Datalogic IP Tech S.r.l.

Alessandro Franchi

Luigi Di Stefano

Federico Tombari

Francesco D'Ercoli

Marco Cumoli

Claudio Saporetti

Roberto Musiani

Maurizio De Girolami

Angelo Carraggi

Domenico Pozzetti

Leonardo Mora

Ivar Keulers

A system and method of detecting an image of a template object in a captured image may include comparing, by a processor, an image model of an imaged template object to multiple locations, rotations, and scales in the captured image. The image model may be defined by multiple model base point sets derived from contours of the imaged template object, where each model base point set inclusive of a plurality of model base points that are positioned at corresponding locations associated with distinctive features of the imaged template object. Each corresponding model base point of the model base point sets may (i) be associated with respective layers and (ii) have an associated gradient vector. A determination may be made as to whether and where the image of the object described by the image model is located in the captured image.

Imaging system and method using a multi-layer model approach to provide robust object detection

A system and method for matching a pattern may include generating a set of model descriptors representative of segment features of a first imaged object. A set of query descriptors representative of segment features of a second imaged object may be generated. A first model segment may be selected to align with the query segments to determine if any of the query segments correspond with the first model segment based on respective model descriptors and query descriptors. An hypothesis may be generated by computing a transformation to match the selected first model descriptor with a query descriptor. The hypothesis may be validated by a model fitting algorithm when comparing other transformed model descriptors with other query descriptors. Based on a consensus value from the model fitting algorithm, a determination as to whether a pattern match exists between the first and second images may be made.

Segment-based pattern matching algorithm

A system and method of inspection may include capturing image data by a stereo imaging device. A determination as to whether noise indicative of a transparent or specular object exists in the image data may be made. A report that a transparent or specular object was captured in the image data may be made.

System and method for identifying the presence or absence of transparent pills in blister packer machines using high resolution 3D stereo reconstruction based on color linear cameras

A system and method may include determining a presence or absence of cigarettes in a set of cigarettes being processed by a cigarette packer machine by using at least one of a first image or a second image inclusive of the cigarettes in the set of cigarettes. A stereographic image may be produced using the first and second images. Determine level of tobacco of each cigarette of the set of cigarettes by using the first and second images. Produce a report in response to determining that a cigarette is not present or that a level of tobacco of a cigarette is outside of a specified level of tobacco of a cigarette.

Stereoscopic system and method for quality inspection of cigarettes in cigarette packer machines

Systems, methods, and computer-readable storage media are provided for identifying (recognizing) an object from its shape in a sequence of images utilizing sequence alignment matrices (SAMs). For a given image, an object is segmented and from the segmented object, a set of key points is extracted. From the extracted key points, a set of local feature descriptors, strictly related to the key points and uniquely ordered in sequence, are extracted. The feature sequence obtained from the segmented object is aligned with a counterpart or reference image (e.g., a model or another image) using a Sequence Alignment Matrix (SAM). A custom scoring technique for the alignment provides a quality index for the identification of the object.

Object recognition utilizing feature alignment

During a description technique, a local descriptor for an object may be generated by computing a 2-dimensional histogram of pairs of angles between pairs of line segments that are aligned with edge pixels associated with the object. The pairs of line segments belong to a subset of k neighboring or proximate line segments. Moreover, this 2D histogram may represent the relative displacement and the relative orientations of the pairs of line segments in the subset as weights in bins or cells defined by angular quantization values, and the 2D histogram may exclude lengths of the line segments. Subsequently, the generated 2D histogram may be compared to predefined sets of descriptors in a model library that are associated with a set of objects, and the object may be identified as one of the set of objects based on a group of match scores determined in the comparisons.

Describing objects using edge-pixel-feature descriptors

During a description technique (), a local descriptor for an object () is generated () by computing a 2-dimensional histogram () of pairs of angles () between pairs of line segments () that are aligned with edge pixels associated with the object (). The pairs of line segments () belong to a subset of k neighboring or proximate line segments (). Moreover, this 2D histogram () may represent the relative displacement and the relative orientations of the pairs of line segments () in the subset as weights in bins or cells defined by angular quantization values, and the 2D histogram (may exclude lengths of the line segments. Subsequently, the generated 2D histogram () may be compared () to predefined sets of descriptors in a model library that are associated with a set of objects, and the object may be identified () as one of the set of objects based on a group of match scores determined in the comparisons.

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Bologna, Italy

Alessandro Franchi