Showing papers on "Grayscale published in 2008"

Sparse Representation for Color Image Restoration

Abstract: Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in . This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.

Learning multiscale sparse representations for image and video restoration

Abstract: This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD algorithm was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein, IEEE Trans. Signal Process., 54 (2006), pp. 4311–4322], formulating sparse dictionary learning for grayscale image representation as an optimization problem, efficiently solved via orthogonal matching pursuit (OMP) and singular value decomposition (SVD). Following this work, we propose a multiscale learned representation, obtained by using an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to predefined dictionaries such as wavelets and is shown to lead to state-of-the-art results in a number of image and video enhancement and restoration applications. This paper describes the proposed framework and accompanies it by numerous examples demonstrating its strength.

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.

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.

Intrinsic colorization

Abstract: In this paper, we present an example-based colorization technique robust to illumination differences between grayscale target and color reference images. To achieve this goal, our method performs color transfer in an illumination-independent domain that is relatively free of shadows and highlights. It first recovers an illumination-independent intrinsic reflectance image of the target scene from multiple color references obtained by web search. The reference images from the web search may be taken from different vantage points, under different illumination conditions, and with different cameras. Grayscale versions of these reference images are then used in decomposing the grayscale target image into its intrinsic reflectance and illumination components. We transfer color from the color reflectance image to the grayscale reflectance image, and obtain the final result by relighting with the illumination component of the target image. We demonstrate via several examples that our method generates results with excellent color consistency.

Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video

Abstract: This paper presents a quick and simple method for converting complex images and video to perceptually accurate greyscale versions. We use a two-step approach first to globally assign grey values and determine colour ordering, then second, to locally enhance the greyscale to reproduce the original contrast. Our global mapping is image independent and incorporates the Helmholtz-Kohlrausch colour appearance effect for predicting differences between isoluminant colours. Our multiscale local contrast enhancement reintroduces lost discontinuities only in regions that insufficiently represent original chromatic contrast. All operations are restricted so that they preserve the overall image appearance, lightness range and differences, colour ordering, and spatial details, resulting in perceptually accurate achromatic reproductions of the colour original.

Perceptual Evaluation of Color-to-Grayscale Image Conversions

Abstract: Color images often have to be converted to grayscale for reproduction, artistic purposes, or for subsequent processing Methods performing the conversion of color images to grayscale aim to retain as much information about the original color image as possible, while simultaneously producing perceptually plausible grayscale results Recently, many methods of conversion have been proposed, but their performance has not yet been assessed Therefore, the strengths and weaknesses of color-to-grayscale conversions are not known In this paper, we present the results of two subjective experiments in which a total of 24 color images were converted to grayscale using seven state-of-the-art conversions and evaluated by 119 human subjects using a paired comparison paradigm We surveyed nearly 20000 human responses and used them to evaluate the accuracy and preference of the color-to-grayscale conversions To the best of our knowledge, the study presented in this paper is the first perceptual evaluation of color-to-grayscale conversions Besides exposing the strengths and weaknesses of the researched methods, the aim of the study is to attain a deeper understanding of the examined field, which can accelerate the progress of color-to-grayscale conversion

Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation

Abstract: This paper mainly discusses fractional differential approach to detecting textural features of digital image and its fractional differential filter. Firstly, both the geometric meaning and the kinetic physical meaning of fractional differential are clearly explained in view of information theory and kinetics, respectively. Secondly, it puts forward and discusses the definitions and theories of fractional stationary point, fractional equilibrium coefficient, fractional stable coefficient, and fractional grayscale co-occurrence matrix. At the same time, it particularly discusses fractional grayscale co-occurrence matrix approach to detecting textural features of digital image. Thirdly, it discusses in detail the structures and parameters of n×n any order fractional differential mask on negative x-coordinate, positive x-coordinate, negative y-coordinate, positive y-coordinate, left downward diagonal, left upward diagonal, right downward diagonal, and right upward diagonal, respectively. Furthermore, it discusses the numerical implementation algorithms of fractional differential mask for digital image. Lastly, based on the above-mentioned discussion, it puts forward and discusses the theory and implementation of fractional differential filter for digital image. Experiments show that the fractional differential-based image operator has excellent feedback for enhancing the textural details of rich-grained digital images.

