The present invention is in the field of methods for determining material properties from foam samples. It relates to a computer-implemented method for determining a material property of a foam sample comprising (a) providing a representation of the sample, (b) extracting at least one structural feature from the representation, wherein the at least one structural feature comprises walls, struts, or nodes (c) providing the at least one structural feature to a material model suitable for obtaining at least one material property from the structural feature, and (d) outputting the at least one material property received from the material model.

A product identification apparatus (20) includes an acquisition unit (210), an image processing unit (220), and a storage processing unit (230). The acquisition unit (210) acquires an image generated by an image capturing apparatus 10. The image includes a product shelf (40) and a product (50). The image processing unit (220) determines a position where continuity of the product shelf (40) is broken by processing the image acquired by the acquisition unit (210), and divides the product shelf (40) into a unit region by using the position. Further, the image processing unit (220) determines a kind and a product name of the product (50) by processing the image acquired by the acquisition unit (210). The storage processing unit (230) causes a storage unit (240) to store product identification information of the product (50) located in the unit region, for each unit region of the product shelf (40).

An image data processing method and apparatus are provided. In a technical solution provided by embodiments of this disclosure, M object feature maps with different sizes are obtained by extracting a source image. While classification confidence levels corresponding to pixel points in each of the object feature maps are acquired, initial predicted polar radii corresponding to the pixel points in each of the object feature maps may also be acquired. The initial predicted polar radii are refined based on polar radius deviations corresponding to the contour sampling points in each of the object feature maps, to acquire target predicted polar radii corresponding to the pixel points in each of the object feature maps. Then the object edge shape of a target object contained in the source image can be determined based on the target predicted polar radii and the classification confidence levels.

An image data processing method and apparatus are provided. In a technical solution provided by embodiments of this disclosure, M object feature maps with different sizes are obtained by extracting a source image. While classification confidence levels corresponding to pixel points in each of the object feature maps are acquired, initial predicted polar radii corresponding to the pixel points in each of the object feature maps may also be acquired. The initial predicted polar radii are refined based on polar radius deviations corresponding to the contour sampling points in each of the object feature maps, to acquire target predicted polar radii corresponding to the pixel points in each of the object feature maps. Then the object edge shape of a target object contained in the source image can be determined based on the target predicted polar radii and the classification confidence levels.

An image processing method includes: recognizing a target contour of a target object in a target image collected at a current moment determining, in the target contour, a starting contour point corresponding to a starting contour position, a final contour point corresponding to a final contour position, and a split contour point corresponding to the current moment taking a line segment composed of contour points between the starting contour point and the split contour point in the target contour as a first line segment, and taking a line segment except the first line segment in the target contour as a second line segment rendering the first line segment according to a first color, and rendering the second line segment according to a second color.

A dimension measurement method includes: extracting a plurality of lines from a plurality of images generated by shooting a target area from a plurality of viewpoints, and generating a line segment model which is a three-dimensional model of the target area that is expressed using the plurality of lines; calculating a dimension of a particular part inside the target area, using the line segment model; and outputting the dimension calculated.

A dimension measurement method includes: extracting a plurality of lines from a plurality of images generated by shooting a target area from a plurality of viewpoints, and generating a line segment model which is a three-dimensional model of the target area that is expressed using the plurality of lines; calculating a dimension of a particular part inside the target area, using the line segment model; and outputting the dimension calculated.

Vessel perfusion and myocardial blush are determined by analyzing fluorescence signals obtained in a static region-of-interest (ROI) in a collection of fluorescence images of myocardial tissue. The blush value is determined from the total intensity of the intensity values of image elements located within the smallest contiguous range of image intensity values containing a predefined fraction of a total measured image intensity of all image elements within the ROI. Vessel (arterial) peak intensity is determined from image elements located within the ROI that have the smallest contiguous range of highest measured image intensity values and contain a predefined fraction of a total measured image intensity of all image elements within the ROI. Cardiac function can be established by comparing the time differential between the time of peak intensity in a blood vessel and that in a region of neighboring myocardial tissue both pre and post procedure.

Vessel perfusion and myocardial blush are determined by analyzing fluorescence signals obtained in a static region-of-interest (ROI) in a collection of fluorescence images of myocardial tissue. The blush value is determined from the total intensity of the intensity values of image elements located within the smallest contiguous range of image intensity values containing a predefined fraction of a total measured image intensity of all image elements within the ROI. Vessel (arterial) peak intensity is determined from image elements located within the ROI that have the smallest contiguous range of highest measured image intensity values and contain a predefined fraction of a total measured image intensity of all image elements within the ROI. Cardiac function can be established by comparing the time differential between the time of peak intensity in a blood vessel and that in a region of neighboring myocardial tissue both pre and post procedure.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.