Showing papers on "Standard test image published in 2003"

Digital Image Processing Using MATLAB

Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.

Invariant image watermark using Zernike moments

Abstract: The paper introduces a robust image watermark based on an invariant image feature vector. Normalized Zernike moments of an image are used as the vector. The watermark is generated by modifying the vector. The watermark signal is designed with Zernike moments. The signal is added to the cover image in the spatial domain after the reconstruction process. We extract the feature vector from the modified image and use it as the watermark. The watermark is detected by comparing the computed Zernike moments of the test image and the given watermark vector. Rotation invariance is achieved by taking the magnitude of the Zernike moments. An image normalization method is used for scale and translation invariance. The robustness of the proposed method is demonstrated and tested using Stirmark 3.1. The test results show that our watermark is robust with respect to geometrical distortions and compression.

Content-based image copy detection

Abstract: This paper proposes a novel method to detect copied versions of digital images. The proposed copy detection scheme can be used as either an alternative approach or a complementary approach to watermarking. A test image is reduced to an 8×8 sub-image by intensity averaging, and the AC coefficients of its discrete cosine transform (DCT) are used to compute distance from those generated from the query image, of which a user wants to find copies. A challenge is that the replicated image may be processed to elude copy detection or enhance image quality. We show ordinal measure of DCT coefficients, which is based on relative ordering of AC magnitude values and using distance metrics between two rank permutations, is robust to various modifications of the original image. The optimal threshold selection scheme using the maximum a posteriori criterion is also described. The efficacy of the proposed method is extensively tested with both cluster-free and cluster-based detection scheme.

Comparison of some thresholding algorithms for text/background segmentation in difficult document images

Abstract: A number of techniques have previously been proposedfor effective thresholding of document images. In this papertwo new thresholding techniques are proposed andcompared against some existing algorithms.The algorithms were evaluated on four types ofdifficult' document images where considerablebackground noise or variation in contrast and illuminationexists. The quality of the thresholding was assessed usingthe Precision and Recall analysis of the resultant words inthe foreground.The conclusion is that no single algorithm works well forall types of image but some work better than others forparticular types of images suggesting that improvedperformance can be obtained by automatic selection orcombination of appropriate algorithm(s) for the type ofdocument image under investigation.

A no-reference quality metric for measuring image blur

Abstract: In this paper, a method for measuring the perceptual quality of blurred images has been proposed. Here, the amount of image blur is characterized by the average extent of edges in the image, or more specifically the average extent of the slope's spread of an edge in the opposing gradients' directions. The effectiveness of such method is validated using subjective tests on blurred images, including JPEG-2000 coded images, and the experimental results show that the proposed method can provide results that correlate relatively well with human subjective ratings.

Digital image processing composition using face detection information

Abstract: A method of automatic or assisted recomposing of digital image processing uses face detection. Pixels that correspond to a face within a digital image are identified. A re-compositioned image is based on spatial or other parameters of the detected image, particularly in relation to the entire digital image or other portions of the image.

The use of fully polarimetric information for the fuzzy neural classification of SAR images

Abstract: Presents a method, based on a fuzzy neural network, that uses fully polarimetric information for terrain and land-use classification of synthetic aperture radar (SAR) image. The proposed approach makes use of statistical properties of polarimetric data, and takes advantage of a fuzzy neural network. A distance measure, based on a complex Wishart distribution, is applied using the fuzzy c-means clustering algorithm, and the clustering result is then incorporated into the neural network. Instead of preselecting the polarization channels to form a feature vector, all elements of the polarimetric covariance matrix serve as the target feature vector as inputs to the neural network. It is thus expected that the neural network will include fully polarimetric backscattering information for image classification. With the generalization, adaptation, and other capabilities of the neural network, information contained in the covariance matrix, such as the amplitude, the phase difference, the degree of polarization, etc., can be fully explored. A test image, acquired by the Jet Propulsion Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the advantages of the proposed method. It is shown that the proposed approach can greatly enhance the adaptability and the flexibility giving fully polarimetric SAR for terrain cover classification. The integration of fuzzy c-means (FCM) and fast generalization dynamic learning neural network (DLNN) capabilities makes the proposed algorithm an attractive and alternative method for polarimetric SAR classification.

Perfecting the optics within a digital image acquisition device using face detection

Abstract: Within a digital acquisition device, acquisition parameters of a digital image are perfected as part of an image capture process using face detection within said captured image to achieve one or more desired image acquisition parameters. Default values are determined of one or more image attributes of at least some portion of the digital image. Values of one or more camera acquisition parameters are determined. Groups of pixels are identified that correspond to an image of a face within the digitally-captured image. Corresponding image attributes to the groups of pixels are determined. One or more default image attribute values are compared with one or more captured image attribute values based upon analysis of the image of the face. Camera acquisition parameters are then adjusted corresponding to adjusting the image attribute values.

