초록 ▼

The present invention relates to a method of identifying a target object in an image using image processing. It further relates to apparatus and computer software implementing the method. The method includes storing template data representing a template orientation field indicative of an orientation

The present invention relates to a method of identifying a target object in an image using image processing. It further relates to apparatus and computer software implementing the method. The method includes storing template data representing a template orientation field indicative of an orientation of each of a plurality of features of a template object; receiving image data representing the image; processing the image data to generate an image orientation field indicating an orientation corresponding to the plurality of image features; processing the image orientation field using the template orientation field to generate a match metric indicative of an extent of matching between at least part of the template orientation field and at least part of the image orientation field; and using the match metric to determine whether or not the target object has been identified in the image. Image and/or template confidence data is used to generate the match metric.

대표청구항 ▼

1. A method of identifying a target object in an image including a plurality of image features, wherein said method includes: a) storing template data, said template data representing a template orientation field indicative of an orientation of each of a plurality of features of a template object;b)

1. A method of identifying a target object in an image including a plurality of image features, wherein said method includes: a) storing template data, said template data representing a template orientation field indicative of an orientation of each of a plurality of features of a template object;b) receiving image data representing said image;c) processing said image data to generate an image orientation field indicating an orientation corresponding to said plurality of image features;d) processing said image data to generate image confidence data based on at least one characteristic for use in identifying said target object in a given image, said characteristic being indicative of an increased likelihood that at least one part of said given image represents at least part of said target object, relative to other parts of said given image;e) applying said image confidence data to said image orientation field to generate a modified image orientation field indicating both an orientation corresponding to said plurality of image features and a likelihood that at least one part of said image orientation field represents at least part of said target object; thenf) processing said modified image orientation field using said template orientation field to generate a match metric indicative of an extent of matching between at least part of said template orientation field and at least part of said image orientation field; andg) using said match metric to determine whether or not said target object has been identified in said image. 2. A method according to claim 1, wherein said characteristic indicated by said image confidence data relates to an extent of image data errors expected in said image data and said method includes using said image confidence data to generate said match metric in accordance with said expected extent of image data errors. 3. A method according to claim 1, wherein said characteristic indicated by said image confidence data relates to a possible occlusion of part of the target object in the image and said method includes using said image confidence data to generate said match metric in accordance with said possible occlusion. 4. A method according to claim 1, wherein said image data is video image data and wherein optionally said characteristic relates to a possible movement of the target object. 5. A method according to claim 4, wherein with said characteristic relating to a possible movement of the target object said method includes using an identified movement of said target object to determine a modified shape of said target object which is modified from a shape of said target object represented by said template data. 6. A method according to claim 1, wherein said image data represents said image in colour and said characteristic relates to at least one colour indicative of said target object. 7. A method according to claim 1, including generating the match metric using deformation data indicative of a deformation of template data with respect to image data. 8. A method according to claim 1, wherein at least one of said image data, said template data, said image confidence data, and deformation data used to generate the match metric, is arranged according to a matrix format. 9. A method according to claim 1, wherein each of said image features represents, in said image, a boundary which separates a region of higher light intensity from a region of lower light intensity. 10. A method according to claim 9, wherein said boundary separates two regions, each having a similar light intensity, from each other. 11. Apparatus comprising: at least one processor;and at least one memory including computer program instructions; the at least one memory and the computer program instructions being configured to, with the at least one processor, cause the apparatus at least to perform the method of claim 1. 12. A computer program product comprising a non-transitory computer-readable storage medium having computer readable instructions stored thereon, the computer readable instructions being executable by a computerised device to cause the computerised device to perform the method according to claim 1. 13. A method of identifying a target object in an image including a plurality of image features, wherein said method includes: a) storing template data, said template data representing a template orientation field indicative of an orientation of each of a plurality of features of a template object;b) receiving template confidence data indicating a likelihood that at least one part of said template accurately represents at least part of said target object;c) receiving image data representing said image;d) processing said image data to generate an image orientation field indicating an orientation corresponding to said plurality of image features;e) applying said template confidence data to said template orientation field to generate a modified template orientation field indicating both an orientation of each of said plurality of features of said template object and a likelihood that at least one part of said template orientation field represents at least part of said target object; thenf) processing said image orientation field using said modified template orientation field to generate a match metric indicative of an extent of matching between at least part of said template orientation field and at least part of said image orientation field; andg) using said match metric to determine whether or not said target object has been identified in said image. 14. A method according to claim 13, including generating the match metric using deformation data indicative of a deformation of template data with respect to image data. 15. A method according to claim 13, wherein each of said image features represents, in said image, a boundary which separates a region of higher light intensity from a region of lower light intensity. 16. A method according to claim 15, wherein said boundary separates two regions, each having a similar light intensity, from each other. 17. Apparatus comprising: at least one processor;and at least one memory including computer program instructions; the at least one memory and the computer program instructions being configured to, with the at least one processor, cause the apparatus at least to perform the method of claim 13. 18. A computer program product comprising a non-transitory computer-readable storage medium having computer readable instructions stored thereon, the computer readable instructions being executable by a computerised device to cause the computerised device to perform the method according to claim 13.