Showing papers on "Standard test image published in 2004"

Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections.

Abstract: We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images.

Image quality assessment using natural scene statistics

Abstract: Measurement of image quality is crucial for designing image processing systems that could potentially degrade visual quality. Such measurements allow developers to optimize designs to deliver maximum quality while minimizing system cost. This dissertation is about automatic algorithms for quality assessment of digital images. Traditionally, researchers have equated image quality with image fidelity, or the closeness of a distorted image to a ‘reference’ image that is assumed to have perfect quality. This closeness is typically measured by modeling the human visual system, or by using different mathematical criteria for signal similarity. In this dissertation, I approach the problem from a novel direction. I claim that quality assessment algorithms deal only with images and videos that are meant for human consumption, and that these signals are almost exclusively images and videos of the visual environment. Image distortions make these so-called natural scenes look ‘unnatural’. I claim that this departure from ‘expected’ characteristics could be quantified for predicting visual quality. I present a novel information-theoretic approach to image quality assessment using statistical models for natural scenes. I approach the quality assessment problem as an information fidelity problem, in which the distortion process is viewed as a channel that limits the flow of information from a source of natural images to the receiver (the brain). I show that quality of a test image is strongly related to the amount of statistical information about the reference image that is present in the test image. I also explore image quality assessment in the absence of the reference, and present a novel method for blindly quantifying the quality of images compressed by wavelet based compression algorithms. I show that images are rendered unnatural by the quantization process during lossy compression, and that this unnaturalness could be quantified blindly for predicting visual quality. I test and validate the performance of the algorithms proposed in this dissertation through an extensive study in which ground truth data was obtained from many human subjects. I show that the methods presented can accurately predict visual quality, and that they outperform current state-of-the-art methods in my simulations.

A classifier design for detecting image manipulations

Abstract: In this paper we present a framework for digital image forensics. Based on the assumptions that some processing operations must be done on the image before it is doctored and an expected measurable distortion after processing an image, we design classifiers that discriminates between original and processed images. We propose a novel way of measuring the distortion between two images, one being the original and the other processed. The measurements are used as features in classifier design. Using these classifiers we test whether a suspicious part of a given image has been processed with a particular method or not. Experimental results show that with a high accuracy we are able to tell if some part of an image has undergone a particular or a combination of processing methods.

Sampling the disparity space image

Abstract: A central issue in stereo algorithm design is the choice of matching cost Many algorithms simply use squared or absolute intensity differences based on integer disparity steps In this paper, we address potential problems with such approaches We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance

3D stereoscopic image pairs by depth-map generation

Abstract: This work presents a new unsupervised technique aimed to generate stereoscopic views estimating depth information from a single input image. Using a single input image, vanishing lines/points are extracted using a few heuristics to generate an approximated depth map. The depth map is then used to generate stereo pairs. The overall method is well suited for real time application and works also on CFA (colour filtering array) data acquired by consumer imaging devices. Experimental results on a large dataset are reported.

Compression of 3D color integral images

Abstract: In this paper, we discuss the compression results of full color 3D Integral Images (II) by MPEG-2 (Motion Picture Experts Group). II is a popular three-dimensional image video recording and display technique. The huge size of II data has become a practical issue for storing and transmitting of 3D scenes. The MPEG is a standard coded representation of moving pictures. We model the elemental images in II as consecutive frames in a moving picture. Therefore, MPEG scheme can be applied to take advantage of the high cross-correlations between elemental images. We also introduce several scanning topologies along the elemental image sequences and investigate their performance with different number of pictures in GOP (Group of Picture). Experimental results are presented to illustrate the image quality of the MPEG-2 and the baseline JPEG with the same compression rate. We show that a well-known and widely-available MPEG-2 scheme can be a good alternative for II compression.

A hybrid face recognition method using Markov random fields

Abstract: We propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a Markov random field (MRF) model. The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's is then inferred using the belief propagation (BP) algorithm. Finally, the ID of the test image is determined by a voting scheme from the estimated patch ID's. Experimental results on several face datasets indicate the significant potential of our method.