Fabric defect detection using modified local binary patterns

Abstract: Local binary patterns (LBPs) are one of the features which have been used for texture classification. In this paper, a method based on using these features is proposed for fabric defect detection. In the training stage, at first step, LBP operator is applied to an image of defect free fabric, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each of these windows. Based on comparison with the reference feature vector, a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is multiresolution and gray scale invariant and can be used for defect detection in patterned and unpatterned fabrics. Because of its simplicity, online implementation is possible as well.

Image equalization based on singular value decomposition

Abstract: In this paper, a novel image equalization technique which is based on singular value decomposition (SVD) is proposed. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD domain and after normalizing the singular value matrix it reconstructs the image in the spatial domain by using the updated singular value matrix. The technique is called the singular value equalization (SVE) and compared with the standard grayscale histogram equalization (GHE) method. The visual and quantitative results suggest that the proposed SVE method clearly outperforms the GHE method.

What is an image

Abstract: Whether in the hands of an advertising executive or a scientist, visual images have the power either to convey information efficiently or to mislead through sleight of hand. To evaluate adequately what is being presented in a published imaging article, readers must first understand what constitutes an image. A wide range of technologies can produce an image of the brain, and those images can capture an even wider range of features of the brain tissue that is imaged (Table 1). Nevertheless, all of those images have in common a basic physical structure, and a common set of terms can be used to describe that structure. Table 1 Neuroimaging Modalities: Method, Strengths, and Weaknesses An image is simply a two-dimensional, physical array of much smaller, two-dimensional squares or rectangles, which are elemental units of the picture called picture elements (or pixels). Each pixel corresponds to a three-dimensional square or rectangular chunk of brain tissue called a volume element (or voxel). Each pixel of the image is typically assigned either a level of visual grayness ranging from black to white (Fig. 1) or an arbitrary color that represents a numerical value. That numerical value in a pixel in turn quantifies some characteristic or feature of the tissue in the corresponding voxel of the brain being imaged. That numerical value and its corresponding grayscale representation or color-encoding may represent, for example, the degree to which x-rays pass through the tissue (in a computed tomography [CT] scan; Fig. 2), the amount of radioactivity emitted by the tissue (in positron emission tomography), the number of hydrogen nuclei in the tissue (in anatomical magnetic resonance imaging [MRI]; Fig. 2), the direction of fiber tracts in the brain (in diffusion tensor imaging [DTI] Fig. 3), the amount of oxygenated or deoxygenated hemoglobin (in functional MRI; Fig. 4), or a molecular concentration (in magnetic resonance spectroscopy [MRS] Fig. 5). Fig. 1 Composition of an image. Left, Each volume element (voxel) contains either high (H) or low (L) concentrations of a physical quantity of interest. Middle, The physical quantities of interest in each voxel are encoded numerically and assigned to a corresponding ... Fig. 2 Computed tomography and magnetic resonance imaging of brain structure. W = white matter, C = caudate, T = thalamus, CSF = cerebrospinal fluid. Reprinted with permission from Lewis's Child and Adolescent Psychiatry. Philadelphia: Lippincott Williams & ... Fig. 3 Diffusion tensor imaging of fiber tracts. Colors depict directions of three-dimensional fiber tracts. Red = left (L) to right (R); green = posterior (P) to anterior (A); BG = basal ganglia; CC = corpus callosum. Reprinted with permission from Lewis's ... Fig. 4 Functional magnetic resonance imaging of brain activity. This axial image (a slice parallel to the floor in a standing person) shows statistical significance of functional activity in one direction in bilateral basal ganglia, inferior frontal and anterior ... Fig. 5 Magnetic resonance spectroscopy (MRS) of brain metabolites. One subregion of the brain is represented by several voxels, each of which generates a spectrum of signals from various neurometabolites. The height of each peak in the spectrum indicates the ... Variation in this level of grayness or its color encoding across the two-dimensional array of pixels can distinguish one type of tissue of the brain from another in the corresponding three-dimensional array of tissue voxels, or slice, of brain tissue. A stack of such slices, one on top of another, will represent a larger volume of brain tissue, possibly the entire brain, visualized at one point in time. Table 1 summarizes