Abstract: A method and apparatus implementing dynamic thresholding techniques in processes that use image subtraction. The subtracted image is separated into a positive difference image using a positive image map and a negative difference image using a negative image map. A positive threshold map and a negative threshold map process a Gaussian filtered first image to generate respective positive and negative dynamic threshold images. The positive and negative threshold map mask out irrelevant areas and implement adaptive gain in pixel thresholds proportional to a difference or contrast between foreground or feature of interest and local background. Dynamic thresholding is a subtraction providing a subtracted positive image and a subtracted negative image. The subtracted positive and subtracted negative images are combined to provide a merged image which is binarized. Edge or Interface detection involves edge detection and true peak detection to find the edge peaks in a pre-image and post image.

Abstract: Machine vision methods for segmenting an image include the steps of generating a first image of the background of an object, generating a second image of the object and background, and subtracting the second image from the first image. The methods are characterized in that the second image is generated such that subtraction of it from the first image emphasizes the object with respect to the background.

Abstract: A method and system detect for the presence of a defect or a non-conforming object of unknown shape, configuration, and location within a semi-opaque enclosure. The semi-opaque enclosure has a pattern which is visible on at least a first of its external surfaces. The semi-opaque enclosure may comprise, for example, a labeled plastic or glass bottle, and the visible pattern may be print and graphical information provided on the bottle's label. A first digital image is captured from first channel light reflected off the first external surface of the enclosure; and a second digital image is captured from second channel light navigating the object inside the enclosure and emanating from the first external surface of the enclosure. A difference image is formed substantially devoid of information representing the visible pattern on the first external surface, by subtracting one of the first and second digital images from the other.

Abstract: A method and apparatus implementing dynamic thresholding techniques in processes that use image subtraction. The subtracted image is separated into a positive difference image using a positive image map and a negative difference image using a negative image map. A positive threshold map and a negative threshold map process a Gaussian filtered first image to generate respective positive and negative dynamic threshold images. The positive and negative threshold map mask out irrelevant areas and implement adaptive gain in pixel thresholds proportional to a difference or contrast between foreground or feature of interest and local background. Dynamic thresholding is a subtraction providing a subtracted positive image and a subtracted negative image. The subtracted positive and subtracted negative images are combined to provide a merged image which is binarized. Edge or Interface detection involves edge detection and true peak detection to find the edge peaks in a pre-image and post image.

Abstract: A method and system detect for the presence of a defect or a non-conforming object of unknown shape, configuration, and location within a semi-opaque enclosure. The semi-opaque enclosure has a pattern which is visible on at least a first of its external surfaces. The semi-opaque enclosure may comprise, for example, a labeled plastic or glass bottle, and the visible pattern may be print and graphical information provided on the bottle's label. A first digital image is captured from first channel light reflected off the first external surface of the enclosure; and a second digital image is captured from second channel light navigating the object inside the enclosure and emanating from the first external surface of the enclosure. A difference image is formed substantially devoid of information representing the visible pattern on the first external surface, by subtracting one of the first and second digital images from the other.

Abstract: A method and system detect for the presence of an object of unknown shape, configuration, orientation and location within a non-rigid semi-opaque enclosure. The semi-opaque enclosure has a pattern which is visible on at least a first of its external surfaces. The semi-opaque enclosure may comprise, for example, a photo-envelope containing a film package, and the visible pattern may be print and graphical information provided on the envelope's outer surface. A first digital image (containing just the front print) is captured from first channel light reflected off the first external surface of the enclosure; and a second digital image (containing the front print and a silhouette of the object) is captured from second channel light navigating the object inside the enclosure and emanating from the first external surface of the enclosure.

Abstract: A machine vision method is provided for segmenting an image based on an adaptive thresholding approach that exploits prior knowledge of the characteristics, such as area, of the object to be segmented. Adaptation to varying gray-values of the candidate object/background from scene-to-scene is obtained by using a sampling window which provides gray-value statistics of the candidate object to be segmented. Next, using these statistics, a characteristic of the image of the object to be segmented, such as its area or its gray-value standard deviation, and the histogram of the image, the segmentation is performed. The invention is especially useful for detecting the presence/absence of adhesive on printed circuit boards, where both the appearance of the printed circuit board and the solder paste commonly found thereon make segmentation difficult. In case of presence/absence determination, an additional step of shape matching is employed.

Abstract: Machine vision methods for inspection of semiconductor die surfaces include the steps of generating a first image of the die surface, generating a second image of the die surface and any defect thereon, and subtracting the second image from the first image. The methods are characterized in that the second image is generated such that subtraction of it from the first image emphasizes the defect with respect to the die surface.

Abstract: Inspection of solder paste on a printed circuit board using a before printing image (pre-image) to normalize an after printing image (post-image) of the printed circuit board. Existing lighting and optics used for alignment of the screen printing stencil to the printed circuit board are used for the solder paste inspection. A stencil in the screen printing process is also inspected using a before printing image (pre) to normalize an after printing image (post) of the stencil.

Abstract: A method and apparatus to search, locate and inspect an object or characteristic of interest on a non-rigid package. First, a model of patterns of interest are stored to train the system with a model of the characteristics of interest. A minimum spanning forest (MSF) is generated defining the characteristics of interest as vertices with each vertex referencing a vertex closest to itself in order to minimize the sum of the distances between vertices in each minimum spanning tree (MST) of the MSF. At run time two coarse alignment features that the system has been trained to identify are located to determine rotation and coarse position of a non-rigid package being inspected. Local alignment then determines the exact position of local alignment points each of which is a starting vertex associated with one or more characteristics of interest as vertices in that starting vertexes MST.