Showing papers on "Pixel published in 2006"
TL;DR: A novel method is proposed for performing multilabel, interactive image segmentation using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs.
Abstract: A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs
2,610 citations
TL;DR: It is proved analytically and shown experimentally that the peak signal-to-noise ratio of the marked image generated by this method versus the original image is guaranteed to be above 48 dB, which is much higher than that of all reversible data hiding techniques reported in the literature.
Abstract: A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.
2,240 citations
17 Jun 2006
TL;DR: A novel algorithm for tracking an object in a video sequence represented by multiple image fragments or patches, which is able to handle partial occlusions or pose change and overcomes several difficulties which cannot be handled by traditional histogram-based algorithms.
Abstract: We present a novel algorithm (which we call "Frag- Track") for tracking an object in a video sequence. The template object is represented by multiple image fragments or patches. The patches are arbitrary and are not based on an object model (in contrast with traditional use of modelbased parts e.g. limbs and torso in human tracking). Every patch votes on the possible positions and scales of the object in the current frame, by comparing its histogram with the corresponding image patch histogram. We then minimize a robust statistic in order to combine the vote maps of the multiple patches. A key tool enabling the application of our algorithm to tracking is the integral histogram data structure [18]. Its use allows to extract histograms of multiple rectangular regions in the image in a very efficient manner. Our algorithm overcomes several difficulties which cannot be handled by traditional histogram-based algorithms [8, 6]. First, by robustly combining multiple patch votes, we are able to handle partial occlusions or pose change. Second, the geometric relations between the template patches allow us to take into account the spatial distribution of the pixel intensities - information which is lost in traditional histogram-based algorithms. Third, as noted by [18], tracking large targets has the same computational cost as tracking small targets. We present extensive experimental results on challenging sequences, which demonstrate the robust tracking achieved by our algorithm (even with the use of only gray-scale (noncolor) information).
1,522 citations
TL;DR: This work presents recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel and presents a simple non-parametric adaptive density estimation method.
1,483 citations
TL;DR: The objective is to enhance texture quality as much as possible with a minor sacrifice in efficiency in order to support the conjecture that the pixel-based approach would yield high quality images.
1,462 citations
TL;DR: In this paper, a method for automated segmentation of the vasculature in retinal images is presented, which produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector.
Abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods
1,435 citations
TL;DR: A novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence that provides many advantages compared to the state-of-the-art.
Abstract: This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.
1,355 citations
TL;DR: This paper presents a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering and yields regions more homogeneous than those of other methods.
1,296 citations
TL;DR: A new window-based method for correspondence search using varying support-weights based on color similarity and geometric proximity to reduce the image ambiguity and outperforms other local methods on standard stereo benchmarks.
Abstract: We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks.
1,267 citations
Patent•
09 Mar 2006
TL;DR: In this paper, the authors proposed to decrease the number of transistors and wires constituting a pixel to increase the aperture ratio, which increases the luminosity obtained from one pixel by increasing the area of a light emitting region in the pixel.
Abstract: When light emitting elements have the same luminance, luminosity obtained from one pixel can be higher as area of a light emitting region in the pixel (also called an aperture ration) is increased. The aperture ratio of a pixel is low if the number of transistors and wires constituting the pixel is large. Thus, the invention is to decrease the number of transistors and wires constituting a pixel to increase the aperture ratio. Instead of a power supply line to which a certain potential is set, a potential supply line which controls a potential by a signal is provided; supplying an applied voltage to a light emitting element can be controlled by a signal of the potential supply line without providing a switch.
1,061 citations
TL;DR: The proposed modification to the least-significant-bit (LSB) matching, a steganographic method for embedding message bits into a still image, shows better performance than traditional LSB matching in terms of distortion and resistance against existing steganalysis.
Abstract: This letter proposes a modification to the least-significant-bit (LSB) matching, a steganographic method for embedding message bits into a still image. In the LSB matching, the choice of whether to add or subtract one from the cover image pixel is random. The new method uses the choice to set a binary function of two cover pixels to the desired value. The embedding is performed using a pair of pixels as a unit, where the LSB of the first pixel carries one bit of information, and a function of the two pixel values carries another bit of information. Therefore, the modified method allows embedding the same payload as LSB matching but with fewer changes to the cover image. The experimental results of the proposed method show better performance than traditional LSB matching in terms of distortion and resistance against existing steganalysis.
TL;DR: An automated method for the segmentation of the vascular network in retinal images that outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity is presented.
Abstract: This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity
17 Jun 2006
TL;DR: A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
Abstract: Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper we present a closed form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors, and show that in the resulting expression it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high quality mattes can be obtained on natural images from a surprisingly small amount of user input.
01 Jan 2006
TL;DR: This work shows that when applied to human faces, the constrained local model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters.
Abstract: We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the Active Appearance Model due to Cootes et al. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our constrained local model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.
