Showing papers on "Grayscale published in 2003"

Fast, Robust Image Registration for Compositing High Dynamic Range Photographs from Hand-Held Exposures

Abstract: In this paper, we present a fast, robust, and completely automatic method for translational alignment of hand-held photographs. The technique employs percentile threshold bitmaps to accelerate image operations and avoid problems with the varying exposure levels used in high dynamic range (HDR) photography. An image pyramid is constructed from grayscale versions of each exposure, and these are converted to bitmaps which are then aligned horizontally and vertically using inexpensive shift and difference operations over each image. The cost of the algorithm is linear with respect to the number of pixels and effectively independent of the maximum translation. A three million pixel exposure can be aligned in a fraction of a second on a contemporary microprocessor using this technique.

Multiscale morphological segmentation of gray-scale images

Abstract: In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.

IWT-interactive watershed transform: a hierarchical method for efficient interactive and automated segmentation of multidimensional gray-scale images

Abstract: In this paper we present the Interactive Watershed Transform (IWT) for efficient segmentation of multidimensional grayscale images. The IWT builds upon a fast immersion-based watershed transform (WT) followed by a hierarchical organization of the resulting basins in a tree structure. Each local image minimum is represented as an atomic basin at the lowest hierarchy level. The fast WT consists of two steps. First, all image elements are sorted according to their image intensity using a Bucket Sort algorithm. Second, each element is processed exactly once with respect to its neighborhood (e. g., 4, 6, and 8 direct neighbors for 2d, 3d, and 4d transform, respectively) in the specified order. Sort-ing, processing, and tree generation are of order O(n). After computing the WT, one global parameter, the so-called preflooding height, and an arbitrary number of markers are evaluated in real-time to control tree partitioning and basin merging. The IWT has been successfully applied to a large variety of medical images, e. g., for segmentation and volu-metry of neuroanatomic structures as well as bone segmentation, without making assumptions on the objects’ shapes. The IWT combines automation and efficient interactive control in a coherent algorithm while completely avoiding oversegmentation which is the major problem of the classical WT.

An image representation algorithm compatible with neural-associative-processor-based hardware recognition systems

Abstract: A robust image representation algorithm compatible with the VLSI-matching-engine-based image recognition system has been developed The spatial distributions of four-principal-direction edges in a 64 /spl times/ 64-pels gray scale image are coded to form a 64-dimension feature vector Since the 2D edge information is reduced to a feature vector by projecting edge flags to the principal directions, it is named the projected principal-edge distribution (PPED) representation The PPED vectors very well preserve the human perception of similarity among images in the vector space, while achieving a substantial dimensionality reduction in the image data The PPED algorithm has been applied to medical radiograph analysis, which was taken as a test vehicle for algorithm optimization The robust nature of the PPED representation has been confirmed by the recognition results comparable to the diagnosis by experts having several years of experience in a university hospital Dedicated digital VLSI circuits have been developed for PPED vector generation in order to expedite the processing A test hardware recognition system was constructed using the vector generation circuits, where the analog neural associative processor chip developed in a separate project was employed as a vector-matching engine As a result, a successful medical radiograph analysis has been experimentally demonstrated using the hardware system Feasibility of a very low-power operation of the system has been also demonstrated

Image display device, image display method, and image display program

Abstract: The invention provides an easy and simple method of converting the resolution of image data, which is capable of generating the high-resolution image data without incongruity, does not make a circuit in the display device complicated and does not increase the power consumption. This invention can include a portable terminal device, such as a mobile telephone or a PDA, that processes and displays image data transmitted from the outside. Image data with a plurality of grayscales can be displayed by controlling the display state of each pixel in a display unit in accordance with grayscale control pulses corresponding to the number of grayscales. For example, when a 64 grayscale display is performed, a grayscale level is defined using sixty four grayscale control pulses. Thus, it is possible to emit light from pixels in the display unit by sixty four grayscale levels. Further, the resolution converting device can generate pseudo-high-resolution image data obtained by increasing the number of pixels of original image data by n multiplication and by reducing the number of grayscales of the original image data to 1/n. When displaying the pseudo-high-resolution image data, the number of grayscale control pulses is changed to 1/n by a halftone controller. That is, in the pseudo-high-resolution image data, the number of grayscales is 1/n. Therefore, the number of grayscale control pulses used for halftone display may be 1/n in accordance with the number of grayscales. Therefore, the low-resolution image data can be displayed without incongruity by converting the resolution, also, it is possible to reduce power consumption of the display unit by the reduced amount of the number of grayscale levels pulses.

