Showing papers on "Pixel published in 2008"

Stereo Processing by Semiglobal Matching and Mutual Information

Abstract: This paper describes the semiglobal matching (SGM) stereo method. It uses a pixelwise, mutual information (Ml)-based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement, and multibaseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments, and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed. A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2 seconds on typical test images. An in depth evaluation of the Ml-based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.

A Closed-Form Solution to Natural Image Matting

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 for natural images may be obtained from a small amount of user input.

A 128 $\times$ 128 120 dB 15 $\mu$ s Latency Asynchronous Temporal Contrast Vision Sensor

Abstract: This paper describes a 128 times 128 pixel CMOS vision sensor. Each pixel independently and in continuous time quantizes local relative intensity changes to generate spike events. These events appear at the output of the sensor as an asynchronous stream of digital pixel addresses. These address-events signify scene reflectance change and have sub-millisecond timing precision. The output data rate depends on the dynamic content of the scene and is typically orders of magnitude lower than those of conventional frame-based imagers. By combining an active continuous-time front-end logarithmic photoreceptor with a self-timed switched-capacitor differencing circuit, the sensor achieves an array mismatch of 2.1% in relative intensity event threshold and a pixel bandwidth of 3 kHz under 1 klux scene illumination. Dynamic range is > 120 dB and chip power consumption is 23 mW. Event latency shows weak light dependency with a minimum of 15 mus at > 1 klux pixel illumination. The sensor is built in a 0.35 mum 4M2P process. It has 40times40 mum2 pixels with 9.4% fill factor. By providing high pixel bandwidth, wide dynamic range, and precisely timed sparse digital output, this silicon retina provides an attractive combination of characteristics for low-latency dynamic vision under uncontrolled illumination with low post-processing requirements.

Image super-resolution using gradient profile prior

Abstract: In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.

ATLAS pixel detector electronics and sensors

Abstract: The silicon pixel tracking system for the ATLAS experiment at the Large Hadron Collider is described and the performance requirements are summarized. Detailed descriptions of the pixel detector electronics and the silicon sensors are given. The design, fabrication, assembly and performance of the pixel detector modules are presented. Data obtained from test beams as well as studies using cosmic rays are also discussed.

PSF estimation using sharp edge prediction

Abstract: Image blur is caused by a number of factors such as motion, defocus, capturing light over the non-zero area of the aperture and pixel, the presence of anti-aliasing filters on a camera sensor, and limited sensor resolution We present an algorithm that estimates non-parametric, spatially-varying blur functions (ie, point-spread functions or PSFs) at subpixel resolution from a single image Our method handles blur due to defocus, slight camera motion, and inherent aspects of the imaging system Our algorithm can be used to measure blur due to limited sensor resolution by estimating a sub-pixel, super-resolved PSF even for in-focus images It operates by predicting a ldquosharprdquo version of a blurry input image and uses the two images to solve for a PSF We handle the cases where the scene content is unknown and also where a known printed calibration target is placed in the scene Our method is completely automatic, fast, and produces accurate results

Using contours to detect and localize junctions in natural images

Abstract: Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the intensity signal. However, a drawback of this strategy is that it confuses textured regions with junctions. We believe that the right approach to junction detection should take advantage of the contours that are incident at a junction; contours themselves can be detected by processes that use more global approaches. In this paper, we develop a new high-performance contour detector using a combination of local and global cues. This contour detector provides the best performance to date (F=0.70) on the Berkeley Segmentation Dataset (BSDS) benchmark. From the resulting contours, we detect and localize candidate junctions, taking into account both contour salience and geometric configuration. We show that improvements in our contour model lead to better junctions. Our contour and junction detectors both provide state of the art performance.

Multi-Class Segmentation with Relative Location Prior

Abstract: Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying "tree" pixels indicates that pixels above and to the sides are more likely to be "sky" whereas pixels below are more likely to be "grass." Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed. In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multi-class image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.

Floor Fields for Tracking in High Density Crowd Scenes

Abstract: This paper presents an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people. Tracking in such a scene is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The novel tracking algorithm, which is outlined in this paper, will overcome these challenges using a scene structure based force model. In this force model an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in the scene. The key ingredients of the force model are three floor fields, which are inspired by the research in the field of evacuation dynamics, namely Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of move from one location to another by converting the long-range forces into local ones. The SFF specifies regions of the scene which are attractive in nature (e.g. an exit location). The DFF specifies the immediate behavior of the crowd in the vicinity of the individual being tracked. The BFF specifies influences exhibited by the barriers in the scene (e.g. walls, no-go areas). By combining cues from all three fields with the available appearance information, we track individual targets in high density crowds.

Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems

Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.