the technologies commonly used to image the brain, the properties of the tissue encoded in the image, and the major strengths and limitations of each of the technologies. A similar technique is used for building images of the brain across time, which can be used to represent electrical or neurochemical functioning in a particular brain voxel. A functional MRI map of functional activity, for example, captures in each pixel the variation across time in the level of deoxygenated hemoglobin, which in turn indexes the level of neural activity, in the corresponding brain voxel. The degree to which that temporal variation in deoxyhemoglobin in each voxel correlates with the temporal variation in behavior or sensory experience of the person being imaged is assessed statistically, and that statistical index (usually a probability or p value) is assigned to the corresponding pixel of the image. That statistical index is then color encoded. In other words, this statistic represents the likelihood that temporal variation in neural activity within that chunk of brain tissue being imaged correlates with the temporal variation in behavior or experience of the subject. If that index passes a preassigned threshold (e.g., p < .05), then neural activity in that chunk of tissue is assumed to participate in the behavior or experience of the subject. That statistic is literally painted on the brain image, usually by being superimposed on a corresponding grayscale representation of brain structure to help identify where the activity is located (Fig. 4). The color-encoded image therefore represents a four-dimensional map comprising three dimensions that define spatial location and a fourth dimension that indexes change in neural activity across time. The quality and clinical or scientific usefulness of an image depends on a number of characteristics of the information carried within and across voxels. Resolution refers to the volume of brain tissue represented by a given voxel. If the size of the tissue being imaged is held constant, then an image with higher resolution has voxels that are smaller in size but greater in number. This allows for greater discrimination of neighboring structures within the brain, but usually increases the time required to obtain the image. At lower resolution, voxels are larger and therefore are more likely to cross the boundaries of tissues and regions, making them more difficult to discriminate from each other. Measurement of any physical properties, including those represented by brain images, inevitably involves some degree of error because no measurement process is perfect. More error, or noise, degrades the quality of an image, just as a snowy picture degrades the image on a television screen with poor reception. The portion of the measurement of a tissue property in each voxel that is accurate (the true signal from the voxel), relative to the noise present in the measurement, is termed the signal-to-noise ratio (SNR) of the image. It provides a useful summary of image fidelity. The strength of the magnetic field of an MRI scanner is an important determinant of the SNR and image quality because it determines how many hydrogen nuclei in a voxel emit radio signals during the scanning process; the larger their number, the stronger their signals are in summation and the more accurate is measurement of the tissue characteristics that those radio signals encode from each voxel. Increasing the size of the voxel (and therefore also increasing the amount of tissue it contains) also increases the number of emitting nuclei and therefore also increases the SNR, but at the expense of decreasing the resolution of the image. Finally, contrast refers to the difference in signal strength between adjacent but distinct types of brain tissues, such as gray matter and white matter. By convention, magnetic resonance images are assigned a grayscale value that ranges from 0 (pure black) to 255 (pure white). In an image with optimal contrast, this number would vary greatly across different types of tissue. One of the principal advantages of magnetic resonance imaging over computed tomographic imaging is that the former provides superior contrast for brain tissues that are important to discriminate for clinical and research purposes (especially gray and white matter), thereby aiding the accurate identification of cortical and subcortical structures. In imaging, as in life, nothing comes for free. Improvement in the convenience of image acquisition (e.g., the amount of time or expense required) or a technical property of the image (e.g., resolution, contrast, SNR) inevitably comes at the expense of some other aspect of the image. In any given study, therefore, the choices made by the imager are crucial for keeping these in balance and for keeping the images optimized for the population studied, the brain feature of interest, and the hypothesis in question. In future columns, we will discuss these choices in relation to what neuroimaging is teaching us about scientifically and clinically relevant questions in child and adolescent psychiatry.