Detecting and correcting red-eye in a digital image

Abstract: Systems and methods of detecting and correcting red-eye in a digital image (12) are described. In one aspect, measures of pixel redness in the digital image (12) are computed. A preliminary set of candidate red-eye pixel areas (50) is identified based on the computed pixel redness measures. Each candidate red-eye pixel area (50) having a computed redness contrast relative to at least one respective neighboring pixel area (51) less than a prescribed redness contrast threshold is filtered from the preliminary set. In addition, each candidate red-eye pixel area (50) located in an area (58) of the digital image having a computed grayscale contrast relative to at least one respective neighboring pixel area (0-7) less than a prescribed grayscale contrast threshold is filtered from the preliminary set.

Robust digital image watermarking method against geometrical attacks

Abstract: This paper proposes a new image watermarking scheme which is robust to RST attacks with cropping by improving Fourier-Mellin transform based watermarking (FMW). The proposed scheme reorders and modifies function blocks of FMW for improvement of realization and performance. Unlike FMW, our method uses log-polar map (LPM) in the spatial domain for scaling invariance, while translation invariance is provided by the use of an invariant centroid (IC) as the origin of LPM. IC is a gravity center of a central area on gray scale image that is invariant although an image is attacked by RST. For this, its calculation method is proposed. Also since LPM includes the property which transforms rotation of Cartesian coordinates system into a cyclic shift, 2-D DFT is performed on the LPM image and the magnitude spectrum extracted to provide a domain that is rotation invariant. The resulting domain, which is invariant to RST, is then used as the watermark-embedding domain. Furthermore, to prevent the watermarked image from degrading due to the coordinate system conversion, only LPM image of watermark signal is inverse mapped to Cartesian coordinates and add to the original image. Experimental results demonstrate that the proposed scheme is robust to RST attacks.

Method and device for creating 3-dimensional view image

Abstract: A 3-dimensional view image formation device (1) calculates a depth value (Z value) according to the gray scale value of the 2-dimensional image data. This Z value is subjected to a reverse judgment processing, a smoothing processing, a distribution correction processing, a local correction processing, and the like. A parallax is decided according to the obtained Z value and each pixel is moved for the amount of the parallax, thereby generating 3-dimensional view image data.

Device for monitoring around a vehicle

Abstract: A device for monitoring around a vehicle capable of detecting objects present around the vehicle based on an image captured by at least one infrared camera member provided with the vehicle. The device includes a binary object extraction unit which subjects a gray scale image of the image captured by the infrared camera member to a binary thresholding method, and extracts a binary object from the gray scale image; a gray scale object extraction unit which extracts a gray scale object, a range of the gray scale object including the binary object, from the gray scale image based on change in luminance of the gray scale image, and a pedestrian determination unit which sets a search area in an area including the gray scale object, and recognizes a pedestrian in the gray scale image based on a luminance variance in the search area.

Images as bags of pixels

Abstract: We propose modeling images and related visual objects as bags of pixels or sets of vectors. For instance, gray scale images are modeled as a collection or bag of (X, Y, I) pixel vectors. This representation implies a permutational invariance over the bag of pixels, which is naturally handled by endowing each image with a permutation matrix. Each matrix permits the image to span a manifold of multiple configurations, capturing the vector set's invariance to orderings or permutation transformations. Permutation configurations are optimized while jointly modeling many images via maximum likelihood. The solution is a uniquely solvable convex program, which computes correspondence simultaneously for all images (as opposed to traditional pairwise correspondence solutions). Maximum likelihood performs a nonlinear dimensionality reduction, choosing permutations that compact the permuted image vectors into a volumetrically minimal subspace. This is highly suitable for principal components analysis which, when applied to the permutationally invariant bag of pixels representation, outperforms PCA on appearance-based vectorization by orders of magnitude. Furthermore, the bag of pixels subspace benefits from automatic correspondence estimation, giving rise to meaningful linear variations such as morphings, translations, and jointly spatio-textural image transformations. Results are shown for several datasets.