Integrating multiple model views for object recognition

Abstract: We present a new approach to appearance-based object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block is local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces (GAMs). During modeling, the model views are connected by a large number of region-tracks, each aggregating image regions of a single physical region across the views. At recognition time, GAMs are constructed matching a test image to each model view. The consistency of configurations of GAMs is measured by exploiting the model connections. A genetic algorithm finds covering the object as completely as possible the most consistent configuration. Introducing GAMs as an intermediate grouping level facilitates decision-making and improves discriminative power. As a complementary application, we introduce a novel GAM-based two-view filter and demonstrate its effectiveness in recovering correct matches in the presence of up to 96% mismatches.

Face recognition system

Abstract: The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.

Image analysis system and method

Abstract: An image analysis system (200) and related methods for automation of the monitoring of samples to determine crystal growth. Samples are imaged from time to time using a set of imaging parameters. The resulting images are evaluated to determine the contents of the samples. If the evaluation of the image indicates the presence of crystals, the sample may be re-imaged using a different set of imaging parameters, and the resulting image analyzed to determine its contents. The sample may also be evaluated by generating multiple images of a sample using various sets of imaging parameters, identifying pixels that depict regions of interest in a plurality of images, merging the pixels from each region of interest into a composite image and analyzing the composite image. An image of a sample can also be evaluated by classifying the pixels of the image based on the contents of the sample that they depict, color-coding the pixels using the classifications, and displaying the colored image for analysis.

Image forming device, post-processing device and color calibration method

Abstract: An image forming device includes: a toner image forming unit that forms a toner image onto a recording medium; a controlling unit that controls the toner image forming unit such that a test image is formed on the recording medium; a sheet transfer unit that transfers the recording medium having the toner image formed thereon by the toner image forming unit; a fixing unit that fixes the toner image formed by the toner image forming unit onto the recording medium; an image detecting unit, disposed downstream from the image fixing unit along the sheet transfer unit, that detects the test image formed on the recording medium; and a calibration unit that performs a color calibration process based upon the test image detected by the image detecting unit.

Camera equipped with red-eye correction feature

Abstract: The camera is equipped with a red-eye correction feature. The camera has an imaging unit for acquiring digital image data of a subject, a first folder for storing original image data that has undergone no image processing or default image processing alone on the digital image data, an automatic red-eye correction unit for automatically detecting red-eye on a photographed subject image in the original image data and automatically modifying the thus detected red-eye to obtain corrected digital image data, a second folder for storing at least one of the corrected digital image data and red-eye correction information related to the original image data and used to perform the automatic red-eye correction, the second folder being separate from the first folder.

Image photographing apparatus

Abstract: PROBLEM TO BE SOLVED: To enable an image photographing apparatus such as a digital camera to automatically correct a photographic image. SOLUTION: A digital camera 1 executes image evaluation processing for automatically evaluating a photographic image (exposure condition evaluation, contrast evaluation, blur or focus blur evaluation). Also, when a photographing mode is detected and if the residual capacity of a recording medium for recording the photographic image is not more than a previously set capacity, the digital camera 1 executes correction processing for correcting the image quality of a photographic image whose evaluation value calculated in the image evaluation processing is not more than a predetermined value out of the images recorded in the recording medium. Then, the digital camera 1 evaluates the images subjected to the image correction again to decide the images whose evaluation value calculated in this image evaluation processing is not more than the predetermined value as candidates to be deleted from the recording medium, and deletes the images designated by a user from among the images of decided deletion candidates. COPYRIGHT: (C)2006,JPO&NCIPI

Image data processing apparatus, medium recording image data set, medium recording image data processing program and image data processing method

Abstract: There has been a problem in that original image data should be saved with another name so as not to be altered, resulting in complexity and an increase in a necessary storage region. Image data are housed in a folder managed as a film metaphor and a database of photographic data 30 b corresponding to each image data is prepared. When a desirable image processing is selected for desirable image data, the selected image processing is updated as modification information in the database structure. When display, output or print is actually required, various image processings are executed by referring to modification information on only a work area with original image data left. Therefore, it is possible to easily enjoy image modification or the like with the original image data left as they are.

