Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to decode a symbol in a digital image. A digital image of a portion of a symbol is received, which includes a grid of pixels and the symbol includes a grid of modules. A spatial mapping is determined between a contiguous subset of modules in the grid of modules to the grid of pixels. Causal relationships are determined, using the spatial mapping, between each module and the grid of pixels. A set of valid combinations of values of neighboring modules in the contiguous subset of modules are tested against the grid of pixels using the causal relationships. A value of at least one module of the two or more neighboring modules is determined based on the tested set of valid combinations. The symbol is decoded based on the determined value of the at least one module.

Abstract: Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.

Abstract: Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.

Abstract: Computerized methods and systems for locating barcodes applied to objects are provided. A method can receive a first image of a first barcode fragment applied to a first object captured at a first time and identify a first position of the first barcode fragment. The method can also receive a second image of a second barcode fragment captured at a second time and identify a second position of the second barcode fragment. The method can also predict a range of possible positions of the first barcode fragment at the second time based on a tracking model that tracks the first barcode fragment based on the first position, and determine that the first barcode fragment and the second barcode fragment correspond to the same barcode, if the second position is within the range of possible positions of the first barcode fragment at the second time.

Abstract: This invention provides a system and method for finding features in images that exhibit saddle point-like structures using relatively computationally low-intensive processes, illustratively consisting of an anti-correlation process, and associated anti-correlation kernel, which operates upon a plurality of pixel neighborhoods within the image. This process enables an entire image to be quickly analyzed for any features that exhibit such saddle point-like structures by determining whether the anti-correlation kernel generates a weak or strong response in various positions within the image. The anti-correlation kernel is designed to generate a strong response regardless of the orientation of a saddle point-like structure. The anti-correlation process examines a plurality of pixel neighborhoods in the image, thereby locating any saddle point-like structures regardless of orientation, as it is angle-independent.

Abstract: Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.

Abstract: Computerized methods and systems for locating barcodes applied to objects are provided. A method can receive a first image of a first barcode fragment applied to a first object captured at a first time and identify a first position of the first barcode fragment. The method can also receive a second image of a second barcode fragment captured at a second time and identify a second position of the second barcode fragment. The method can also predict a range of possible positions of the first barcode fragment at the second time based on a tracking model that tracks the first barcode fragment based on the first position, and determine that the first barcode fragment and the second barcode fragment correspond to the same barcode, if the second position is within the range of possible positions of the first barcode fragment at the second time.

Abstract: Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.

Abstract: Methods and apparatus are disclosed for extracting a one-dimensional digital signal from a two-dimensional digital image along a projection line. In some embodiments a repeating sequence of pixel weight templates, and a sequence of relative positions, are selected in response to the orientation of a projection line and used to compute a sequence of weighted sums. The sequence can be selected to achieve desirable properties, for example photometric accuracy, geometric accuracy, resolution, and/or noise reduction. In some embodiments registers and multiply-accumulators are arranged and controlled so as to compute the 1D signal.

Abstract: Systems and methods for decoding a barcode are disclosed. A scan signal for a first portion of the barcode is acquired. Integrated sub-character barcode feature evidence for the barcode is stored including evidence for a first plurality of possible sub-character barcode feature interpretations for a first plurality of sub-character barcode features. Sub-character barcode feature measurements are extracted for a second plurality of sub-character barcode features from the scan signal. Sub-character barcode feature evidence is determined for the second plurality of sub-character barcode features, including evidence for a second plurality of possible sub-character barcode feature interpretations for the second plurality of sub-character barcode features. Whether a portion of the integrated sub-character barcode feature evidence corresponds to a portion of the sub-character barcode feature evidence is determined. The sub-character barcode feature evidence is integrated into the integrated sub-character barcode.

Abstract: Systems and methods for decoding a barcode are disclosed. A scan signal for a first portion of the barcode is acquired. Integrated sub-character barcode feature evidence for the barcode is stored including evidence for a first plurality of possible sub-character barcode feature interpretations for a first plurality of sub-character barcode features. Sub-character barcode feature measurements are extracted for a second plurality of sub-character barcode features from the scan signal. Sub-character barcode feature evidence is determined for the second plurality of sub-character barcode features, including evidence for a second plurality of possible sub-character barcode feature interpretations for the second plurality of sub-character barcode features. Whether a portion of the integrated sub-character barcode feature evidence corresponds to a portion of the sub-character barcode feature evidence is determined. The sub-character barcode feature evidence is integrated into the integrated sub-character barcode.

