Signal processing disparity resolution

Claim 2

A process for matching two related but transformed signal waveforms representative of sensor outputs in a particular application domain, said process comprising the steps of: selecting criteria, associated with the particular application domain, for use in the determination of significant points of interest, type sets of allowable types of point matches, type match valuation, match windows, disparity windows, and disparity similarity valuation; identifying significant points of interest in each data set; determining positional offset information and type information for significant points of interest in each data set responsive to said criteria; determining a match window for each significant point of interest responsive to said criteria; locating and determining match values for all allowable matches between significant points in the two data sets, within respective match windows; determining disparity windows for all significant points in the two data sets; determining composite similarity values for each allowable match to a particular significant point in the first data set, responsive to the positional offset information for each allowable match to said particular point and to the positional offset information for correspondingly offset allowable matches for other significant points in the first data set, for such points within the disparity window with said particular point, responsive to the match values; determining a best match for each said particular point within the first data set corresponding to the allowable match point in the second data set having the maximum composite similarity value for the respective particular point, responsive to the match values and the composite similarity values; and matching the two signal waveforms responsive to the criteria and the determined best matches.

Claim 3

The process as in claim 1 further comprising the steps of: storing said determined type information associatively mapped to positional information for each significant point of interest in each data set; storing, for each significant point of interest in the first data set, said match values each associatively mapped to positional offset data representative of the respective relative distance offset between each significant point in the first data set and each respective allowable match point in the second data set; storing said composite similarity values, associatively mapped to positional offset data, for each allowable match to a particular point in the first data set; and storing said best match values associatively mapped to the positional offset information for the particular point, and for the allowable match point in the second data set having the maximum composite similarity value for the respective particular point, responsive to the match value and the composite similarity value.

Claim 4

The process as in claim 1 further comprising the step of: analyzing relationships in parallel of multiple points of the two data sets within the window, to determine a best value match for each point of interest on the first waveform.

Claim 5

The process as in claim 1 further comprising the step of: relating positional offset data for corresponding best match points between the first and second waveforms to yield a fused data set representing coherent data about the related waveforms.

Claim 6

The process as in claim 1 wherein the step of determining composite similarity values is further characterized as being based on cooperatively and local consensus.

Claim 7

The process as in claim 1 wherein the step of determining composite similarity values is further comprised of the step of: allowing for local deformations in the interaction of the data sets.

Claim 8

The process as in claim 1 wherein the step of determining the composite similarity value is further comprised of the step of: scaling the valuation of neighboring allowable matches by the spatial separation of the points at which the disparities are measured.

Claim 9

The process of claim 1 wherein the step of determining the composite similarity value is further comprised of the steps of: determining a predominant gradient orientation for each waveform, and modifying the disparity window responsive to the predominant gradient.

Claim 10

The process as in claim 1 further comprising the step of: biasing the determination of composite similarity in favor of local continuity.

Claim 13

The system as in claim 10 further comprising: means for determining in parallel the best match for multiple significant points of interest on the first waveform.

Claim 14

The system as in claim 10 further comprising: means for relating positional offset data for corresponding best match points between the first and second waveform to yield a fused data set representing coherent data about the two related waveforms.

Claim 15

The system as in claim 10 further characterized as determining composite similarity based on cooperativity and local consensus.

Claim 16

The system as in claim 10 wherein said means for determining composite similarity values is further characterized as allowing for local deformations in the interaction of the data sets.

Claim 17

The system as in claim 10 further characterized in that said composite similarity value contribution for each allowable match is scaled by the spatial separation of the significant points of interest at which the disparities are measured.

Claim 18

The system as in claim 10 wherein said means for determining said composite similarity value is further comprised of: means for determining a predominant gradient orientation, means for measuring the local image intensity gradient, and means for modifying the disparity window responsive to said predominant gradient, said local gradient, and to the criteria.

Claim 19

The system as in claim 10 further comprising: means for biasing the determination of composite similarity in favor of local continuity.