Image Acquisition and Processing with LabVIEW

Abstract: IMAGE TYPES AND FILE MANAGEMENT Types of Images File Types Working with Image Files SETTING UP Cameras Image Acquisition Hardware From Object to Camera Lighting IMAGE ACQUISITION Configuring Your Camera Acquisition Types NI-IMAQ for IEEE 1394 User Solution: Webcam Image Acquisition Other Third-Party Image Acquisition Software Acquiring A VGA Signal TWAIN Image Acquisition User Solution: Combining High-Speed Imaging and Sensors for Fast Event Measurement DISPLAYING IMAGES Simple Display Techniques Displaying Images within Your Front Panel The Image Browser Overlay Tools The Vision Window Tools Palette IMAGE PROCESSING The ROI (Region of Interest) User Solution: Dynamic Microscopy in Brain Research Connectivity Basic Operators User Solution: Image Averaging with LabVIEW Other Tools User Solution: QuickTime for LabVIEW Filters MORPHOLOGY Simple Morphology Theory Practical Morphology The Structuring Element Specific Morphological Functions Case Study: Find and Classify Irregular Objects User Solution: Digital Imaging and Communications in Medicine (DICOM) for LabVIEW IMAGE ANALYSIS Searching and Identifying (Pattern Matching) User Solution: Connector Pin Inspection Using Vision Mathematical Manipulation of Images User Solution: Image Processing with Mathematica Link for LabVIEW Histograms and Histographs User Solution: Automated Instrumentation for the Assessment of Peripheral Vascular Function Intensity Profiles Particle Measurements User Solution: Sisson-Ammons Video Analysis (SAVA) of CBFs Analytical Geometry MACHINE VISION Optical Character Recognition Parsing Human-Machine Information GLOSSARY BIBLIOGRAPHY INDEX

A novel method to extract features for iris recognition system

Abstract: In general, the iris recognition systems have used the wavelet transform as feature extraction techniques. Since the wavelet transform does not have the shift-invariant property, the iris features are inconsistently extracted due to the eye image rotation and the inexact iris localization. In this paper, a novel method to extract features is proposed for iris recognition system. Two types of features are obtained from the discrete wavelet frame decomposition. The first one is the global feature which is insensitive to the iris image deformation. The second one is the local feature which can represent the iris local texture. If the global distance between the test image and the stored one in the database is smaller than the threshold value, it is added to the candidates. And then, local matching is performed by Hamming distance. Experimental results show the proposed system could be used for the personal recognition efficiently.

Method, apparatus, and storage media for image processing

Abstract: To generating a mosaic image by combining a plurality of material images, an original image is divided into tiles and a material image having a characteristic similar to that of an image in each tile is applied to the tile. If more than one version of image data having different resolutions is provided as the image data for the material image, a low-resolution version of the image data is used to calculate a characteristic quantity of the image. Thus, the amount of time required to generate the mosaic image can be reduced.

System and method for image editing using an image stack

Abstract: A system and method for editing images. A simple but powerful image stack is employed in creating an enhanced image from a stack of registered images. This paradigm combines pixels using multi-image operations on the image stack. Image Stacks can help create group photographs, create high dynamic range images, combine images captured under different lighting conditions, remove unwanted objects from images, and combine images captured at different times and with different focal lengths.

Digital imaging system and file format for storage and selective transmission of processed and unprocessed image data

Abstract: An electronic still imaging system employs an image sensor comprised of discrete light sensitive picture elements overlaid with a color filter array (CFA) pattern to produce color image data corresponding to the CFA pattern, an A/D converter for producing digital CFA image data from the color image data, and a memory for storing the digital CFA image data from the picture elements. A processor enables the processing of the digital CFA image data to produce finished image data, and the digital CFA image data and the finished image data are both stored together in an image file. This enables image processing from raw camera data to final output data to be completed in a single, integrated process to provide improved image quality when printing.

Color image forming apparatus and control method therefor

Abstract: A color image forming apparatus includes a density sensor which detects the light reflecting characteristics of an unfixed toner image formed on an image carrier or a transfer material carrier, and a color sensor which detects the light reflecting characteristics of the fixed toner image formed on a transfer material. A test image is formed on the transfer material in accordance with the detection result of the density sensor, and a density control process of controlling image forming conditions is controlled to be executed in accordance with a detection result of detecting the test image by the color sensor.