TL;DR: A general presentation of the extraction of displacement fields from the knowledge of pictures taken at different instants of an experiment is given, and different strategies can be followed to achieve a sub-pixel uncertainty.
Abstract: The current development of digital image correlation, whose displacement uncertainty is well below the pixel value, enables one to better characterise the behaviour of materials and the response of structures to external loads. A general presentation of the extraction of displacement fields from the knowledge of pictures taken at different instants of an experiment is given. Different strategies can be followed to achieve a sub-pixel uncertainty. From these measurements, new identification procedures are devised making use of full-field measures. A priori or a posteriori routes can be followed. They are illustrated on the analysis of a Brazilian test.
TL;DR: In this paper, the authors proposed a method for estimating the parameters of the models based on a minimization of the mean absolute error between the color measurements obtained by the models, and by a commercial colorimeter for uniform and homogenous surfaces.
TL;DR: This paper discusses empirical and analytical rules to select a suitable grid resolution for output maps and based on the inherent properties of the input data to derive the true optimal grid resolution that maximizes the predictive capabilities or information content of a map.
TL;DR: A new camera architecture is developed that employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns that can be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.
Abstract: Compressive Sensingis an emerging field based on the revelation that a small numbe r of linear projections of a compressible signal contain enough information for reconstruction and processing. It has many promising implications and enables the design of new kinds of Compressive Imagingsystems and cameras. In this paper, we develop a new camera architecture that employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtai n an image with a single detection element while sampling the image fewer times than the number of pixels. Other attractive properties include its universality, robustness, scalab ility, progressivity, and computational asymmetry. The most intriguing feature of the system is that, since it relies on a sing le photon detector, it can be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.
TL;DR: This paper shows how to recover a 3D, full color shadow-free image representation by first (with the help of the 2D representation) identifying shadow edges and proposing a method to reintegrate this thresholded edge map, thus deriving the sought-after 3D shadow- free image.
Abstract: This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequence, images represented in this form are shadow-free. We then extend this 1D representation to an equivalent 2D, chromaticity representation. We show that in this 2D representation, it is possible to relight all the image pixels in the same way, effectively deriving a 2D image representation which is additionally shadow-free. Finally, we show how to recover a 3D, full color shadow-free image representation by first (with the help of the 2D representation) identifying shadow edges. We then remove shadow edges from the edge-map of the original image by edge in-painting and we propose a method to reintegrate this thresholded edge map, thus deriving the sought-after 3D shadow-free image.
TL;DR: A novel method of steganographic embedding in digital images is described, in which each secret digit in a (2n+1)-ary notational system is carried by n cover pixels and, at most, only one pixel is increased or decreased by 1.
Abstract: A novel method of steganographic embedding in digital images is described, in which each secret digit in a (2n+1)-ary notational system is carried by n cover pixels and, at most, only one pixel is increased or decreased by 1. In other words, the (2n+1) different ways of modification to the cover pixels correspond to (2n+1) possible values of a secret digit. Because the directions of' modification are fully exploited, the proposed method provides high embedding efficiency that is better than previous techniques
TL;DR: High detection accuracy and overall Kappa were achieved by OB-Reflectance method in temperate forests using three SPOT-HRV images covering a 10-year period.
TL;DR: A novel adaptive and patch-based approach is proposed for image denoising and representation based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel to associate with each pixel the weighted sum of data points within an adaptive neighborhood.
Abstract: A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods
TL;DR: A detailed examination of the performances of each algorithm reveals that the iterative spatial domain cross-correlation algorithm (Newton–Raphson method) is more accurate, but much slower than other algorithms, and is recommended for use in these applications.
Abstract: Developments in digital image correlation in the last two decades have made it a popular and effective tool for full-field displacement and strain measurements in experimental mechanics In digital image correlation, the use of the sub-pixel registration algorithm is regarded as the key technique to improve accuracy Different types of sub-pixel registration algorithms have been developed However, little quantitative research has been carried out to compare their performances This paper investigates three types of the most commonly used sub-pixel displacement registration algorithms in terms of the registration accuracy and the computational efficiency using computer-simulated speckle images A detailed examination of the performances of each algorithm reveals that the iterative spatial domain cross-correlation algorithm (Newton–Raphson method) is more accurate, but much slower than other algorithms, and is recommended for use in these applications
TL;DR: In this paper, the authors review the characteristics of the HST imaging with the Advanced Camera for Surveys (ACS) and parallel observations with NICMOS and WFPC2.
Abstract: The Cosmic Evolution Survey (COSMOS) was initiated with an extensive allocation
(590 orbits in Cycles 12-13) using the Hubble Space Telescope (HST) for high resolution imaging. Here we review the characteristics of the HST imaging with the Advanced Camera for Surveys
(ACS) and parallel observations with NICMOS and WFPC2. A square field (1.8$\sq$\deg) has been imaged with single-orbit ACS I-F814W exposures with 50% completeness for sources 0.5\arcsec in diameter at I$_{AB} $ = 26.0 mag. The ACS imaging is a key part of the COSMOS survey, providing very high sensitivity and high resolution (0.09\arcsec FWHM, 0.05\arcsec pixels) imaging and detecting 1.2 million objects to a limiting magnitude of 26.5 (AB). These images yield resolved morphologies for several hundred thousand galaxies. The small HST PSF also provides greatly enhanced sensitivity for weak lensing investigations of the dark matter distribution.
