An optimal linear operator for step edge detection

Shen, Jun; Castan, Serge

doi:10.1016/1049-9652(92)90060-b

Cited by 318 publications

(188 citation statements)

References 9 publications

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“…Previous work on boundary detection and image segmentation include the filtering approaches to detect intensity/color discontinuities [5], [8], [14], [25], [32], [36], [37], active contour models [6], [18], [30], region growing and merging algorithms Fig. 1.…”

Section: Previous Workmentioning

confidence: 99%

“…Much of the research on edge detection has been devoted to the development of optimal edge detectors which provide the best trade-off between the detection and localization performance [5], [8], [25], [32]. A common strategy in designing such edge operators is to find the filter which optimizes the performance with respect to the three criteria: good detection, good localization, and a unique response to a single edge.…”

Section: A Edge Detectionmentioning

confidence: 99%

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EdgeFlow: a technique for boundary detection and image segmentation

Manjunath

2000

IEEE Trans. on Image Process.

280

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Abstract-A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. Segmentation results on a large and diverse collections of natural images are provided, demonstrating the usefulness of this method to content based image retrieval.

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Section: Previous Workmentioning

confidence: 99%

Section: A Edge Detectionmentioning

confidence: 99%

EdgeFlow: a technique for boundary detection and image segmentation

Manjunath

2000

IEEE Trans. on Image Process.

280

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“…The ¦rst is to compute centroids of extended objects. The approach is to use Shen Castan edge detection [11] to ¦nd the limb of a planetary image and to compute the centroids from curve ¦ts to the limb. This permits partial disks, such as those due to phasing, to be used.…”

Section: Navigationmentioning

confidence: 99%

Integrated communications and optical navigation system

Mueller

Pajer

Paluszek

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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The Integrated Communications and Optical Navigation System (ICONS) is a §exible navigation system for spacecraft that does not require global positioning system (GPS) measurements. The navigation solution is computed using an Unscented Kalman Filter (UKF) that can accept any combination of range, range-rate, planet chord width, landmark, and angle measurements using any celestial object. Both absolute and relative orbit determination is supported. The UKF employs a full nonlinear dynamical model of the orbit including gravity models and disturbance models. The ICONS package also includes attitude determination algorithms using the UKF algorithm with the Inertial Measurement Unit (IMU). The IMU is used as the dynamical base for the attitude determination algorithms. This makes the sensor a more capable plug-in replacement for a star tracker, thus reducing the integration and test cost of adding this sensor to a spacecraft. Recent additions include an integrated optical communications system which adds communications, and integrated range and range rate measurement and timing. The paper includes test results from trajectories based on the NASA New Horizons spacecraft. NOMENCLATUREa planet radius or half distance between landmarks f focal length k 1 ¦rst-order optical distortion coe©cient in the imager focal plane k 2 second-order distortion coe©cient in the imager focal plane l i vector from the Sun to the planet center or the center of a straight line joining two landmarks o x image center x coordinate o y image center y coordinate p c the position in the camera frame p w the position in the world frame R transforms from the world to the camera frame

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“…3G-J). 17 Segmentation accuracy. The quality of a nuclear mask is inspected visually by superimposing it on the histone or pixel maximum image ( Fig.…”

Section: Image Segmentationmentioning

confidence: 99%

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Surkova

Myasnikova²,

Janssens³

et al. 2008

Fly

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We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns. IntroductionBiology is increasingly asking quantitative questions. Quantification is essential to understand the principles of organism functioning. Modern physics and engineering provide tools and strategies for accurate measurements and the acquisition of comprehensive and consistent data sets. For example, microarrays are widely used to quantify the levels of gene expression, 1 and fluorescence restoration after photobleaching (FRAP) is applied to measure diffusion rate or molecular transport. 2 In the last decade developmental biology has achieved tremendous progress. Due to the success of genetics and functional genomics a large number of genes controlling development has been cloned and sequenced, the products of these genes have been identified, and the function of many of these molecules has been revealed. However, despite of these spectacular achievements the integrative picture of how an organism controls the phenotype of tissues and organs is still absent.The morphogenetic field is a basic unit of ontogeny. 3 This physically detached area is formed by complex coordinated interactions of transcripts and proteins that make up the field. Understanding the developmental processes within the morphogenetic field requires a quantitative characterization of the dynamics of each of the field components.Currently available methods for quantification of gene expression like microarrays, quantitative PCR and CAT assays have some limitations for investigations of developmental processes. All of these methods are based on the preparation of homogenates of cells and are unable to capture spatial information about gene expression.The study of development in morphogenetic fields requires methods for the acquisition of gene expression data that will allow monitoring the expression time course of all of the genes simultaneously at the resolution of a single cell. Confocal scanning microscopy of fluorescently tagged molecules constitute the most common data acquisition strategy, which preserves spatial information about the distribution of gene products. This method involves the recognition of protein or RNA in situ by target-specific primary antibodies or a labeled antisense RNA probe. The detection method is usually based on the use of secondary antibodies conjugated with a fluorophore. Confocal microscopy provides high quality digital images ready for computer pr...

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An optimal linear operator for step edge detection

Cited by 318 publications

References 9 publications

EdgeFlow: a technique for boundary detection and image segmentation

EdgeFlow: a technique for boundary detection and image segmentation

Integrated communications and optical navigation system

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

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