Abstract: A method of object detection in an image uses a background anomaly approach that searches for anomalies of a particular size and shape that are distinguishable from the image's local background. Included is a geometric classifier used to distinguish regularly-shaped objects from irregularly-shaped objects.

Abstract: A rendering method converts three-dimensional streak tube imaging lidar (STIL) data to two-dimensional contrast and range maps thereof. A temporal portion of the STIL data is processed for each pixel of the three-dimensional image. Each temporal portion includes a Gaussian-like portion and non-Gaussian-like portions. Processing for each pixel includes: (i) removing noise from the temporal portion using the non-Gaussian-like portions wherein a filtered form of the Gaussian-like portion is formed, (ii) determining a non-integer center of mass of the filtered form of the Gaussian-like portion with the non-integer center of mass being indicative of a range value, and (iii) applying a mathematical interpolation function to determine amplitude of the filtered form of the Gaussian-like portion at the range value defined by the non-integer center of mass. The amplitude so-determined is indicative of a contrast value. The process is repeated for all pixels to thereby produce range and contrast maps.

Abstract: A method is provided for enhancing two-dimensional contrast and range images rendered from three-dimensional STIL data. A series of sequential steps are provided for each of the rendered contrast image data and rendered range image data. Briefly, the rendered contrast image data is scaled based on a mean intensity value of all pixels of data therein, stretched to a predetermined dynamic range, modified to compensate for jitter and CCD array effects associated with the STIL camera system, normalized using an exponential decay function that describes contrast intensity roll-off associated therewith, and has a histogram clip routine applied thereto.

Abstract: A method is provided for enhancing two-dimensional contrast images rendered from three-dimensional STIL data. Briefly, the rendered contrast image data is scaled based on a mean intensity value of all pixels of data therein, stretched to a predetermined dynamic range, modified to compensate for jitter and CCD array effects associated with the STIL camera system, normalized using an exponential decay function that describes contrast intensity roll-off associated therewith, and has a histogram clip routine applied thereto.

Abstract: A method is provided for enhancing two-dimensional range images rendered from three-dimensional STIL data. Briefly, the rendered range image data is modified to compensate for jitter effects associated with the STIL camera system, has background portions of the resulting data modified to compensate for CCD array effects and range intensity roll-off associated with the STIL camera system, has a “salt and pepper” noise reduction routine applied thereto, and has a histogram clip routine applied thereto.

Abstract: A method of and sensor for processing a laser line scan image to enhance portion representing otherwise obscure objects from background. Two contrast enhancement routines are applied to enhance obscured objects in the low signal strength regions. One is a background equalization routine employing an overlapping piecewise least squares error technique that equalizes the high/low signal strength regions. The second is a local histogram clipping routine that applies a moving window histogram clip to enhance details in the low strength regions without effecting the high strength regions.

Abstract: Line contrast difference effect which limits the capabilities of laser line scan (LLS) sensors is substantially reduced by a process that uses a smhing array, modified based upon a mean magnitude array of all columns of a discrete Fourier transform (DFT) of an image matrix. Trouble frequencies (both small band width and large band width) indicative of line contrast difference effect are identified from the mean magnitude DFT array using a linear least squares error method. The same technique is used to calculate suppression values for each of the identifiable trouble frequencies and the smoothing array is modified thereby. Once all modifications are made to the smoothing array, it is applied to the mean magnitude DFT array to suppress those pixels of the array that are associated with trouble frequencies. The corrected mean magnitude DFT array is then subject to an inverse DFT function for each column in the array to reconstruct the original image, now in corrected form, to be saved for display or storage.

Abstract: To produce an enhanced image, the contrast of the image is first measured computing a central histogram moment. The image is scaled at each pixel thereof based on the central histogram moment and the maximum value of the image's dynamic range resulting in the generation of a scaled image. An estimate of the image background is subtracted from the scaled image to produce a low contrast enhancement value and added to the scaled image to produce a high contrast enhancement value. A portion of the low contrast enhancement value is summed with a complimentary portion of the high contrast enhancement value to generate an enhanced image intensity at each pixel.