# US 9,436,974 B2 · Method and apparatus to recover scene data using re-sampling compressive sensing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,436,974 B2 - Document ID: US9436974B2 - Status: Granted - Kind: B2 - Application number: 14/629,870 - Filed: 2015-02-24 - Published: 2015-08-27 - Granted: 2016-09-06 - Earliest claimed: 2014-02-24 - Assignees: Vencore Labs, Inc. - Applicants: Vencore Labs, Inc. - Inventors: Chi Leung Lau; Ted K. Woodward - Web page: https://patentgrep.com/patent/US9436974B2 ## CPC - G01S 17/89: for mapping or imaging - G01S 7/4808: {Evaluating distance, position or velocity data} - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Methods, computer program products, and computer systems for recovering data of scene are provided. The techniques include: obtaining, by at least one processor, data of a scene; re-sampling, by the at least one processor, the data of the scene to obtain re-sampled sensing data of the scene and a corresponding sensing matrix; and constructing, by the at least one processor, recovered data of the scene using the re-sampled sensing data of the scene and the corresponding sensing matrix. In one embodiment, the method includes enhancing occluded data of the scene to construct the recovered data of the scene, where the recovered data of the scene facilitates identification of the scene. In another embodiment, the method includes compressing original data of a scene, the compressing including sampling the original data of the scene to select the data of the scene. ## Claims ### Claim 1 A method comprising: obtaining, by at least one processor, data of a scene; re-sampling, by the at least one processor, the data of the scene to obtain re-sampled sensing data of the scene and a corresponding sensing matrix, the re-sampling using a series of entries, corresponding to a sampling rate for the data of the scene, and a probability parameter to control creation of the sensing matrix, the creation of the sensing matrix comprising probabilistically assigning values of the sensing matrix by evaluating the entries in the series of entries to determine whether a next entry of the series of entries indicates that a next value for the sensing matrix is to be a first value with a first probability or a second value with a second probability; and constructing, by the at least one processor, recovered data of the scene using the re-sampled sensing data of the scene and the corresponding sensing matrix. ### Claim 2 (depends on 1) The method of claim 1, wherein the data of the scene comprises occluded data of the scene, and the constructing comprises: enhancing the occluded data of the scene to construct the recovered data of the scene, wherein the recovered data of the scene facilitates identification of the scene. ### Claim 3 (depends on 1) The method of claim 1, further comprising: identifying, by the at least one processor, the scene using the recovered data of the scene. ### Claim 4 (depends on 1) The method of claim 1, wherein the obtaining comprises: compressing original data of a scene, the compressing comprising sampling the original data of the scene to select the data of the scene, wherein the original data of the scene has a first size and the data of the scene has a second size, the second size being smaller than the first size. ### Claim 5 (depends on 4) The method of claim 4, further comprising: transmitting, by the at least one processor, the data of the scene. ### Claim 6 (depends on 1) The method of claim 1, wherein the constructing comprises: minimizing an L1 norm of the recovered data of the scene, wherein the re-sampled sensing data of the scene is equal to an inner product of the sensing matrix and the recovered data of the scene. ### Claim 7 (depends on 1) The method of claim 1, wherein the constructing comprises: minimizing an L1 norm of the recovered data of the scene. ### Claim 8 (depends on 1) The method of claim 1, wherein the constructing comprises: selecting the re-sampled sensing data of the scene to be equal to an inner product of the sensing matrix and the recovered data of the scene. ### Claim 9 (depends on 1) The method of claim 1, wherein the obtaining comprises: randomly sampling the scene to determine the data of the scene. ### Claim 10 (depends on 9) The method of claim 9, wherein the random sampling comprises non-Gaussian sampling. ### Claim 11 A computer system comprising: a memory; and at least one processor in communication with the memory, wherein the computer system is configured to perform a method, said method comprising: obtaining, by the at least one processor, data of a scene; re-sampling, by the at least one processor, the data of the scene to obtain re-sampled sensing data of the scene and a corresponding sensing matrix, the re-sampling using a series of entries, corresponding to a sampling rate for the data of the scene, and a probability parameter to control creation of the sensing matrix, the creation of the sensing matrix comprising probabilistically assigning values of the sensing matrix by evaluating the entries in the series of entries to determine whether a next entry of the series of entries indicates that a next value for the sensing matrix is to be a first value with a first probability or a second value with a second probability; and constructing, by the at least one processor, recovered data of the scene using the re-sampled sensing data of the scene and the corresponding sensing matrix. ### Claim 12 (depends on 11) The computer system of claim 11, wherein the data of the scene comprises occluded data of the scene, and the constructing comprises: enhancing the occluded data of the scene to construct the recovered data of the scene, wherein the recovered data of the scene facilitates identification of the scene. ### Claim 13 (depends on 11) The computer system of claim 11, wherein said method further comprises: identifying, by the at least one processor, the scene using the recovered data of the scene. ### Claim 14 (depends on 11) The computer system of claim 11, wherein the obtaining comprises: compressing original data of a scene, the compressing comprising sampling the original data of the scene to select the data of the scene, wherein the original data of the scene has a first size and the data of the scene has a second size, the second size being smaller than the first size. ### Claim 15 (depends on 11) The computer system of claim 11, wherein the constructing comprises: minimizing an L1 norm of the recovered data of the scene, wherein the re-sampled sensing data of the scene is equal to an inner product of the sensing matrix and the recovered data of the scene. ### Claim 16 A computer program product comprising: a computer readable storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising: obtaining, by at least one processor, data of a scene; re-sampling, by the at least one processor, the data of the scene to obtain re-sampled sensing data of the scene and a corresponding sensing matrix, the re-sampling using a series of entries, corresponding to a sampling rate for the data of the scene, and a probability parameter to control creation of the sensing matrix, the creation of the sensing matrix comprising probabilistically assigning values of the sensing matrix by evaluating the entries in the series of entries to determine whether a next entry of the series of entries indicates that a next value for the sensing matrix is to be a first value with a first probability or a second value with a second probability; and constructing, by the at least one processor, recovered data of the scene using the re-sampled sensing data of the scene and the corresponding sensing matrix. ### Claim 17 (depends on 16) The computer program product of claim 16, wherein the data of the scene comprises occluded data of the scene, and the constructing comprises: enhancing the occluded data of the scene to construct the recovered data of the scene, wherein the recovered data of the scene facilitates identification of the scene. ### Claim 18 (depends on 16) The computer program product of claim 16, wherein said method further comprises: identifying, by the at least one processor, the scene using the recovered data of the scene. ### Claim 19 (depends on 16) The computer program product of claim 16, wherein the obtaining comprises: compressing original data of a scene, the compressing comprising sampling the original data of the scene to select the data of the scene, wherein the original data of the scene has a first size and the data of the scene has a second size, the second size being smaller than the first size. ### Claim 20 (depends on 16) The computer program product of claim 16, wherein the constructing comprises: minimizing an L1 norm of the recovered data of the scene, wherein the re-sampled sensing data of the scene is equal to an inner product of the sensing matrix and the recovered data of the scene.