Learning the Uncorrelated, Independent, and Discriminating Color Spaces for Face Recognition

Abstract: This paper presents learning the uncorrelated color space (UCS), the independent color space (ICS), and the discriminating color space (DCS) for face recognition. The new color spaces are derived from the RGB color space that defines the tristimuli R, G, and B component images. While the UCS decorrelates its three component images using principal component analysis (PCA), the ICS derives three independent component images by means of blind source separation, such as independent component analysis (ICA). The DCS, which applies discriminant analysis, defines three new component images that are effective for face recognition. Effective color image representation is formed in these color spaces by concatenating their component images, and efficient color image classification is achieved using the effective color image representation and an enhanced Fisher model (EFM). Experiments on the face recognition grand challenge (FRGC) and the biometric experimentation environment (BEE) show that for the most challenging FRGC version 2 Experiment 4, which contains 12 776 training images, 16 028 controlled target images, and 8014 uncontrolled query images, the ICS, DCS, and UCS achieve the face verification rate (ROC III) of 73.69%, 71.42%, and 69.92%, respectively, at the false accept rate of 0.1%, compared to the RGB color space, the 2-D Karhunen-Loeve (KL) color space, and the FRGC baseline algorithm with the face verification rate of 67.13%, 59.16%, and 11.86%, respectively, with the same false accept rate.

Efficient 2-D Grayscale Morphological Transformations With Arbitrary Flat Structuring Elements

Abstract: An efficient algorithm is presented for the computation of grayscale morphological operations with arbitrary 2D flat structuring elements (S.E.). The required computing time is independent of the image content and of the number of gray levels used. It always outperforms the only existing comparable method, which was proposed in the work by Van Droogenbroeck and Talbot, by a factor between 3.5 and 35.1, depending on the image type and shape of S.E. So far, filtering using multiple S.E.s is always done by performing the operator for each size and shape of the S.E. separately. With our method, filtering with multiple S.E.s can be performed by a single operator for a slightly reduced computational cost per size or shape, which makes this method more suitable for use in granulometries, dilation-erosion scale spaces, and template matching using the hit-or-miss transform. The discussion focuses on erosions and dilations, from which other transformations can be derived.

Improved document image binarization by using a combination of multiple binarization techniques and adapted edge information

Abstract: This paper presents a new adaptive approach for document image binarization. The proposed method is mainly based on the combination of several state- of-the-art binarization methodologies as well as on the efficient incorporation of the edge information of the gray scale source image. An enhancement step based on mathematical morphology operations is also involved in order to produce a high quality result while preserving stroke information. The proposed method demonstrated superior performance against six (6) well-known techniques on numerous degraded handwritten and machine- printed documents. The performance evaluation is based on visual criteria as well as on an objective evaluation methodology.

Color image enhancement using brightness preserving dynamic histogram equalization

Abstract: Histogram equalization (HE), although one of the most popular techniques used for digital image enhancement, is not very suitable to be implemented directly in consumer electronics, such as television, because this method tends to produce an output with saturation effect. To overcome this weakness, it is suggested that the mean intensity of the input image be maintained in the output image. Previously, we proposed a method known as brightness preserving dynamic histogram equalization (BPDHE) which can fulfill this requirement for grayscale images. In this paper, we present several possibilities to extend this method for color images.