Claim 20

A stereo correspondence process for matching of figurally similar three dimensional images responsive to discretized representations of the images, comprising the step of determining significant features and positions there for each of the images; determining all potential matches of features as between the two images in accordance with predefined feature matching windows; determining support for each potential match of features responsive to a predefined disparity valuation function for valving support of potential matches of neighboring features within a predefined window; utilizing all support disparity values for each potential feature match to determine a composite value for each potential feature match; selecting as a best match the maximum composite value for each individual feature of one image to features of the other image; matching the images for stereo correspondence according to the selected best matches.

Claim 21

A stereo correspondence process for matching of figurally similar three dimensional images responsive to discretized representations of the images, comprising the step of determining significant features and positions there for each of the images; determining all potential matches of features as between the two images in accordance with predefined feature matching windows; determining support for each potential match of features responsive to a predefined disparity valuation function for valving support of potential matches of neighboring features within a predefined window; utilizing all support disparity values for each potential feature match to determine a composite value for each potential feature match; selecting as a best match the maximum composite value for each individual feature of one image to features of the other image; matching the images for stereo correspondence according to the selected best matches.

Claim 22

The process as in claim 18 further comprising: storing identifying characteristic data representative of determined significant features for each image in a memory, said identifying characteristic data associatively mapped to its respective position in the respective image; storing value characteristic data representative of determined potential matches in a memory, said value characteristic data associately mapped to positional data for the respective features in the images; and summing in respective separate memories, the respective support disparity values for each respective feature in one image, associatively mapped according to relative offset of potential matches.

Claim 23

A process for matching two transformed but related signals, comprising the steps of: provding selection criteria for significant points of interest, type sets for allowable matches, matching window, disparity window, disparity similarity valuation and type match valuation; storing separately digitized data sets for the two signals, each data set associately mapped by relative positional information within each signal; analyzing each data set according to the selection criteria to determine location and type value for each significant point of interest, and storing the type value associatively mapped to the position of each point; determining match windows responsive to the selection criteria; locating allowable matches between significant points of interest in the two data sets within each mach window responsive to the selection criteria and to the associatively mapped type store; valuing each allowable match responsive to the selection criteria, and storing the match values, associatively mapped according to positional offset and according to each particular points' position within the first data set; determining disparity windows responsive to the selection criteria for all points within the data sets; determining composite similarity values for each allowable match for each particular point in the first data set based on disparity similarity of allowable matches for the particular point and of neighboring points in the first data set within the disparity window; accumulating sums in separate "activity cells" for the disparity similarity values for allowable matches to the particular point and neighboring points according to positional offsets, responsive to the associatively mapped match value store to the selection criteria; selecting the maximally valued activity cell for each particular point as identifying the best match for the particular point; storing positional data for the best match from the second data set for each point of interest in the first data, associatively mapping the corresponding best match points to each other; matching the two signals and removing disparities therebetween responsive to the selection criteria and to the best match mapped store.

Claim 24

A process for matching two transformed but related signals, comprising the steps of: provding selection criteria for significant points of interest, type sets for allowable matches, matching window, disparity window, disparity similarity valuation and type match valuation; storing separately digitized data sets for the two signals, each data set associately mapped by relative positional information within each signal; analyzing each data set according to the selection criteria to determine location and type value for each significant point of interest, and storing the type value associatively mapped to the position of each point; determining match windows responsive to the selection criteria; locating allowable matches between significant points of interest in the two data sets within each mach window responsive to the selection criteria and to the associatively mapped type store; valuing each allowable match responsive to the selection criteria, and storing the match values, associatively mapped according to positional offset and according to each particular points' position within the first data set; determining disparity windows responsive to the selection criteria for all points within the data sets; determining composite similarity values for each allowable match for each particular point in the first data set based on disparity similarity of allowable matches for the particular point and of neighboring points in the first data set within the disparity window; accumulating sums in separate "activity cells" for the disparity similarity values for allowable matches to the particular point and neighboring points according to positional offsets, responsive to the associatively mapped match value store to the selection criteria; selecting the maximally valued activity cell for each particular point as identifying the best match for the particular point; storing positional data for the best match from the second data set for each point of interest in the first data, associatively mapping the corresponding best match points to each other; matching the two signals and removing disparities therebetween responsive to the selection criteria and to the best match mapped store.