Captured image projection apparatus and captured image correction method

Abstract: A calligraphy/drawing camera (3) image-captures a document and sends a resultant captured image to a personal computer (S101). The personal computer applies various image processes such as correction of various deformations including a trapezoidal deformation based on image information obtained from the captured image, a rotation process, a cut out process, etc. to the captured image (S102 to S104). The personal computer sends the processed to a projector (S105). The projector projects the processed image on a screen (S106). Since the captured image is corrected based on the image information obtained from the captured image, not only correction of a trapezoidal deformation but also various other corrections can be done on the captured image.

Image processing apparatus, image processing method, and x-ray ct system

Abstract: PROBLEM TO BE SOLVED: To provide an image processing apparatus which can smoothly correct the other portion while maintaining the sharpness of the edge portion of a specific region of a CT image and can quantitatively evaluate a reconstructed image, an image processing method and an X-ray CT system. SOLUTION: The image reconstruction of the CT image is executed from the X-ray projection data scanned by a gantry 100 using an FBP (filter back projection) method and the image quality of a prescribed comparison image is improved by a method for successive approximation. Then, a prescribed ROI is set on the updated comparison image to calculate standard deviation. Here, whether or not the image quality improvement of the comparison image is further necessary is determined and when it is determined to be necessary, the image quality of the above comparison image is further improved by the method for successive approximation. On the other hand, when the image quality improvement is determined to be unnecessary, the above comparison image is outputted as the CT image. COPYRIGHT: (C)2006,JPO&NCIPI

Correction of subject area detection information, and image combining apparatus and method using the correction

Abstract: An image combining method for combining an image obtained by image sensing real space with a computer-generated image and displaying the combined image. Mask area color information is determined based on a first real image including an object as the subject of mask area and a second real image not including the object, and the color information is registered. The mask area is extracted from the real image by using the registered mask area color information, and the real image and the computer-generated image are combined by using the mask area.

Color matching image data

Abstract: A method of matching image color and or luminance characteristics in an image processing system. In order to match an input image with a reference image, a color transformation M is initialised ( 601 ). An output image is copied ( 602 ) from the input image. The following sequence of operations is then repeated: Output and reference images are displayed on a system monitor. The user identifies ( 603 ) a highlight, shadow or overall region in both images. These regions are processed ( 604 ) to identify a difference ( 605 ). The difference is concatenated ( 606 ) onto transformation M. The output image is updated ( 607 ) by processing the input with M.

Digital image classification system

Abstract: Techniques are described to identify and categorize digital images based on the content encoded with the digital image.

Independent component analysis for texture defect detection

Abstract: In this paper, a novel method for texture defect detection is presented The method makes use of Independent Component Analysis (ICA) for feature extraction from the nonoverlapping subwindows of texture images and classifies a subwindow as defective or nondefective according to Euclidean distance between the feature obtained from average value of the features of a defect free sample and the feature obtained from one subwindow of a test image The experimental results demonstrating the use of this method for visual inspection of textile products obtained from a real factory environment are also presented

Image display method and image display system

Abstract: Real space image including a simple prototype created based on three-dimensional CAD data is captured by an image input apparatus. A position/orientation measuring apparatus measures positions and orientations of the image input apparatus and simple prototype. An information processor captures position/orientation information representing the position and orientation of the simple prototype in the image captured by the image input apparatus 102. The information processor further extracts a hand area from an image, renders a three-dimensional computer graphic image on the simple prototype, excluding the extracted hand area, in the image based on the position/orientation information and the three-dimensional CAD data, and synthesizes the image and the three-dimensional computer graphic image. Here, the simple prototype has a different color from a color of the hand area.

