Abstract: A process comprising subjecting a composition comprising an alpha- or beta-hydroxycarboxylic acid, water, an extraction solvent, and an acid catalyst to extractive dehydration to form an unsaturated carboxylic acid product.

Abstract: Disclosed are processes and apparatuses for producing a crystalline product. The processes and apparatuses may extend the operational time of an evaporative crystallizer by providing an internal volume or large deposit inventory for fouling deposits to reside without impacting the unit operation.

Abstract: A process comprising subjecting a composition comprising an alpha- or beta-hydroxycarboxylic acid, water, an extraction solvent, and an acid catalyst to extractive dehydration to form an unsaturated carboxylic acid product.

Abstract: Disclosed are processes and apparatuses for producing a crystalline product. The processes and apparatuses may extend the operational time of an evaporative crystallizer by providing an internal volume or large deposit inventory for fouling deposits to reside without impacting the unit operation.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.

Abstract: An image of high-aspect-ratio objects is analyzed to locate linear features within the image. The gradient direction for each pixel is determined, and connected pixels having similar gradient directions are grouped into line support regions. A linear feature is determined for each line support region. The linear features are analyzed to identify those that are co-linear. The linear features are then analyzed to find groups that are parallel and that sufficiently overlap. Additional sets of linear features that intersect the identified group are combined into that group. The line support regions of the grouped linear features are analyzed to determine representative orientation and/or dimensional information, which is analyzed to determine statistical information about a plurality of the objects. This information is used to monitor or control one or more processes associated with the objects, to reject the objects or a structure or device comprising the objects, or the like.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.