Abstract: Computerized methods and systems for locating barcodes applied to objects are provided. A method can receive a first image of a first barcode fragment applied to a first object captured at a first time and identify a first position of the first barcode fragment. The method can also receive a second image of a second barcode fragment captured at a second time and identify a second position of the second barcode fragment. The method can also predict a range of possible positions of the first barcode fragment at the second time based on a tracking model that tracks the first barcode fragment based on the first position, and determine that the first barcode fragment and the second barcode fragment correspond to the same barcode, if the second position is within the range of possible positions of the first barcode fragment at the second time.

Abstract: Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.

Abstract: Methods and apparatus are disclosed for extracting a one-dimensional digital signal from a two-dimensional digital image along a projection line. In some embodiments a repeating sequence of pixel weight templates, and a sequence of relative positions, are selected in response to the orientation of a projection line and used to compute a sequence of weighted sums. The sequence can be selected to achieve desirable properties, for example photometric accuracy, geometric accuracy, resolution, and/or noise reduction. In some embodiments registers and multiply-accumulators are arranged and controlled so as to compute the 1D signal.

Abstract: This invention overcomes the disadvantages of the prior art by providing a system and method for reading symbology, and more typically linear barcodes (“symbols”) that employs trained attributes that remain invariant between scans of different barcodes during runtime to tune various aspects of the procedure for locating and decoding such barcodes. The procedure relies upon a training step in which one or more exemplary barcodes are decoded and the invariant attributes for such barcodes, including, but not limited to, size, shape, relative angle in the image field of view and resolution are stored. The runtime decoding procedure then employs these attributes to constrain the search for subsequent, scanned barcodes and then decode it according to a plurality of tuned steps.

Abstract: Methods and apparatus are disclosed for extracting a one-dimensional digital signal from a two-dimensional digital image along a projection line. In some embodiments a repeating sequence of pixel weight templates, and a sequence of relative positions, are selected in response to the orientation of a projection line and used to compute a sequence of weighted sums. The sequence can be selected to achieve desirable properties, for example photometric accuracy, geometric accuracy, resolution, and/or noise reduction. In some embodiments registers and multiply-accumulators are arranged and controlled so as to compute the 1D signal.

Abstract: Methods and apparatus are disclosed for extracting a one-dimensional digital signal from a two-dimensional digital image along a projection line. In some embodiments a repeating sequence of pixel weight templates, and a sequence of relative positions, are selected in response to the orientation of a projection line and used to compute a sequence of weighted sums. The sequence can be selected to achieve desirable properties, for example photometric accuracy, geometric accuracy, resolution, and/or noise reduction. In some embodiments registers and multiply-accumulators are arranged and controlled so as to compute the 1D signal.

Abstract: A method is provided for computing a composite image representing a focused image of an object in an application of machine vision in an optical inspection system. An image tessellated into focus regions is evaluated by region for fine feature sharpness. A sharpness image is computed for each focused region using a fine feature sharpness measurement. A focused composite image is computed by combining as a weighted average, the images at several focus settings, using the sharpness image at each focus setting as the weight. The focused composite image can be further analyzed, inspected, or otherwise processed.

Abstract: A method is provided for detecting spot defects on an object when an allowable variation (called the “background”) in the appearance of the object can be modeled. Methods are also provided for measuring and classifying detected spot defects. An alignment model is used to align the image of the object, a background model is then used to estimate the (possibly different) background in each region, and each background is substantially removed from the image so as to form a foreground image on which blob analysis can be applied to detect spot defects, the blob analysis using a threshold image that accommodates different noise statistics for each region. The method facilitates robust spot defect inspection of fiber optic end faces, or of any object with different object regions. The method also allows use of blob analysis over a larger range of conditions, including conditions that make simple blob analysis infeasible.

Abstract: The invention provides methods and apparatuses for finding features that are similar in the image. The invention finds the similar features by searching portions of the image for features that are substantially similar to a feature prototype. First, individual features in the image are located and designated candidate features, and optionally a spatial pattern representing a majority of the candidate features is generated. Next, feature profiles are generated therefrom, and the feature prototype is constructed using at least a subset of feature profiles. An example is described wherein the object is a ball grid array, the similar features are the solder balls on the ball grid array, the feature profiles are local images of balls of a ball grid array, and the feature prototype is an average of a sub-set of the local images of the balls.