Abstract: An active optical detection system includes an optical transmitter configured to transmit light in a signal pattern and an optical receiver configured to receive light and determine a correlation between the received light and the signal pattern. The correlation of the received light and the signal pattern is indicative of range to an object.

Abstract: An active optical detection system includes an optical transmitter configured to transmit light in a signal pattern and an optical receiver configured to receive light and determine a correlation between the received light and the signal pattern. The correlation of the received light and the signal pattern is indicative of range to an object.

Abstract: A device for detecting a presence of an object includes an optical phased array, a detector, a processing portion and an indicator. The optical phased array can transmit a first optical beam to a first location at a first time and can transmit a second optical beam to a second location at a second time. The detector can detect a first reflected beam based on the first optical beam and can detect a second reflected beam based on the second optical beam. The processing portion can determine the presence of the object based on the first reflected beam and the second reflected beam. The indicator can generate an indicator signal based on the presence of the object.

Abstract: A device for detecting a presence of an object includes an optical phased array, a detector, a processing portion and an indicator. The optical phased array can transmit a first optical beam to a first location at a first time and can transmit a second optical beam to a second location at a second time. The detector can detect a first reflected beam based on the first optical beam and can detect a second reflected beam based on the second optical beam. The processing portion can determine the presence of the object based on the first reflected beam and the second reflected beam. The indicator can generate an indicator signal based on the presence of the object.

Abstract: A device for detecting a presence of an object includes an optical phased array, a detector, a processing portion and an indicator. The optical phased array can transmit a first optical beam to a first location at a first time and can transmit a second optical beam to a second location at a second time. The detector can detect a first reflected beam based on the first optical beam and can detect a second reflected beam based on the second optical beam. The processing portion can determine the presence of the object based on the first reflected beam and the second reflected beam. The indicator can generate an indicator signal based on the presence of the object.

Abstract: A device for detecting a presence of an object includes an optical phased array, a detector, a processing portion and an indicator. The optical phased array can transmit a first optical beam to a first location at a first time and can transmit a second optical beam to a second location at a second time. The detector can detect a first reflected beam based on the first optical beam and can detect a second reflected beam based on the second optical beam. The processing portion can determine the presence of the object based on the first reflected beam and the second reflected beam. The indicator can generate an indicator signal based on the presence of the object.

Abstract: A method for segmenting a small feature in a multidimensional digital array of intensity values in a data processor computes an edge metric along each ray of a plurality of multidimensional rays originating at a local intensity extreme (local maximum or minimum). A multidimensional point corresponding to a maximum edge metric on each said ray is identified as a ray edge point. Every point on each ray from the local extreme to the ray edge point is labeled as part of the small object. Further points on the feature are grown by labeling an unlabeled point if the unlabeled point is adjacent to a labeled point, and the unlabeled point has a more extreme intensity than the labeled point, and the unlabeled point is closer than the labeled point to the local extreme. The resulting segmentation is quick, and identifies boundaries of small features analogous to boundaries identified by human analysts, and does not require statistical parameterizations or thresholds manually determined by a user.

Abstract: A method for segmenting a small feature in a multidimensional digital array of intensity values in a data processor computes an edge metric along each ray of a plurality of multidimensional rays originating at a local intensity extreme (local maximum or minimum). A multidimensional point corresponding to a maximum edge metric on each said ray is identified as a ray edge point. Every point on each ray from the local extreme to the ray edge point is labeled as part of the small object. Further points on the feature are grown by labeling an unlabeled point if the unlabeled point is adjacent to a labeled point, and the unlabeled point has a more extreme intensity than the labeled point, and the unlabeled point is closer than the labeled point to the local extreme. The resulting segmentation is quick, and identifies boundaries of small features analogous to boundaries identified by human analysts, and does not require statistical parameterizations or thresholds manually determined by a user.