Non-ideal iris segmentation using graph cuts

Abstract: A non-ideal iris segmentation approach using graph cuts is presented. Unlike many existing algorithms for iris localization which extensively utilize eye geometry, the proposed approach is predominantly based on image intensities. In a step-wise procedure, first eyelashes are segmented from the input images using image texture, then the iris is segmented using grayscale information, followed by a post-processing step that utilizes eye geometry to refine the results. A preprocessing step removes specular reflections in the iris, and image gradients in a pixel neighborhood are used to compute texture. The image is modeled as a Markov random field, and a graph cut based energy minimization algorithm [2] is used to separate textured and untextured regions for eyelash segmentation, as well as to segment the pupil, iris, and background using pixel intensity values. The algorithm is automatic, unsupervised, and efficient at producing smooth segmentation regions on many non-ideal iris images. A comparison of the estimated iris region parameters with the ground truth data is provided.

Method and apparatus for image processing

Abstract: Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.

Noise removal and enhancement of binary images using morphological operations

Abstract: Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.

Introduction to Grayscale Calibration and Related Aspects of Medical Imaging Grade Liquid Crystal Displays

Abstract: Consistent presentation of digital radiographic images at all locations within a medical center can help ensure a high level of patient care. Currently, liquid crystal displays (LCDs) are the electronic display technology of choice for viewing medical images. As the inherent luminance (and thereby perceived contrast) properties of different LCDs can vary substantially, calibration of the luminance response of these displays is required to ensure that observer perception of an image is consistent on all displays. The digital imaging and communication in medicine (DICOM) grayscale standard display function (GSDF) defines the luminance response of a display such that an observer’s perception of image contrast is consistent throughout the pixel value range of a displayed image. The main purpose of this work is to review the theoretical and practical aspects of calibration of LCDs to the GSDF. Included herein is a review of LCD technology, principles of calibration, and other practical aspects related to calibration and observer perception of images presented on LCDs. Both grayscale and color displays are considered, and the influence of ambient light on calibration and perception is discussed.

Display device, display driver and image display method

Abstract: In a display device and a display driver, when a grayscale of a display image is equal to or lower than a specific grayscale value obtained from a histogram of the display image, a display grayscale is extended with a linear function. On the other hand, when a grayscale of a display image is equal to or higher than the specific grayscale value, histogram equalization of a part higher than the specific grayscale value is performed, and the display grayscale is extended with a non-linear function obtained from the histogram equalization.

Performance analysis of FPGA based Sobel edge detection operator

Abstract: Edge detection operation is an essential part in the field of image processing There are few ways to improve the performance of edge detection This paper proposes a FPGA-based approach Sobel edge detection An image is captured by a CMOS camera and converted into RGB color space and the image is converted into grayscale to obtain image intensity for edge detection The proposed Sobel edge detection operator is model using of Finite State Machine (FSM) which executes a matrix area gradient operation to determine the level of variance through different of pixels and display the result on a monitor The whole process is performed in the hardware level that utilizes the resources of FPGA board The result shows good performance of edge detection with 27 MHz clock operation

A Hardware Efficient Support Vector Machine Architecture for FPGA

Abstract: In real-time video mining applications it is desirable to extract information about human subjects, such as gender, ethnicity, and age, from grayscale frontal face images. Many algorithms have been developed in the machine learning, statistical data mining, and pattern classification communities that perform such tasks with remarkable accuracy. Many of these algorithms, however, when implemented in software, suffer poor frame rates due to the amount and complexity of the computation involved. This paper presents an FPGA friendly implementation of a Gaussian Radial Basis SVM well suited to classification of grayscale images. We identify a novel optimization of the SVM formulation that dramatically reduces the computational inefficiency of the algorithm. The implementation achieves 88.6% detection accuracy in gender classification which is to the same degree of accuracy of software implementations using the same classification mechanism.