Color-to-grayscale conversion to maintain discriminability

Abstract: Monochrome devices that receive color imagery must perform a conversion from color to grayscale. The most common approach is to calculate the luminance signal from the three color signals. The problem with this approach is that the distinction between two colors of similar luminance (but different hue) is lost. This can be a significant problem when rendering colors within graphical objects such as pie charts and bar charts, which are often chosen for maximum discriminability. This paper proposes a method of converting color business graphics to grayscale in a manner that preserves discriminability. Colors are first sorted according to their original lightness values. They are then spaced equally in gray, or spaced according to their 3-D color difference from colors adjacent to them along the lightness dimension. This is most useful when maximum differentiability is desired in images containing a small number of colors, such as pie charts and bar graphs. Subjective experiments indicate that the proposed algorithms outperform standard color-to-grayscale conversions.

driving method of liquid crystal display

Abstract: A driving method of a liquid crystal display is described. The driving method of liquid crystal display firstly detects the maximum grayscale of all pixel of liquid crystal display, and adjusts the out-put brightness of back-light modules in order to provide the corresponding brightness to the pixels of the maximum grayscale. Meanwhile, adjust all grayscale values of all pixels to map a new grayscale value, and drive each pixel. This driving method of this crystal display provides a sharp display quality when displaying darker image, and further reduces power consumption, particularly when displaying low brightness images.

Colorization algorithm for grayscale image by propagating seed pixels

Abstract: This paper presents a method of the colorization for a black and white imagery by propagating seed pixels. Although the colorization is an ill-posed problem, in this paper, the problem is solved based on local Markov property on images. At first, a user plants some color pixels as seeds on a grayscale image. Then, colors are propagated from the seed pixels by minimizing the color difference among connected 4-pixels. In this paper, a basic propagating algorithm is proposed and modified algorithms, which can stop the propagation at edge parts by setting partitions, are introduced. By comparing with a previous algorithm, the proposed algorithm works very well for both quality and speed.

Genetic algorithm optimization of multidimensional grayscale soft morphological filters with applications in film archive restoration

Abstract: Automatic restoration of old film archives has become of increasing interest in the last few years with the rise of consumer digital video applications and the need to supply more programming material of an acceptable quality in a multimedia context. A technique is described for the optimization of multidimensional grayscale soft morphological filters for applications in automatic film archive restoration, specific to the problem of film dirt removal. The optimization is undertaken with respect to a criterion based on mean absolute error and is performed using a genetic algorithm. Experiments have shown that the filter found using this technique has excellent performance in attenuating/removing film dirt from image sequences and has little, if any, effect on the image detail. The results of applying such a filter to a real image sequence were analyzed and compared to those obtained by restoring the same image sequence using a global filtering approach (LUM filter) and a spatio-temporal local filtering approach (ML3Dex filter with noise detection). From a film dirt removal point of view, the optimized soft morphological filter showed improved results compared to the LUM filter and comparable results with respect to the ML3Dex filter with noise detection. Also, the optimized filter accurately restored all fast-moving objects present in the sequence, without the need for motion compensation, whereas the other two methods failed to do this. The proposed method proved to be a simple, fast, and cheap approach for the automatic restoration of old film archives.

Color Calibration of Scanners for Scanner‐Independent Grain Grading

Abstract: Scanner technology is emerging as a cost-effective and robust imaging alternative to camera-based systems in many applications. However, scanner technology is changing so fast that image quality can vary from model to model. It is critical that images scanned with different scanners be brought to a common basis for processing and measurement through a calibration process that eliminates scanner-to-scanner variability. The focus of this research was to investigate scanner-to-scanner variability and develop color correction or mapping functions to allow for machineindependent grain inspection. Various makes and models of scanners were compared for optical and color characteristics. Three different color correction methods wereevaluated: grayscale (GS) transformation, redgreen-blue (RGB) transformation, and histogram matching. All three models of color correction worked within satisfactory tolerance for a multicolor Q60 chart. However, for grain samples of a limited color range, the histogram matchi...

Joint halftoning and watermarking

Abstract: A framework to jointly halftone and watermark a grayscale images is presented. The framework needs the definition of three components: a human visual system (HVS)-based error metric between the continuous-tone image and a halftone, a watermarking scheme with a corresponding watermark detection measure, and a search strategy to traverse the space of halftones. We employ the HVS-based error metric used in the direct binary search (DBS) halftoning algorithm, and we use a block-based spread spectrum watermarking scheme and the toggle and swap search strategy of DBS. The halftone is printed on a desktop printer and scanned using a flatbed scanner. The watermark is detected from the scanned image and a number of post-processed versions of the scanned image, including one restored in Adobe PhotoShop. The results show that the watermark is extremely resilient to printing, scanning, and post-processing; for a given baseline image quality, joint optimization is better than watermarking and halftoning independently. For this particular algorithm, the original continuous-tone image is required to detect the watermark.