Method of extracting image feature, image feature extracting program, imaging device, and image processing device

Abstract: PROBLEM TO BE SOLVED: To extract features, such as a human face in an image, at a high speed as well as at high precision. SOLUTION: In an image feature extracting method wherein images of required size are segmented, one by one from an image to be processed, each segmented image is compared with collation data of a feature image, and whether there is the feature image in the image to be processed is detected, the range of the size of the feature image, in contrast to the size of the image to be processed, is limited based on the information of distance from an object, when the image to be processed is photographed (Step S6). Thus, comparison of too large or too small segmented images, as compared to the feature image for which the collation data can be skipped, and processing at higher speed as well as with higher precision can be attained. COPYRIGHT: (C)2005,JPO&NCIPI

Automatic white balancing using luminance component and standard deviation of RGB components [image preprocessing]

Abstract: Automatic white balancing is an essential image preprocessing component in consumer digital still cameras, and it can greatly improve the final image quality of the captured image. In this paper, a novel automatic white balancing algorithm based on both the luminance component and standard deviation of RGB components of the pre-captured image is proposed. A light source model for evaluation of an automatic white balancing is also described. The simulation results indicate that the proposed algorithm can improve the final image quality of the captured image.

Method and apparatus for automatically correcting image misalignment arising in a rear-projection LCD television display

Abstract: A method and apparatus for projecting an image is provided. The apparatus includes a projector operable to receive a signal representative of an image and to generate light representative of the image. A lens assembly is provided for receiving from the projector the generated light representative of the image and projecting the generated light. The generated light is projected onto a display screen so that the image is visible to a viewer. An image alignment arrangement adjusts a relative position of the image on the display screen. The image alignment arrangement, which is operable to project a test image from the projector onto the display screen, includes at least one sensor located on the display screen for detecting the test image and generating an alignment signal representative of an amount of misalignment of the test image on the display screen. The image alignment arrangement also includes a mechanical actuator for adjusting a relative position of the projector in response to the alignment signal so that the test image is properly aligned on the display screen.

METACOW: A public-domain, high-resolution, fully-digital, noise-free, metameric, extended-dynamic-range, spectral test target for imaging system analysis and simulation

Abstract: Standard, easily accessible, test targets have long served the field of color imaging as a foundation for comparison of the performance of various imaging systems and algorithms and the open and meaningful exchange of research results. This paper details the creation and application of a new digital color test target useful for research and development of color imaging systems. The target has several advantages over previous types of targets that include spatial resolution, dynamic range, spectral resolution, metameric properties, lack of noise, and continuous tonal variations. All these features can be important for visual assessment, computational analysis, and colorimetric evaluation. This target, known as METACOW, is freely available to all performing research in color imaging. Introduction and Objectives Test targets of various sorts have been tremendously helpful in color imaging research and development. Perhaps the most widely used and recognized instantiation is the GretagMacbeth ColorChecker color rendition chart originally designed by McCamy et al.1 approximately 30 years ago. The ColorChecker was designed with an array of 24 color patches that could be easily evaluated visually and instrumentally. The design was such that the spectral reflectance characteristics of the chart, not just the colors, were created to simulate objects of special interest such as blue flowers (notoriously difficult to reproduce with photographic color film), skin tones, and a nonselective gray scale. The ColorChecker has been so widely used, and was so effectively designed, that its 24 color patches can often be considered as memory colors to scientists and engineers in color imaging. More recent evolution of digital photographic systems have prompted the evolution of the ColorChecker to a new second form embodied as the GretagMacbeth ColorChecker DC color reference chart.2 The ColorChecker DC serves similar purposes, but with an order of magnitude more patches (237 instead of 24) it was designed with some of the opensystems properties of digital color imaging in mind. It includes replicate gray scales around the chart to evaluate the spatial uniformity of color balance and tone reproduction. The large number of patches and extended gamut facilitate use of the ColorChecker DC in construction of ICC profiles for scanner or camera characterization. And while reproduction of the ColorChecker DC can be evaluated both visually and instrumentally, visual evaluation is far more difficult with such a large number of color patches. It is safe to assume that the 237 patches of the ColorChecker DC will never become widely recognized memory colors and that the usefulness of the target in psychophysical experiments is somewhat limited. Similar applications are served by test targets such as the well-known ANSI IT8 targets3,4 that consist of large numbers of well-characterized color patches that can be used for scanner characterization and related applications. Again, such targets are of limited practical use in psychophysical evaluation of imaging systems due the large number of patches although some versions have a small area of pictorial content more amenable to human judgement. For visual evaluation of imaging systems or imaging algorithms, standard (actual or de facto) pictorial images are often used by various investigators to allow comparison of research results and combination of data from various studies. Well-known examples include the SCID, standard color image data, and SHIPP, standard high precision picture data, images that are available in digital form in various color representations.5-7 Previous examples provide targets that well represent two categories of need. The first are well defined spectrally and instrumentally but often with too many patches for easy visual evaluation. The second are standard digital images that are well defined colorimetrically and appropriate for visual evaluation, but difficult to assess instrumentally. They are also potentially impacted by the original image capture technology. The objective of the test target described in this paper is two bridge the gap between those two categories and produce an image that can be used in research and development that is both well specified spectrally and contains continuously shaded areas that are useful for perceptual judgements. This image is not intended to replace both of the previous categories, but rather to supplement them with a new type of test image with significant practical utility. Additional objectives in the development of this target include eliminating noise in the target itself, including metameric reflectance pairs, and being of sufficient spatial and photometric resolution to be applicable to the evaluation of essentially any imaging technology. The addition of metameric reflectance pairs alone is a significant advance in practical utility over previous color charts.

