Showing papers on "Pixel published in 2001"

Unsupervised segmentation of color-texture regions in images and video

Abstract: A method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for "good" segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image," in which high and low values correspond to possible boundaries and interiors of color-texture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video.

Contour and Texture Analysis for Image Segmentation

Abstract: This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.

Improved wavelet-based watermarking through pixel-wise masking

Abstract: A watermarking algorithm operating in the wavelet domain is presented. Performance improvement with respect to existing algorithms is obtained by means of a new approach to mask the watermark according to the characteristics of the human visual system (HVS). In contrast to conventional methods operating in the wavelet domain, masking is accomplished pixel by pixel by taking into account the texture and the luminance content of all the image subbands. The watermark consists of a pseudorandom sequence which is adaptively added to the largest detail bands. As usual, the watermark is detected by computing the correlation between the watermarked coefficients and the watermarking code, and the detection threshold is chosen in such a way that the knowledge of the watermark energy used in the embedding phase is not needed, thus permitting one to adapt it to the image at hand. Experimental results and comparisons with other techniques operating in the wavelet domain prove the effectiveness of the new algorithm.

Solid-state time-of-flight range camera

Abstract: The concept of a real-time range camera without moving parts is described, based on the time-of-flight (TOF) principle. It operates with modulated visible and near-infrared radiation, which is detected and demodulated simultaneously by a 2-D array of lock-in pixels employing the charge-coupled device principle. Each pixel individually measures the amplitude, offset and phase of the received radiation. The theoretical resolution limit of this TOF range camera is derived, which depends on the square root of the detected background radiation and the inverse of the modulation amplitude. Actual measurements of 3-D sequences acquired at 10 range images per second show excellent agreement between our theory and the observed results. A range resolution of a few centimeters over a range of 10 m, with an illumination power of a few hundreds of milliwatts is obtained in laboratory scenes for noncooperative, diffusely reflecting objects.

Reliable detection of LSB steganography in color and grayscale images

Abstract: A large number of commercial steganographic programs use the Least Significant Bit embedding (LSB) as the method of choice for message hiding in 24-bit, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, we describe a new very accurate and reliable method that can detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8-bit grayscale or color images. It is based on our previous work on lossless data embedding [1]. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", we can reliably detect messages as short as 0.03bpp.

Automatic image segmentation by integrating color-edge extraction and seeded region growing

Abstract: We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.

Handling occlusions in dense multi-view stereo

Abstract: While stereo matching was originally formulated as the recovery of 3D shape from a pair of images, it is now generally recognized that using more than two images can dramatically improve the quality of the reconstruction. Unfortunately, as more images are added, the prevalence of semi-occluded regions (pixels visible in some but not all images) also increases. We propose some novel techniques to deal with this problem. Our first idea is to use a combination of shiftable windows and a dynamically selected subset of the neighboring images to do the matches. Our second idea is to explicitly label occluded pixels within a global energy minimization framework, and to reason about visibility within this framework so that only truly visible pixels are matched. Experimental results show a dramatic improvement using the first idea over conventional multibaseline stereo, especially when used in conjunction with a global energy minimization technique. These results also show that explicit occlusion labeling and visibility reasoning do help, but not significantly, if the spatial and temporal selection is applied first.

Image-based modeling and photo editing

Abstract: We present an image-based modeling and editing system that takes a single photo as input. We represent a scene as a layered collection of depth images, where each pixel encodes both color and depth. Starting from an input image, we employ a suite of user-assisted techniques, based on a painting metaphor, to assign depths and extract layers. We introduce two specific editing operations. The first, a “clone brushing tool,” permits the distortion-free copying of parts of a picture, by using a parameterization optimization technique. The second, a “texture-illuminance decoupling filter,” discounts the effect of illumination on uniformly textured areas, by decoupling large- and small-scale features via bilateral filtering. Our system enables editing from different viewpoints, extracting and grouping of image-based objects, and modifying the shape, color, and illumination of these objects.

CMOS active pixel sensor type imaging system on a chip

Abstract: Single substrate device is formed to have an image acquistition device and a controller. The controller on the substrate controls the system operation.

Medical imaging, diagnosis, and therapy using a scanning single optical fiber system

Abstract: An integrated endoscopic image acquisition and therapeutic delivery system for use in minimally invasive medical procedures (MIMPs). The system uses directed and scanned optical illumination provided by a scanning optical fiber or light waveguide that is driven by a piezoelectric or other electromechanical actuator included at a distal end of an integrated imaging and diagnostic/therapeutic instrument. The directed illumination provides high resolution imaging, at a wide field of view (FOV), and in full color that matches or excels the images produced by conventional flexible endoscopes. When using scanned optical illumination, the size and number of the photon detectors do not limit the resolution and number of pixels of the resulting image. Additional features include enhancement of topographical features, stereoscopic viewing, and accurate measurement of feature sizes of a region of interest in a patient's body that facilitate providing diagnosis, monitoring, and/or therapy with the instrument.

Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching

Abstract: Reports on the design and test of an image processing algorithm for the localization of the optic disk (OD) in low-resolution (about 20 /spl mu//pixel) color fundus images The design relies on the combination of two procedures: 1) a Hausdorff-based template matching technique on edge map, guided by 2) a pyramidal decomposition for large scale object tracking The two approaches are tested against a database of 40 images of various visual quality and retinal pigmentation, as well as of normal and small pupils An average error of 7% on OD center positioning is reached with no false detection In addition, a confidence level is associated to the final detection that indicates the "level of difficulty" the detector has to identify the OD position and shape

A monolithic active pixel sensor for charged particle tracking and imaging using standard VLSI CMOS technology

Abstract: A novel Monolithic Active Pixel Sensor (MAPS) for charged particle tracking made in a standard CMOS technology is proposed. The sensor is a photodiode, which is readily available in a CMOS technology. The diode has a special structure, which allows the high detection efficiency required for tracking applications. The partially depleted thin epitaxial silicon layer is used as a sensitive detector volume. Semiconductor device simulation, using either ToSCA based or 3-D ISE-TCAD software packages shows that the charge collection is efficient, reasonably fast (order of 100 ns), and the charge spreading limited to a few pixels only. A first prototype has been designed, fabricated and tested. It is made of four arrays each containing 64×64 pixels, with a readout pitch of 20 μm in both directions. The device is fabricated using standard submicron 0.6 μm CMOS process, which features twin-tub implanted in a p-type epitaxial layer, a characteristic common to many modern CMOS VLSI processes. Extensive tests made with soft X-ray source ( 55 Fe) and minimum ionising particles (15 GeV/ c pions) fully demonstrate the predicted performances, with the individual pixel noise (ENC) below 20 electrons and the Signal-to-Noise ratio for both 5.9 keV X-rays and Minimum Ionising Particles (MIP) of the order of 30. This novel device opens new perspectives in high-precision vertex detectors in Particle Physics experiments, as well as in other application, like low-energy beta particle imaging, visible light single photon imaging (using the Hybrid Photon Detector approach) and high-precision slow neutron imaging.

Hyperspectral subpixel target detection using the linear mixing model

Abstract: Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. Over the past several years, different algorithms for the detection of full-pixel or subpixel targets with known spectral signature have been developed. The authors take a closer and more in-depth look at the class of subpixel target detection algorithms that explore the linear mixing model (LMM) to characterize the targets and the interfering background. Sensor noise is modeled as a Gaussian random vector with uncorrelated components of equal variance. The paper makes three key contributions. First, it provides a complete and self-contained theoretical derivation of a subpixel target detector using the generalized likelihood ratio test (GLRT) approach and the LMM. Some other widely used algorithms are obtained as byproducts. The performance of the resulting detector, under the postulated model, is discussed in great detail to illustrate the effects of the various operational factors. Second, it introduces a systematic approach to investigate how well the adopted model characterizes the data, and how robust the detection algorithm is to model-data mismatches. Finally, it compares the derived algorithms with regard to two desirable properties: capacity to operate in constant false alarm rate mode and ability to increase the separation between target and background.

A 10000 frames/s CMOS digital pixel sensor

Abstract: A 352/spl times/288 pixel CMOS image sensor chip with per-pixel single-slope ADC and dynamic memory in a standard digital 0.18-/spl mu/m CMOS process is described. The chip performs "snapshot" image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4/spl times/9.4 /spl mu/m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 /spl mu/V/e/sup -/. At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

Image sensing apparatus

Abstract: It is an object of this invention to provide an image sensing apparatus capable of forming a high-resolution image by increasing the number of final output pixels. To achieve this object, a digital color camera has a plurality of image sensing units for receiving object images via different apertures. These image sensing units are so arranged that images of an object at a predetermined distance are received as they are shifted a predetermined amount from each other in at least the vertical direction. Further, these image sensing units have filters having different spectral transmittance characteristics.

A fast direct Fourier-based algorithm for subpixel registration of images

Abstract: This paper presents a new direct Fourier-based algorithm for performing image-to-image registration to subpixel accuracy, where the image differences are restricted to translations and uniform changes of illumination. The algorithm detects the Fourier components that have become unreliable estimators of shift due to aliasing, and removes them from the shift-estimate computation. In the presence of aliasing, the average precision of the registration is a few hundredths of a pixel. Experimental data presented here show that the new algorithm yields superior registration precision in the presence of aliasing when compared to several earlier methods and has comparable precision to the iterative method of P. Thevenaz et al. (1998).

Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data

Abstract: A characteristic intensity of a feature in image data generated by scanning a microarray probe is determined (304). A set of pixels of the image data that nominally represent the feature is identified. The pixels each have a value (such as an intensity value) associated therewith. For each of a plurality of subsets of the set of pixels, a variation statistic value is determined that corresponds to a variation in the values associated with the pixels of that subset (Fig.3).

A global matching framework for stereo computation

Abstract: This paper presents a new global matching framework for stereo computation. In this framework, the second view is first predicted from the reference view using the depth information. A global match measure is then defined as the similarity function between the predicted image and the actual image. Stereo computation is converted into a search problem where the goal is to find the depth map that maximizes the global match measure. The major advantage of this framework is that the global visibility constraint is inherently enforced in the computation. This paper explores several key components of this framework including (1) three color segmentation based depth representations, (2) an incremental warping algorithm that dramatically reduces the computational complexity, and (3) scene constraints such as the smoothness constraint and the color similarity constraint. Experimental results using different types of depth representations are presented. The quality of the computed depth maps is demonstrated through image-based rendering from new viewpoints.

Object segmentation with background extraction and moving boundary techniques.

Abstract: Image data for an image frame are allocated among three groups. In one group are image data which are part of a derived motion boundary, along with image data which differ by at least a threshold amount from a corresponding point among normalized background data. In another group are image data which closely correspond to the normalized background image data. A third group includes the remaining pixels. An initial estimate for the object boundary is adjusted iteratively based on the groupings, followed by application of an active contour model.

Image transformation and synthesis methods

Abstract: In a telepresence system, a scene is captured by recording pixel data elements, each associated with a pixel ray vector having a direction and an intercept on an known locus in the frame of reference of the scene. Each pixel data element includes data representing the illumination along the pixel ray vector. For example, the pixel data elements may be captured by operating numerous video cameras pointing in different directions on a spherical locus. A virtual viewpoint image representing the image which would be seen from an arbitrary viewpoint, looking in an arbitrary direction, can be synthesized by determining the directions of synthetic pixel ray vectors from each pixel of the virtual viewpoint image through the virtual viewpoint and the intercepts of these vectors on the locus. Recorded pixel data elements having pixel ray vector directions and intercepts close to those of the synthetic pixel ray vector can be copied or interpolated to provide data representing illumination in the synthetic pixel.

A background model initialization algorithm for video surveillance

Abstract: Many motion detection and tracking algorithms rely on the process of background subtraction, a technique which detects changes from a model of the background scene. We present a new algorithm for the purpose of background model initialization. The algorithm takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene. Multiple hypotheses of the background value at each pixel are generated by locating periods of stable intensity in the sequence. The likelihood of each hypothesis is then evaluated using optical flow information from the neighborhood around the pixel, and the most likely hypothesis is chosen to represent the background. Our results are compared with those of several standard background modeling techniques using surveillance video of humans in indoor environments.

Optical pseudo trackball controls the operation of an appliance or machine

Abstract: An optical fingertip tracker, which may be a pseudo trackball, responds to the motion of the texture on one of the user's digits pressed against an image input aperture to produce motion signals processed within an appliance to be controlled. The appliance may have a minimal GUI, such as an LCD display having only a modest number of low resolution pixel locations and/or preestablished characters or icons. An appliance control parameter is set or adjusted in response to translation of the fingertip along a particular axis. E.g., to enter a parameter value (volume) a changing bar graph can be displayed. The display might even be omitted; to set the tone control of an audio appliance it can be sufficient to simply adjust the tone until the result is as desired. A numeric display of control parameter digits may be desirable, as when moving the fingertip tunes a receiver. There can be several particular axes along which fingertip movement changes an associated appliance control parameter. Other appliances can include character recognition of input characters traced by the fingertip. A pressure sensitive switch coupled to the fingertip tracker can serve as a delimiting mechanism useful in ratification of interpreted results and in editing strings that have been input. Excursions is a loss-of-correlation signal can be detected and interpreted as tapping the image input aperture with the fingertip. Such “taps” and “double taps” can be defined, and have selected meanings associated therewith that may be used in conjunction with, or in place of, the pressure sensitive switch.