Neural associative memory storing gray-coded gray-scale images

Abstract: We present a neural associative memory storing gray-scale images. The proposed approach is based on a suitable decomposition of the gray-scale image into gray-coded binary images, stored in brain-state-in-a-box-type binary neural networks. Both learning and recall can be implemented by parallel computation, with time saving. The learning algorithm, used to store the binary images, guarantees asymptotic stability of the stored patterns, low computational cost, and control of the weights precision. Some design examples and computer simulations are presented to show the effectiveness of the proposed method.

Color Optical Flow

Abstract: Grayscale optical-flow methods have long been the focus of methods for recovering optical flow. Optical flow recovery from color-images can be implemented using direct methods, i.e. without using computationally costly iterations or search strategies. The quality of recovered optical flow can be assessed and tailored after processing, providing an effective, efficient tool for motion estimation. In this paper, a brief introduction to optical flow is presented, the optical flow constraint equation and its extension to color images is presented. New methods for solving this extended equation are given. Results of applying these methods to two synthetic image sequences are presented.

Dose control for fabrication of grayscale structures using a single step electron-beam lithographic process

Abstract: Many optoelectronic devices include multidimensional (grayscale) semiconductor structures such as diffractive optical elements (DOE’s), blazed gratings, and photonic band gap (PBG) crystals. Their performance is known to be highly sensitive to dimensional accuracy of features in the multidimensional structure. Therefore, it is essential to have a tight control of the feature size in the fabrication process. Grayscale electron(e)-beam lithography enables a single step lithographic process for fabrication of such multidimensional structures and therefore has several advantages over using a binary lithographic process multiple times. As the feature size decreases, proximity effect in the e-beam lithographic process can make dimensions of the written features in a device substantially different from the ideal ones so that performance of the device is significantly degraded. In this paper, an efficient dose control scheme for grayscale e-beam lithography, which attempts to minimize the difference between the s...

A LSB steganography detection algorithm

Abstract: The spatial LSB steganography results in the alteration of the smooth characteristics between adjoining pixels of the raw image. The relation between the length of embedded message and the gradient energy is theoretically analyzed, and then a steganalysis and detection method, named gradient energy-flipping rate detection (GEFR) is proposed. Through the analysis of the variation of the gradient energy, which results from the LSB steganography in color and grayscale image, the secret message embedded in the target image is detected, and the length of the embedded message is estimated. The method is proved effective and accurate by simulation (detection rate reaches 0.01bit per pixel).

An adaptive approach to vehicle license plate localization

Abstract: It is well known that locating vehicle license plate is a very difficult problem in the field of image segmentation and ITS (intelligent transportation system). And unfortunately most of the methods already presented don't work robustly and usually impose some special restrictions on the input image condition, such as illumination, noise, etc. In this paper we proposed an accurate and robust localization algorithm by employing an adaptive thresholding and search strategy. The algorithm is based on the spatial frequency characteristic that the grayscale in the license plate region usually varies more frequently than in the background due to the existence of characters. In this paper, an adaptive threshold is firstly reached through an iterative selection-verification process. The gray image is then binarized by this threshold, and the algorithm localizes the license plate in the binarized image combining some prior knowledge of the license plate. The final accurate license plate segmentation is achieved after an accurate edge detection process based on the color information of the license plate. The robustness and performance of the algorithm are demonstrated by the convincing segmentation results even when the images are rather complex, noisy and in poor illumination.

An unsupervised approach to color video thresholding

Abstract: Thresholding color video images is challenging because of the low spatial resolution and the complex backgrounds. This paper investigates the issue of thresholding these images by reducing the number of colors in order to improve automated text detection and recognition. An unsupervised thresholding approach is presented which reduces the background complexity while retaining the important text character pixels. The experiments show that our proposed thresholding approach performs significantly better than simple image histogram-based methods, which generally do not produce satisfactory results.

Color error-diffusion halftoning

Abstract: Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.