on changes in flow between the inspection image and the reference image.

Resolving Objects at Higher Resolution from a Single Motion-blurred Image

Abstract: Motion blur can degrade the quality of images and is considered a nuisance for computer vision problems. In this paper, we show that motion blur can in-fact be used for increasing the resolution of a moving object. Our approach utilizes the information in a single motion-blurred image without any image priors or training images. As the blur size increases, the resolution of the moving object can be enhanced by a larger factor, albeit with a corresponding increase in reconstruction noise. Traditionally, motion deblurring and super-resolution have been ill-posed problems. Using a coded-exposure camera that preserves high spatial frequencies in the blurred image, we present a linear algorithm for the combined problem of deblurring and resolution enhancement and analyze the invertibility of the resulting linear system. We also show a method to selectively enhance the resolution of a narrow region of high-frequency features, when the resolution of the entire moving object cannot be increased due to small motion blur. Results on real images showing up to four times resolution enhancement are presented.

Single view motion tracking by depth and silhouette information

Abstract: In this work1a combination of depth and silhouette information is presented to track the motion of a human from a single view. Depth data is acquired from a Photonic Mixer Device (PMD), which measures the time-of-flight of light. Correspondences between the silhouette of the projected model and the real image are established in a novel way, that can handle cluttered non-static backgrounds. Pose is estimated by Nonlinear Least Squares, which handles the underlying dynamics of the kinematic chain directly. Analytic Jacobians allow pose estimation with 5 FPS.

Penrose Pixels Super-Resolution in the Detector Layout Domain

Abstract: We present a novel approach to reconstruction based super- resolution that explicitly models the detector's pixel layout. Pixels in our model can vary in shape and size, and there may be gaps between adjacent pixels. Furthermore, their layout can be periodic as well as aperiodic, such as penrose tiling or a biological retina. We also present a new variant of the well known error back-projection super-resolution algorithm that makes use of the exact detector model in its back projection operator for better accuracy. Our method can be applied equally well to either periodic or aperiodic pixel tiling. Through analysis and extensive testing using synthetic and real images, we show that our approach outperforms existing reconstruction based algorithms for regular pixel arrays. We obtain significantly better results using aperiodic pixel layouts. As an interesting example, we apply our method to a retina-like pixel structure modeled by a centroidal Voronoi tessellation. We demonstrate that, in principle, this structure is better for super-resolution than the regular pixel array used in today's sensors.

Image Inpainting Using a TV-Stokes Equation

Abstract: Based on some geometrical considerations, we propose a two-step method to do digital image inpainting. In the first step, we try to propagate the isophote directions into the inpainting domain. An energy minimization model combined with the zero divergence condition is used to get a nonlinear Stokes equation. Once the isophote directions are constructed, an image is restored to fit the constructed directions. Both steps reduce to the solving of some nonlinear partial differential equations. Details about the discretization and implementation are explained. The algorithms have been intensively tested on synthetic and real images. The advantages of the proposed methods are demonstrated by these experiments.