Segmentation of Fingerprint Images

Abstract: An important step in an automatic fingerprint recognition system is the segmentation of fingerprint images. The task of a fingerprint segmentation algorithm is to decide which part of the image belongs to the foreground, originating from the contact of a fingertip with the sensor, and which part to the background, which is the noisy area at the borders of the image. In this paper, an algorithm for the segmentation of fingerprints is presented. The method uses three pixel features, being the coherence, the mean and the variance. An optimal linear classifier is trained for the classification per pixel, while morphology is applied as postprocessing to obtain compact clusters and to reduce the number of classification errors. Manual inspection shows that the proposed method provides accurate high-resolution segmentation results. Only 6.8% of the pixels is misclassified while the postprocessing further reduces this ratio. Experiments show that the proposed segmentation method and manual segmentation perform equally well in rejecting false fingerprint features from the noisy background. Keywords— Image processing, fingerprint image segmentation, pixel features, coherence, linear classification, neural network.

Shadow identification and classification using invariant color models

Abstract: A novel approach to shadow detection is presented. The method is based on the use of invariant color models to identify and to classify shadows in digital images. The procedure is divided into two levels: first, shadow candidate regions are extracted; then, by using the invariant color features, shadow candidate pixels are classified as self shadow points or as cast shadow points. The use of invariant color features allows a low complexity of the classification stage. Experimental results show that the method succeeds in detecting and classifying shadows within the environmental constrains assumed as hypotheses, which are less restrictive than state-of-the-art methods with respect to illumination conditions and the scene's layout.

Statistical metering and filtering of content via pixel-base metadata

Abstract: Data supplied to a display having a plurality of pixels comprises both content to be displayed and metadata that identifies the content of the respective pixel as being of a particular type by setting the metadata for each pixel to a value that is one of a predefined set of values. The identification of the content as being of a particular type enables the classification of the pixels on a per pixel basis into one or more categories. Pixels containing data for an advertisement can be identified and metered to determine the total display space they occupy and length of time they are displayed, which are both considered measures of the effectiveness of the advertisement. This metering can therefore be used to more equitably charge for advertising on web pages because an advertiser can be charged on the basis of what a user actually sees on the display. If only a percentage of the advertisement is visible, the advertiser pays an amount in proportion to the percentage of the advertisement that is visible. Pixels belonging to an advertisement can also be metered by a video game in a manner that gives an incentive to a game player to view advertisements. Additionally, metadata can be used to identify a pixel as containing objectionable content or some other information or type of content not desired by a user. The pixels containing objectionable or undesired content, which usually are pixels that display an object or portions thereof, can then be filtered out of the picture in a more precise way to either delete or leave a blurred image of the object or objectionable/undesirable portion thereof only, without hiding an entire screen of data.

Optimum image thresholding via class uncertainty and region homogeneity

Abstract: Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum thresholds has remained a challenge over decades. Besides being a segmentation tool on its own, often it is also a step in many advanced image segmentation techniques in spaces other than the image space. We introduce a thresholding method that accounts for both intensity-based class uncertainty-a histogram-based property-and region homogeneity-an image morphology-based property. A scale-based formulation is used for region homogeneity computation. At any threshold, intensity-based class uncertainty is computed by fitting a Gaussian to the intensity distribution of each of the two regions segmented at that threshold. The theory of the optimum thresholding method is based on the postulate that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The main idea here is to select that threshold at which pixels with high class uncertainty accumulate mostly around object boundaries. To achieve this, a threshold energy criterion is formulated using class-uncertainty and region homogeneity such that, at any image location, a high energy is created when both class uncertainty and region homogeneity are high or both are low. Finally, the method selects that threshold which corresponds to the minimum overall energy. The method has been compared to a maximum segmented image information method. Superiority of the proposed method was observed both qualitatively on clinical medical images as well as quantitatively on 250 realistic phantom images generated by adding different degrees of blurring, noise, and background variation to real objects segmented from clinical images.

Noise filter for bayer pattern image data

Abstract: A method of filtering an image filter is disclosed. The filter is provided for a digital camera including image sensors sensitive to light, a color filter placed over sensitive elements of the sensors and patterned according to a Bayer mosaic pattern layout and an interpolation algorithm joining together the digital information provided by differently colored adjacent pixels in said Bayer pattern. The filter is adaptive and includes a noise level computation block for operating directly on a said Bayer pattern data set of for each color channel thus removing noise while simultaneously preserving picture detail.

Encoding and decoding a message within an image

Abstract: A method for decoding a message embedded in a pattern of pixels. The method includes the steps of determining the pixel values for pixels from the pattern of pixels, determining binary values from the pixel values for pixels from the pattern of pixels; and determining the embedded message from the binary values. The pixels have a range of pixel values between a maximum and a minimum. The pixels are divided into cells each having glyph cell and background pixels. The binary value of a glyph pixel is determined by the contrast the glyph pixel has with its background pixels. The method can be used to decode embedded web-site address from an image with a foreground image and the embedded web-site address.

Color image enhancement via chromaticity diffusion

Abstract: A novel approach for color image denoising is proposed in this paper. The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. We present the underlying theory, a number of examples, and briefly compare with the current literature.