Abstract: The invention is a method and apparatus for automated detection of small structures in images. One specific use is to detect malignant microcalcification clusters in mammograms. A digitized and filtered mammogram image is stored in a computer. Seed pixels, which are pixels that are brighter than their immediate neighbors, are identified to indicate candidate structures and used to construct two regions. Various features are then measured using the two regions around each seed point. The features characterize each candidate structure and are input to a classifier, such as a neural network. The classifier then distinguishes between structures of interest and background. The structures detected by the classifier are then presented to a clustering algorithm. A detected structure that is less than a threshold distance away from the nearest structure and a cluster is included in that cluster. Finally, the results are displayed, either on a monitor or on hard copy, with a frame around the detected cluster.

Abstract: The invention is a method and apparatus for automated detection of small structures in images. One specific use is to detect malignant microcalcification clusters in mammograms. A digitized and filtered mammogram image is stored in a computer. Seed pixels, which are pixels that are brighter than their immediate neighbors, are identified to indicate candidate structures and used to construct two regions. Various features are then measured using the two regions around each seed point. The features characterize each candidate structure and are input to a classifier, such as a neural network. The classifier then distinguishes between structures of interest and background. The structures detected by the classifier are then presented to a clustering algorithm. A detected structure that is less than a threshold distance away from the nearest structure and a cluster is included in that cluster. Finally, the results are displayed, either on a monitor or on hard copy, with a frame around the detected cluster.

Abstract: The invention is a method and apparatus for automated detection of small structures in images. One specific use is to detect malignant microcalcification clusters in mammograms. A digitized and filtered mammogram image is stored in a computer. Seed pixels, which are pixels that are brighter than their immediate neighbors, are identified to indicate candidate structures and used to construct two regions. Various features are then measured using the two regions around each seed point. The features characterize each candidate structure and are input to a classifier, such as a neural network. The classifier then distinguishes between structures of interest and background. The structures detected by the classifier are then presented to a clustering algorithm. A detected structure that is less than a threshold distance away from the nearest structure and a cluster is included in that cluster. Finally, the results are displayed, either on a monitor or on hard copy, with a frame around the detected cluster.

Abstract: The invention is a novel method for automated detection of objects in images. One specific use is to detect malignant microcalcification clusters in mammograms. The method operates by forming a contour plot of the image (mammogram), the object (microcalcification) in the contour plot being comprised of a set of nested contour lines wherein the contour lines correspond to intensity levels thereby causing the object to appear as a prominent peak in relation to the local surround in the image.

Abstract: A method for segmenting a small feature in a multidimensional digital array of intensity values in a data processor computes an edge metric along each ray of a plurality of multidimensional rays originating at a local intensity extreme (local maximum or minimum). A multidimensional point corresponding to a maximum edge metric on each said ray is identified as a ray edge point. Every point on each ray from the local extreme to the ray edge point is labeled as part of the small object. Further points on the feature are grown by labeling an unlabeled point if the unlabeled point is adjacent to a labeled point, and the unlabeled point has a more extreme intensity than the labeled point, and the unlabeled point is closer than the labeled point to the local extreme. The resulting segmentation is quick, and identifies boundaries of small features analogous to boundaries identified by human analysts, and does not require statistical parameterizations or thresholds manually determined by a user.

Abstract: A method for segmenting a small feature in a multidimensional digital array of intensity values in a data processor computes an edge metric along each ray of a plurality of multidimensional rays originating at a local intensity extreme (local maximum or minimum). A multidimensional point corresponding to a maximum edge metric on each said ray is identified as a ray edge point. Every point on each ray from the local extreme to the ray edge point is labeled as part of the small object. Further points on the feature are grown by labeling an unlabeled point if the unlabeled point is adjacent to a labeled point, and the unlabeled point has a more extreme intensity than the labeled point, and the unlabeled point is closer than the labeled point to the local extreme. The resulting segmentation is quick, and identifies boundaries of small features analogous to boundaries identified by human analysts, and does not require statistical parameterizations or thresholds manually determined by a user.