Pixel-level Image Fusion using Wavelets and Principal Component Analysis

Abstract: Image registration and fusion are of great importance in defence and civilian sectors, e.g. , recognising a ground/air force vehicle and medical imaging. Pixel-level image fusion using wavelets and principal13; component analysis has been implemented and demonstrated in PC MATLAB. Different performance metrics with and without reference image are implemented to evaluate the performance of image fusion algorithms. As expected, the simple averaging fusion algorithm shows degraded13; performance. The ringing tone presented in the fused image can be avoided using wavelets with shift invariant property. It has been concluded that image fusion using wavelets with higher level of decomposition showed better performance in some metrics and in other metrics, principal components analysis showed better performance.13;

Superpixel lattices

Abstract: Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Ideally, every pixel within each superpixel region will belong to the same real-world object. Existing algorithms generate superpixels that forfeit many useful properties of the regular topology of the original pixels: for example, the nth superpixel has no consistent position or relationship with its neighbors. We propose a novel algorithm that produces superpixels that are forced to conform to a grid (a regular superpixel lattice). Despite this added topological constraint, our algorithm is comparable in terms of speed and accuracy to alternative segmentation approaches. To demonstrate this, we use evaluation metrics based on (i) image reconstruction (ii) comparison to human-segmented images and (iii) stability of segmentation over subsequent frames of video sequences.

Linear Time Maximally Stable Extremal Regions

Abstract: In this paper we present a new algorithm for computing Maximally Stable Extremal Regions (MSER), as invented by Matas et al. The standard algorithm makes use of a union-find data structure and takes quasi-linear time in the number of pixels. The new algorithm provides exactly identical results in true worst-case linear time. Moreover, the new algorithm uses significantly less memory and has better cache-locality, resulting in faster execution. Our CPU implementation performs twice as fast as a state-of-the-art FPGA implementation based on the standard algorithm. The new algorithm is based on a different computational ordering of the pixels, which is suggested by another immersion analogy than the one corresponding to the standard connected-component algorithm. With the new computational ordering, the pixels considered or visited at any point during computation consist of a single connected component of pixels in the image, resembling a flood-fill that adapts to the grey-level landscape. The computation only needs a priority queue of candidate pixels (the boundary of the single connected component), a single bit image masking visited pixels, and information for as many components as there are grey-levels in the image. This is substantially more compact in practice than the standard algorithm, where a large number of connected components must be considered in parallel. The new algorithm can also generate the component tree of the image in true linear time. The result shows that MSER detection is not tied to the union-find data structure, which may open more possibilities for parallelization.

A Novel Difference Expansion Transform for Reversible Data Embedding

Abstract: Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.

Reading apparatus having partial frame operating mode

Abstract: A method for decoding a decodable symbol using an optical reader having a 2D image sensor that is configured to operate in a partial frame capture operating mode. In a partial frame operating mode, the reader clocks out and captures at least one partial frame of image data having image data corresponding to less than all of the pixels of an image sensor pixel array. In one embodiment, the reader operating in a partial frame operating mode captures image data corresponding to a linear pattern of pixels of the image sensor, reads the image data, and attempts to decode for a decodable bar code symbol which may be represented in the image data.

Indicia reading terminal having plurality of optical assemblies

Abstract: There is described an indicia reading terminal having an image sensor array including a plurality of pixels, a first optical assembly for focusing imaging light rays onto a first set of pixels of an image sensor array and a second optical assembly for focusing imaging light rays onto a second set of pixels of the image sensor array. The indicia reading terminal can be adapted to process image data corresponding to pixels of the image sensor array for attempting to decode a decodable indicia.

A region based stereo matching algorithm using cooperative optimization

Abstract: This paper presents a new stereo matching algorithm based on inter-regional cooperative optimization. The proposed algorithm uses regions as matching primitives and defines the corresponding region energy functional for matching by utilizing the color statistics of regions and the constraints on smoothness and occlusion between adjacent regions. In order to obtain a more reasonable disparity map, a cooperative optimization procedure has been employed to minimize the matching costs of all regions by introducing the cooperative and competitive mechanism between regions. Firstly, a color based segmentation method is used to segment the reference image into regions with homogeneous color. Secondly, a local window-based matching method is used to determine the initial disparity estimate of each image pixel. And then, a voting based plane fitting technique is applied to obtain the parameters of disparity plane corresponding to each image region. Finally, the disparity plane parameters of all regions are iteratively optimized by an inter-regional cooperative optimization procedure until a reasonable disparity map is obtained. The experimental results on Middlebury test set and real stereo images indicate that the performance of our method is competitive with the best stereo matching algorithms and the disparity maps recovered are close to the ground truth data.

Measuring fiber alignment in electrospun scaffolds: a user's guide to the 2D fast Fourier transform approach

Abstract: In this study we describe how to use a two-dimensional fast Fourier transform (2D FFT) approach to measure fiber alignment in electrospun materials. This image processing function can be coupled with a variety of imaging modalities to assign an objective numerical value to scaffold anisotropy. A data image of an electrospun scaffold is composed of pixels that depict the spatial organization of the constituent fibers. The 2D FFT function converts this spatial information into a mathematically defined frequency domain that maps the rate at which pixel intensities change across the original data image. This output image also contains quantitative information concerning the orientation of objects in a data image. We discuss the theory and practice of using the frequency plot of the 2D FFT function to measure relative scaffold anisotropy and identify the principal axis of fiber orientation. We note that specific degrees of scaffold anisotropy may represent a critical design feature in the fabrication of tissue...