Index print producing method, image processing system, image processing method and image processing device

Abstract: An image, which is subjected to a non-standard image processing selectively performed in image units in addition to standard image processings performed when a main print is produced, is subject to an image processing equivalent to a processing performed when the main print is produced or characters indicating the contents of the non-standard image processing are added when an index print is produced. If distortion aberration correction processing is performed when the main print is produced, and an image processing equivalent to the distortion aberration correction processing is not performed when the index print is produced, a frame indicating an image range on the main print is superposed and recorded on an index image.

Object recognition based on photometric alignment using RANSAC

Abstract: For object recognition under varying illumination conditions, we propose a method based on photometric alignment. The photometric alignment is known as a technique that models both diffuse reflection components and attached shadows under a distant point light source by using three basis images. However, in order to reliably reproduce these components in a test image, we have to take into account outliers such as specular reflection components and shadows in the test image. Accordingly, our proposed method utilizes Random Sample Consensus (RANSAC), which has been used successfully for estimating basis images. In the present study, we have conducted experiments using the Yale Face Database B and confirmed that a combination of the photometric alignment and RANSAC provides a simple but effective method for object recognition under varying illumination conditions.

Automatic filter selection using image quality assessment

Abstract: We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.

Method of correcting for distortion of projected image, distortion correcting program used in same method, and projection-type image display device

Abstract: A distortion correcting method is provided which is capable of correcting for distortions of projected images without a need for additionally placing a display unit or a test image displaying unit and by a low-cost configuration and by a simple operation. The distortion correcting method includes a first step of moving a pointer on a screen according to operations of an operator and of sequentially displaying correction reference points which correspond to correction points for a projected image and being designated by operations of the operator on the screen, and then of displaying a correction contour frame on the screen, wherein the correction contour frame is obtained by connecting at least two being adjacent to each other out of the correction reference points, a second step of determining the correction contour frame according to an instruction for determining the correction contour frame from the operator and of calculating a correction parameter according to a distance between each of the correction points for the projected image and the correction reference points of the correction contour frame corresponding to each of the correction points, and a third step of correcting for distortions of projected images based on the correction parameter.

Digital still camera and image correction method

Abstract: An image picked up can be corrected to an image desired by a user by using only a digital still camera There are provided an RAM (15) for holding image data of an image on which gradation correction is to be executed, by a format of a standard color space, and a gradation correction circuit (35) The image data is read out from the RAM (15) and the read-out image data is subjected to gradation correction by the gradation correction circuit (35) Automatic gradation correction statistically analyzes the luminance signal of the image and categorizes the image so as to perform correction by using an appropriate correction curve

No-reference JPEG-2000 image quality metric

Abstract: In this paper, a method for measuring the perceptual image quality of JPEG-2000 coded images has been proposed. The image quality is characterized by the average edge-spread in the image, or more specifically the average extent of the slope's spread of an edge in the opposing gradients' directions. The proposed method is, in effect, a way of measuring the amount of blurring in the image. The effectiveness of such method is validated using subjective tests and the experimental results show that the proposed method can provide results that correlate relatively well with human subjective ratings.

Image data processing apparatus for and image data processing method of pattern matching

Abstract: A pattern matching technique provides a small number of circuits for flexibly performing a pattern match on image data in various two-dimensional matrix formats at a high speed The pattern matching technique also provides a high-speed image area separation and to promote the stability in the image area determination

Method, apparatus and program for image processing

Abstract: Image processing is carried out accurately on image data obtained by a digital camera, for a high-quality reproduced image. Reading means 21 reads image data S0 and main part information acquisition means 22 obtains main part information M. Scene inference means 23 obtains scene information H by inferring a photography scene based on the main part information M and photography information T added to the image data S0. Tone processing condition setting means 25 reads from a memory 24 a tone processing condition K0 corresponding to the scene information H input thereto, and sets a tone processing condition K1 by correcting a reference tone processing condition B according to the tone processing condition K0. Image processing means 28 carries out image processing on the image data S0 according to the tone processing condition K1 and image processing conditions G, and generates processed image data S1.

Testing liquid crystal microdisplays

Abstract: By rendering a special test image and applying flat-field correction for a device under test (DUT) non-uniformity, the E-O response of a reflective LCOS microdisplay can be quickly determined through an image processing algorithm. The measurement is made in a spatial domain instead of in a temporal domain. From the measurement, the driving voltage of maximum brightness, Vbright, can be determined. The use of Vbright enhances the visibility of pixel and sub-pixel defects to the test system. Other defect visibility enhancements are achieved through appropriate sampling rate, optical axis rotation and improved parallelism between the DUT and the CCD sensor camera. By modeling a sub-pixel defect as a local non-uniformity, a near neighborhood algorithm may be used for detection. The neighborhood algorithm does not rely on the alignment between the display pixels and the camera pixels.

Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms

Abstract: The aim of this study was to determine the visually lossless threshold of a wavelet-based compression algorithm in case of microcalcification cluster detection in mammography. The threshold was determined by means of observer performance using a set of digitized mammograms. In addition, the transfer characteristics of the compression algorithm were assessed by means of image-quality parameters using computer-generated test images. The observer performance study was based on rating performed by four independent radiologists, who reviewed 68 mammograms, from the Digital Database for Screening Mammography (DDSM), at six different compression ratios. Receiver operating characteristics (ROC) analysis was performed on observers' responses and the area under ROC curve (Az) was calculated at each compression ratio for each observer. The parameters used for assessment of transfer characteristics of the compression algorithm were input/output response, noise, high-contrast response, and low-contrast-detail response. The computer-generated test image, used for this assessment, mimicked mammographic image characteristics (pixel size, pixel depth, and noise) as well as microcalcification characteristics (size and contrast). The ROC analysis for microcalcification cluster detection indicated a threshold at compression ratio 40:1, as Student's t-test shows statistically significant differences in Az values (p<0.05) for compression ratios 70:1 and 100:1. Observers' grading of mammogram quality lowers this threshold at 25:1. Low-contrast-detail detectability in the transfer characteristics study indicate a threshold of 35:1, whereas non-perceptibility of image-quality-parameters degradation lowers this threshold to 30:1. The ROC and transfer characteristics analysis provided comparable thresholds, indicating the potential use of the latter in limiting the target range of compression ratios for subsequent observer studies.

Image printing apparatus, image printing system and control method

Abstract: An image printing apparatus and its control method for printing an image based on image data from an image data source. In a case where a correction condition is set for the image data, if the image data is a new format file image data including predetermined information, correction processing is performed in accordance with the set correction condition. If the image data is not a new format file image data, the correction processing in accordance with the correction condition is not performed.

Neuron recognition by parallel Potts segmentation

Abstract: Identifying neurons and their spatial coordinates in images of the cerebral cortex is a necessary step in the quantitative analysis of spatial organization in the brain. This is especially important in the study of Alzheimer's disease (AD), in which spatial neuronal organization and relationships are highly disrupted because of neuronal loss. To automate neuron recognition by using high-resolution confocal microscope images from human brain tissue, we propose a recognition method based on statistical physics that consists of image preprocessing, parallel image segmentation, and cluster selection on the basis of shape, optical density, and size. We segment a preprocessed digital image into clusters by applying Monte Carlo simulations of a q-state inhomogeneous Potts model. We then select the range of Potts segmentation parameters to yield an ideal recognition of simplified objects in the test image. We apply our parallel segmentation method to control individuals and to AD patients and achieve recognition of 98% (for a control) and 93% (for an AD patient), with at most 3% false clusters.

Image matching system using 3-dimensional object model, image matching method, and image matching program

Abstract: Even when a small number of reference images are available for each object, it is possible to search at high speed a reference image stored in a database from an input image of an object imaged with a different pose and a different illumination condition. A reference image matching result storage section (50) inputs reference images from a reference image storage section (70) and stores in advance results of matching of the input images with representative 3-dimensional object models of a representative 3-dimensional object model storage section (20). According to each representative 3-dimensional object model, image generation means (30) generates a comparison image having an input condition similar to the input image obtained from the image input means (10). Image matching means (40) calculates similarity between the input image and the image generated. Result matching means (60) calculates similarity between the matching result of the image matching means (40) and the reference image stored in the reference image matching result storage section (50), extracts reference images having similar matching results in the descending order of the similarity, and displays them on result display means (80).

Method and apparatus for testing image sensors

Abstract: Methods and apparatuses for testing image sensors are disclosed. Desirable apparatuses of the present invention include image sensor testing devices comprising a digital light projection system capable of projecting static or dynamic images onto an image sensing device under test and an image sensor signal detection means for analyzing the output of said image sensing device under test. The digital light projection system comprises a light source, collimating optics, a digital micromirror device, and focusing optics. Other desirable methods and apparatuses of the present invention include image sensor testing devices employing a digital light projection system capable of simultaneously testing a plurality of image sensors. According to the present invention, the light source is calibrated and converted to a desired test image by the digital micromirror device. The test image is then focused onto an image sensor, the output of which is read by a detector and correlated with the input digital test image.