Abstract: Two pupil detection methods implemented in an ophthalmic laser surgery system for detecting the pupil of the patient's eye both before and after the eye is applanated by a patient interface device. In the first method, an infrared image is captured before the eye is coupled to the patient interface. After excluding certain image artifacts, the pixel intensity is clustered into four clusters, and pixels belonging to the cluster with the lowest intensity are deemed pupil pixels, and pupil boundary and pupil center are determined accordingly. In the second method, a color image is captured after the eye is coupled to the patient interface. The color image is converted to the HSV color space, and only the hue channel is used for pupil detection. The hue channel image is filtered by median filtering and edge-preserving filtering (e.g. guided image filtering), then binarized, before pupil detection is performed.

Abstract: Methods and systems for image alignment are provided. One method includes selecting three or more salient feature points for use in test image to reference image alignment by applying a selected salient feature point detection method to at least a reference image generated for the specimen. The method also includes detecting the three or more salient feature points in the test image and the reference image and aligning the detected three or more salient feature points in the test image to the detected three or more salient feature points in the reference image. The method further includes aligning remaining portions of the test image to remaining portions of the reference image based on results of the previous aligning step.

Abstract: An inspection system is disclosed. The system includes a controller communicatively couplable with an inspection sub-system configured to receive illumination from a sample and generate image data. The controller includes one or more processors configured to execute program instructions causing the one or more processors to receive the image data, wherein the image data comprises at least one image, downsample the at least one image using bicubic interpolation or bilinear interpolation, transform the at least one image from a spatial domain to a frequency domain using a Fourier transform, filter frequencies higher than a threshold frequency from the at least one image, transform the at least one image from the frequency domain to the spatial domain using an inverse Fourier transform, and detect one or more flat-pattern defects in the at least one image.

Abstract: Methods and systems for image alignment are provided. One method includes selecting three or more salient feature points for use in test image to reference image alignment by applying a selected salient feature point detection method to at least a reference image generated for the specimen. The method also includes detecting the three or more salient feature points in the test image and the reference image and aligning the detected three or more salient feature points in the test image to the detected three or more salient feature points in the reference image. The method further includes aligning remaining portions of the test image to remaining portions of the reference image based on results of the previous aligning step.

Abstract: An inspection system is disclosed. The system includes a controller communicatively couplable with an inspection sub-system configured to receive illumination from a sample and generate image data. The controller includes one or more processors configured to execute program instructions causing the one or more processors to receive the image data, wherein the image data comprises at least one image, downsample the at least one image using bicubic interpolation or bilinear interpolation, transform the at least one image from a spatial domain to a frequency domain using a Fourier transform, filter frequencies higher than a threshold frequency from the at least one image, transform the at least one image from the frequency domain to the spatial domain using an inverse Fourier transform, and detect one or more flat-pattern defects in the at least one image.

Abstract: Methods and systems for detecting defects on a wafer are provided. One method includes altering one or more design clips based on how the one or more design clips will appear in output generated by a wafer inspection process for a wafer. The method also includes aligning the one or more altered design clips to the output generated for the wafer during the wafer inspection process. In addition, the method includes detecting defects on the wafer based on the output aligned to the one or more altered design clips.

Abstract: A method for removing horizontal and vertical lines in a document image while preserving integrity of the character strokes that intersect the lines. For each detected horizontal line, a vertical run length profile is calculated. Areas of the run length profile having two adjacent peaks with a valley in between are detected, which correspond to intersections of the horizontal line with non-vertical lines. A first derivative curve may be used to detect such peaks and valleys. Areas of the run length profile with large run length value for consecutive pixel locations are also detected, which corresponds to intersections of the horizontal line with near vertical lines. The horizontal line is removed in areas outside of the intersection areas, while preserving pixels within the intersection areas. Vertical line removal may be done similarly. This template-free method can remove lines in tables, forms, and underline and extract handwriting or printed characters.

Abstract: A text line segmentation method for a document image containing printed text and handwriting, or document image containing skewed lines or printed text. Connected component (CC) are obtained for the document, and their bounding boxes and centroids are calculated. The CCs are categorized into three categories based on bounding box sizes: small objects, regular text objects, and large objects involving handwriting. The centroids of regular text objects are used in a cluster analysis to find the vertical centers of the N text lines. Then, each CC is classified into one of the N lines based on the vertical distance between its centroid and the vertical centers of text lines, and copied into to a corresponding object board. Extra spaces are removed from the object boards to obtain the line segments. The large object involving handwriting will be classified into one of the lines but absent from other lines.

Abstract: Methods and systems for detecting defects on a wafer are provided. One method includes altering one or more design clips based on how the one or more design clips will appear in output generated by a wafer inspection process for a wafer. The method also includes aligning the one or more altered design clips to the output generated for the wafer during the wafer inspection process. In addition, the method includes detecting defects on the wafer based on the output aligned to the one or more altered design clips.

Abstract: A text line segmentation method for a document image containing printed text and handwriting, or document image containing skewed lines or printed text. Connected component (CC) are obtained for the document, and their bounding boxes and centroids are calculated. The CCs are categorized into three categories based on bounding box sizes: small objects, regular text objects, and large objects involving handwriting. The centroids of regular text objects are used in a cluster analysis to find the vertical centers of the N text lines. Then, each CC is classified into one of the N lines based on the vertical distance between its centroid and the vertical centers of text lines, and copied into to a corresponding object board. Extra spaces are removed from the object boards to obtain the line segments. The large object involving handwriting will be classified into one of the lines but absent from other lines.

Abstract: A word segmentation method for processing a document image applies clustering analysis to the spacing segments of a line. The spacing segments are generated by thresholding a one-dimensional vertical projection profile of the line. Taking advantage of the bimodal distribution of spacing length distribution of text lines, a k-means clustering algorithm is used, with the number of clusters pre-set to two, to classify the spacing segments as either character spacing or word spacing. Moreover, k-means++ initialization is used to enhance performance of cluster analysis. The clustering result such as cluster centers and compactness is used to prune single-word text line, single table item, etc. The locations of the word spacing segments are then used to segment the line of text into words.

Abstract: A method for removing horizontal and vertical lines in a document image while preserving integrity of the character strokes that intersect the lines. For each detected horizontal line, a vertical run length profile is calculated. Areas of the run length profile having two adjacent peaks with a valley in between are detected, which correspond to intersections of the horizontal line with non-vertical lines. A first derivative curve may be used to detect such peaks and valleys. Areas of the run length profile with large run length value for consecutive pixel locations are also detected, which corresponds to intersections of the horizontal line with near vertical lines. The horizontal line is removed in areas outside of the intersection areas, while preserving pixels within the intersection areas. Vertical line removal may be done similarly. This template-free method can remove lines in tables, forms, and underline and extract handwriting or printed characters.

Abstract: A word segmentation method for processing a document image applies clustering analysis to the spacing segments of a line. The spacing segments are generated by thresholding a one-dimensional vertical projection profile of the line. Taking advantage of the bimodal distribution of spacing length distribution of text lines, a k-means clustering algorithm is used, with the number of clusters pre-set to two, to classify the spacing segments as either character spacing or word spacing. Moreover, k-means++ initialization is used to enhance performance of cluster analysis. The clustering result such as cluster centers and compactness is used to prune single-word text line, single table item, etc. The locations of the word spacing segments are then used to segment the line of text into words.