Edge Detection in Gray Level Images based on the Shannon Entropy

Abstract: Most of the classical mathematical methods for edge detection based on the derivative of the pixels of the original image are Gradient operators, Laplacian and Laplacian of Gaussian operators. Gradient based edge detection methods, such as Roberts, Sobel and Prewitts, have used two 2-D linear filters to process vertical edges and horizontal edges separately to approximate first-order derivative of pixel values of the image. The Laplacian edge detection method has used a 2-D linear filter to approximate second-order derivative of pixel values of the image. Major drawback of second-order derivative approach is that the response at and around the isolated pixel is much stronger. In this research study, a novel approach utilizing Shannon entropy other than the evaluation of derivates of the image in detecting edges in gray level images has been proposed. The proposed approach solves this problem at some extent. In the proposed method, we have used a suitable threshold value to segment the image and achieve the binary image. After this the proposed edge detector is introduced to detect and locate the edges in the image. A standard test image is used to compare the results of the proposed edge detector with the Laplacian of Gaussian edge detector operator. In order to validate the results, seven different kinds of test images are considered to examine the versatility of the proposed edge detector. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study were quite promising.

An Image Encryption Scheme Based on Cross Chaotic Map

Abstract: In the paper, a cross chaotic map using Logistic map and Chebyshev map is presented. With the exceptionally desirable properties of non-periodical motion and non-convergence of chaos, scheme for image encryption has suggested a new and efficient way to deal with the intractable problem of fast and highly secure image. In order to realize image encryption, every pixel of image is randomly changed according to encryption matrix in the process of grayscale substitution; simultaneously scrambling transformation technique (row rotation and column rotation technique) is used in the process of position permutation. The experimental results indicate the scheme is more secure and efficient than convention alones, and it is feasible.

Segmentation-free skeletonization of grayscale volumes for shape understanding

Abstract: Medical imaging has produced a large number of volumetric images capturing biological structures in 3D. Computer-based understanding of these structures can often benefit from the knowledge of shape components, particularly rod-like and plate-like parts, in such volumes. Previously, skeletons have been a common tool for identifying these shape components in a solid object. However, obtaining skeletons of a grayscale volume poses new challenges due to the lack of a clear boundary between object and background. In this paper, we present a new skeletonization algorithm on grayscale volumes typical to medical imaging (e.g., MRI, CT and EM scans), for the purpose of identifying shape components. Our algorithm does not require an explicit segmentation of the volume into object and background, and is capable of producing skeletal curves and surfaces that lie centered at rod-shaped and plate-shaped parts in the grayscale volume. Our method is demonstrated on both synthetic and medical data.

An improved contrast enhancing approach for color-to-grayscale mappings

Abstract: Despite the widespread availability of color sensors for image capture, the printing of documents and books are still primarily done in black-and-white for economic reasons. In this case, the included illustrations and photographs are printed in grayscale, with the potential loss of important information encoded in the chrominance channels of these images. We present an efficient contrast enhancement algorithm for color-to-grayscale image conversion that uses both luminance and chrominance information. Our algorithm is about three orders of magnitude faster than previous optimization-based methods, while providing some guarantees on important image properties. More specifically, our approach preserves gray values present in the color image, ensures global consistency, and locally enforces luminance consistency. Our algorithm is completely automatic, scales well with the number of pixels in the image, and can be efficiently implemented on modern GPUs. We also introduce an error metric for evaluating the quality of color-to-grayscale transformations.

Image processing device, electronic camera and image processing program

Abstract: PROBLEM TO BE SOLVED: To provide an image processing device in which a gray scale reproduction range is enlarged.SOLUTION: An image processing device comprises: an image input part; a deviation detection part; an alignment part; and a gray scale combination part. In the device, the image input part inputs a low resolution image and a high resolution image which are obtained by photographing a same object by changing an exposure condition and the resolution. The deviation detection part detects a positional deviation in an image pattern between the low resolution image and the high resolution image. The alignment part corrects the positional deviation in the low resolution image. The gray scale combination part extracts gray scale information of the low resolution image from the low resolution image in which the positional deviation is corrected, and combines it to the gray scale information of the high resolution image. This processing generates a composite image in which a gray scale reproduction range is enlarged.