Imaging apparatus, image processing apparatus, image processing method of imaging apparatus and computer program

Abstract: A document camera has an image processing apparatus, which acquires the contour of an image of an original by means of a Roberts filter. The image processing apparatus detects straight lines as candidates for forming an image of the original from the acquired contour and acquires the shape of a quadrangle of the original. The image processing apparatus determines the projection parameters showing the relationship between the shape of the image of the original and the shape of the actual original from the positions of the corners of the quadrangle and executes an operation of projection/transformation on the image of the original. The document camera outputs the image data of the image to a projector, which projects an image of the original on a screen according to the image data.

Image forming device, calibration method and storage medium storing program

Abstract: An image forming device includes an image forming unit that forms an image including at least one of a user-requested image and a test image onto a recording medium, a controller that controls the image forming unit to form a test image including a mixed color on the recording medium, a transporting path that transports the recording medium on which the test image is formed by the image forming unit, an image detecting unit disposed at the transporting path that detects the test image formed on the recording medium, and a calibration unit that performs a color calibration process based upon the test image detected by the image detecting unit.

Color transfer to greyscale images using texture spectrum

Abstract: Color transfer between two images is one of the most common tasks in image processing. We present a method of pattern recognition to increase the successful possibility of transferring color from color image to greyscale image. A kind of texture descriptor, texture spectrum, plays an important role in the whole procedure. Source image retrieval is used and based on texture-spectrum similarity, which can find the most suitable color images in a color image database. Furthermore, texture-spectrum is also considered to sample color pixels reasonably. We found that the method works well and it can be successfully applied to a variety of images.

Information measure for assessing pixel-level fusion methods

Abstract: An objective measure for evaluating the performance of pixel level fusion methods is introduced in this work. The proposed measure employs mutual information and conditional mutual information in order to assess and represent the amount of information transferred from the source images to the final fused greyscale image. Accordingly, the common information contained in the source images is considered only once in the formation of the final image. The measure can be used regardless the number of source images or the assumptions about the intensity values and there is no need for an ideal or test image. The experimental results clarify the usefulness of the proposed measure.

Image forming apparatus, image forming method and storage medium

Abstract: An image forming apparatus includes an image forming unit which forms an image on a recording medium, a post-processing unit which performs a post-processing on the recording medium on which the image is formed by the image forming unit, and a post-processing controller which controls the post-processing unit according to whether an image requested by a user or a test image is formed on the recording medium. An image forming method includes, in a case where the requested image and the test image are formed on plural recording media, controlling the post-processing according to whether the requested image or the test image is formed on the recording medium, and according to the control, performing the post-processing on the recording medium on which the requested image or the test image is formed.