TL;DR: A comparison is made between three different speckle patterns originated by the same referenceSpeckle pattern, and it is shown that the size of the speckles combined with thesize of the used pixel subset clearly influences the accuracy of the measured displacements.
01 Dec 2006
TL;DR: This paper proposes algorithms and hardware to support a new theory of compressive imaging based on a new digital image/video camera that directly acquires random projections of the signal without first collecting the pixels/voxels.
Abstract: Compressive Sensing is an emerging field based on the revelation that a small group of non-adaptive linear projections of a compressible signal contains enough information for reconstruction and processing. In this paper, we propose algorithms and hardware to support a new theory of Compressive Imaging. Our approach is based on a new digital image/video camera that directly acquires random projections of the signal without first collecting the pixels/voxels. Our camera architecture employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudo-random binary patterns. Its hallmarks include the ability to obtain an image with a single detection element while measuring the image/video fewer times than the number of pixels ? this can significantly reduce the computation required for video acquisition/encoding. Because our system relies on a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. We are currently testing a proto-type design for the camera and include experimental results.
TL;DR: The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object‐oriented image analysis approach gave a much higher accuracy than that obtained using the pixel‐based approach.
Abstract: Pixel‐based and object‐oriented classifications were tested for land‐cover mapping in a coal fire area. In pixel‐based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object‐oriented classification, a region‐growing multi‐resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land‐cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object‐oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel‐based classification by 36.77%, and the user’s and producer’s ac...
CERN1
TL;DR: In this article, a pixel detector readout chip was developed with a new front-end architecture aimed at eliminating the spectral distortion produced by charge diffusion in highly segmented semiconductor detectors.
Abstract: A prototype pixel detector readout chip has been developed with a new front-end architecture aimed at eliminating the spectral distortion produced by charge diffusion in highly segmented semiconductor detectors. In the new architecture neighbouring pixels communicate with one another. At the corner of each pixel summing circuits add the total charge deposited in each sub-group of 4 pixels. Arbitration logic assigns a hit to the summing circuit with the highest charge. In the case where incoming X-ray photons produce fluorescence-a particular issue in high-Z materials-the charge deposited by those fluorescent photons will be included in the charge sum provided that the deposition takes place within the volume of the pixels neighbouring the initial impact point. The chip is configurable such that either the dimensions of each detector pixel match those of one readout pixel or detector pixels are 4 times greater in area than the readout pixels. In the latter case event-by-event summing is still possible between the larger pixels. As well as this innovative analog front-end circuit, each pixel contains comparators, logic circuits and two 15-bit counters. When the larger detector pixels are used these counters can be configured to permit multiple thresholds in a pixel providing spectroscopic information. The prototype chip has been designed and manufactured in an 8-metal 0.13 mum CMOS technology. First measurements show an electronic pixel noise of ~ 72 e-rms (Single Pixel Mode) and ~ 140 e-rms (Charge Summing Mode).
TL;DR: This study proposed the vision-based system which remotely measures dynamic displacement of bridges in real-time using digital image processing techniques, which has a number of innovative features including a high resolution in dynamic measurement, remote sensing, cost-effectiveness, real- time measurement and visualization, ease of installation and operation and no electro-magnetic interference.
Abstract: This study proposed the vision-based system which remotely measures dynamic displacement of bridges in real-time using digital image processing techniques. This system has a number of innovative features including a high resolution in dynamic measurement, remote sensing, cost-effectiveness, real-time measurement and visualization, ease of installation and operation and no electro-magnetic interference. The digital video camera combined with a telescopic device takes a motion picture of the target installed on a measurement location. Meanwhile, the displacement of the target is calculated using an image processing technique, which requires a target recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and number of pixels moved. For the purpose of verification, a laboratory test using shaking table test and field application on a bridge with open-box girders were carried out. The test results gave sufficient dynamic resolution in frequency as well as the amplitude.
TL;DR: A multi-point filtersim algorithm is proposed that classifies structural patterns using selected filter statistics that represent directional mean, gradient, and curvature properties in multiple-point simulation.
Abstract: Multiple-point simulation, as opposed to simulation one point at a time, operates at the pattern level using a priori structural information. To reduce the dimensionality of the space of patterns we propose a multi-point filtersim algorithm that classifies structural patterns using selected filter statistics. The pattern filter statistics are specific linear combinations of pattern pixel values that represent directional mean, gradient, and curvature properties. Simulation proceeds by sampling from pattern classes selected by conditioning data.