Abstract: The dynamic adjusting of the conditions for identifying inputs as touching a touch-sensitive device is discloses. In some examples, in addition to using a signal density make threshold to identify an input patch as touching the surface, a signal density stability threshold can be used to identify the input patch as touching the surface. In some examples, a weighted average of peak signal density contributions from recent identified touches can be computed to dynamically adjust the make threshold for new input patches. In other examples, a new input patch identified as associated with the same path as an earlier touch can have its “make” threshold dynamically adjusted based on the earlier touch without computing a weighted average.

Abstract: This relates to a method of extrapolating proximity information to generate a border column or row of touch nodes (also known as touch pixels) and then fitting an ellipse to the contact patch including the extrapolated border touch nodes. Additionally, a contact can be identified as a thumb based on both its major axis and its distance to an edge of the touch sensing surface.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: This relates to a method of extrapolating proximity information to generate a border column or row of touch nodes (also known as touch pixels) and then fitting an ellipse to the contact patch including the extrapolated border touch nodes. Additionally, a contact can be identified as a thumb based on both its major axis and its distance to an edge of the touch sensing surface.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: The rejection of undesired touch events on a touch surface is disclosed. A weighted meta-centroid of all touch events can be computed. In some examples, touches can be weighted inversely to their size and distance to the nearest neighboring touch. In some examples, touches can be weighted based on the size of their minor radii. A meta-ellipse can be fit to the weighted touch events. In some examples, the major axis of the meta-ellipse can be used to determine the orientation of a user's hand to reject undesired touches. In other examples, touches within the meta-ellipse can be rejected.

Abstract: The dynamic adjusting of the conditions for identifying inputs as touching a touch-sensitive device is discloses. In some examples, in addition to using a signal density make threshold to identify an input patch as touching the surface, a signal density stability threshold can be used to identify the input patch as touching the surface. In some examples, a weighted average of peak signal density contributions from recent identified touches can be computed to dynamically adjust the make threshold for new input patches. In other examples, a new input patch identified as associated with the same path as an earlier touch can have its “make” threshold dynamically adjusted based on the earlier touch without computing a weighted average.

Abstract: A touch sensitive device that detects the occurrence of an electrostatic discharge event on the device by analyzing an acquired touch image for characteristics associated with the occurrence of an ESD event is provided. An acquired touch image is analyzed for characteristics that differentiate it from a touch image generated by a user input and are correlated with an expected touch image generated by an ESD event.

Abstract: A touch sensitive device that detects the occurrence of an electrostatic discharge event on the device by analyzing an acquired touch image for characteristics associated with the occurrence of an ESD event is provided. An acquired touch image is analyzed for characteristics that differentiate it from a touch image generated by a user input and are correlated with an expected touch image generated by an ESD event.

Abstract: This relates to a method of extrapolating proximity information to generate a border column or row of touch nodes (also known as touch pixels) and then fitting an ellipse to the contact patch including the extrapolated border touch nodes. Additionally, a contact can be identified as a thumb based on both its major axis and its distance to an edge of the touch sensing surface.

Abstract: A touch sensitive device that detects the occurrence of an electrostatic discharge event on the device by analyzing an acquired touch image for characteristics associated with the occurrence of an ESD event is provided. An acquired touch image is analyzed for characteristics that differentiate it from a touch image generated by a user input and are correlated with an expected touch image generated by an ESD event.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.