Image encryption based on a new total shuffling algorithm

Abstract: This paper presents image encryption scheme, which employs a new image total shuffling matrix to shuffle the positions of image pixels and then uses the states combination of two chaotic systems to confuse the relationship between the plain-image and the cipher-image. The experimental results demonstrate that the new image total shuffling algorithm has a low time complexity and the suggested encryption algorithm of image has the advantages of large key space and high security, and moreover, the distribution of grey values of the encrypted image has a random-like behavior.

Automatic Image Colorization Via Multimodal Predictions

Abstract: We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a non-uniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.

Content-Based Image Retrieval Using Multiresolution Color and Texture Features

Abstract: In this paper, we propose a content-based image retrieval method based on an efficient combination of multiresolution color and texture features. As its color features, color autocorrelo- grams of the hue and saturation component images in HSV color space are used. As its texture features, BDIP and BVLC moments of the value component image are adopted. The color and texture features are extracted in multiresolution wavelet domain and combined. The dimension of the combined feature vector is determined at a point where the retrieval accuracy becomes saturated. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for six test DBs. Especially, it demonstrates more excellent retrieval accuracy for queries and target images of various resolutions. In addition, the proposed method almost always shows performance gain in precision versus recall and in ANMRR over the other methods.

Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding

Abstract: In recent years, practical Wyner-Ziv (WZ) video coding solutions have been proposed with promising results. Most of the solutions available in the literature model the correlation noise (CN) between the original frame and its estimation made at the decoder, which is the so-called side information (SI), by a given distribution whose relevant parameters are estimated using an offline process, assuming that the SI is available at the encoder or the originals are available at the decoder. The major goal of this paper is to propose a more realistic WZ video coding approach by performing online estimation of the CN model parameters at the decoder, for pixel and transform domain WZ video codecs. In this context, several new techniques are proposed based on metrics which explore the temporal correlation between frames with different levels of granularity. For pixel-domain WZ (PDWZ) video coding, three levels of granularity are proposed: frame, block, and pixel levels. For transform-domain WZ (TDWZ) video coding, DCT bands and coefficients are the two granularity levels proposed. The higher the estimation granularity is, the better the rate-distortion performance is since the deeper the adaptation of the decoding process is to the video statistical characteristics, which means that the pixel and coefficient levels are the best performing for PDWZ and TDWZ solutions, respectively.

Constrained connectivity for hierarchical image partitioning and simplification

Abstract: This paper introduces an image partitioning and simplification method based on the constrained connectivity paradigm. According to this paradigm, two pixels are said to be connected if they satisfy a series of constraints defined in terms of simple measures such as the maximum gray-level differences over well-defined pixel paths and regions. The resulting connectivity relation generates a unique partition of the image definition domain. The simplification of the image is then achieved by setting each segment of the partition to the mean value of the pixels falling within this segment. Fine to coarse partition hierarchies (and, therefore, images of increasing degree of simplification) are produced by varying the threshold value associated with each connectivity constraint. The paper also includes a generalization to multichannel images, application examples, a review of related image segmentation techniques, and pseudocode for an implementation based on queue and stack data structures.

System, method and a computer readible medium for providing an output image

Abstract: A method for providing an output image, the method includes: determining an importance value for each input pixels out of multiple input pixels of an input image; applying on each of the multiple input pixels a conversion process that is responsive to the importance value of the input pixel to provide multiple output pixels that form the output image; wherein the input image differs from the output image.

A method and an apparatus for processing a video signal

Abstract: An apparatus for processing a video signal and method thereof are disclosed. The present invention includes receiving prediction mode information, interpolating information and a residual of a current block, reconstructing an interpolating pixel using the interpolating information and a neighbor block, and reconstructing the current block using the interpolating pixel, the prediction mode information and the residual, wherein the interpolating information is generated based on a location of the current block. According to an apparatus and method for processing a video signal, high reconstruction rate can be obtained by improving the related art method having limited intra prediction modes available for a current block located on a boundary area of a picture in encoding in a manner of reconstructing and using an interpolating pixel based on interpolating information.

Optical lock-in detection imaging microscopy for contrast-enhanced imaging in living cells

Abstract: One of the limitations on imaging fluorescent proteins within living cells is that they are usually present in small numbers and need to be detected over a large background. We have developed the means to isolate specific fluorescence signals from background by using lock-in detection of the modulated fluorescence of a class of optical probe termed "optical switches." This optical lock-in detection (OLID) approach involves modulating the fluorescence emission of the probe through deterministic, optical control of its fluorescent and nonfluorescent states, and subsequently applying a lock-in detection method to isolate the modulated signal of interest from nonmodulated background signals. Cross-correlation analysis provides a measure of correlation between the total fluorescence emission within single pixels of an image detected over several cycles of optical switching and a reference waveform detected within the same image over the same switching cycles. This approach to imaging provides a means to selectively detect the emission from optical switch probes among a larger population of conventional fluorescent probes and is compatible with conventional microscopes. OLID using nitrospirobenzopyran-based probes and the genetically encoded Dronpa fluorescent protein are shown to generate high-contrast images of specific structures and proteins in labeled cells in cultured and explanted neurons and in live Xenopus embryos and zebrafish larvae.