Abstract: Systems and methods for performance testing software using computer vision. Systems can include a performance testing computer vision system and a computer vision-based performance testbed system. Methods can include generating a computer vision-based testing package and performance testing software in one or more testing environments on at least one virtualized testbed machine according to testing constraints using the computer vision-based testing package.

Abstract: Systems and methods for performance testing software using computer vision. Systems can include a performance testing computer vision system and a computer vision-based performance testbed system. Methods can include generating a computer vision-based testing package and performance testing software in one or more testing environments on at least one virtualized testbed machine according to testing constraints using the computer vision-based testing package.

Abstract: Mesenchymal stromal cells are engineered to express a chimeric antigen receptor (CAR), that specifically binds a marker of activated myeloid cells, including without limitation folate receptor beta; and are administered to an individual for treatment of inflammation at sites characterized by the presence of activated myeloid cells.

Abstract: Systems and methods for performance testing software using computer vision. Systems can include a performance testing computer vision system and a computer vision-based performance testbed system. Methods can include generating a computer vision-based testing package and performance testing software in one or more testing environments on at least one virtualized testbed machine according to testing constraints using the computer vision-based testing package.

Abstract: Systems and methods for performance testing software using computer vision. Systems can include a performance testing computer vision system and a computer vision-based performance testbed system. Methods can include generating a computer vision-based testing package and performance testing software in one or more testing environments on at least one virtualized testbed machine according to testing constraints using the computer vision-based testing package.

Abstract: Mesenchymal stromal cells are engineered to express a chimeric antigen receptor (CAR), that specifically binds a marker of activated myeloid cells, including without limitation folate receptor beta; and are administered to an individual for treatment of inflammation at sites characterized by the presence of activated myeloid cells.

Abstract: Embodiments disclose systems and methods for transmitting user-extracted video and content more efficiently by recognizing that user-extracted video provides the potential to treat parts of a single frame of a user-extracted video differently. An alpha mask of the image part of the user-extracted video is used when encoding the image part so that it retains a higher quality upon transmission than the remainder of the user-extracted video.

Abstract: Systems and methods for functionally testing software using computer vision. Systems can include a functional testing computer vision system and a computer vision-based functional testbed system. Methods can include generating a computer vision-based testing package and functionally testing software on at least one virtualized testbed machine using the computer vision-based testing package.

Abstract: Disclosed herein are methods and systems for classifying pixels as foreground using both short-range depth data and long-range depth data. One embodiment takes the form of a process that includes obtaining video data depicting at least a portion of a user. The process also includes obtaining short-range depth data associated with the video data. The process also includes obtaining long-range depth data associated with the video data. The video data, short-range depth data, and long-range depth data may be obtained via a single 3-D video camera. The process also includes classifying pixels of the video data as foreground based at least in part on both the short-range depth data and the long-range depth data. In some embodiments, classifying pixels of the video data as foreground comprises employing an alpha mask. The alpha mask may comprise binary foreground (hard) indicators. The alpha mask may comprise foreground-likelihood (soft) indicators.

Abstract: Systems and methods for functionally testing software using computer vision. Systems can include a functional testing computer vision system and a computer vision-based functional testbed system. Methods can include generating a computer vision-based testing package and functionally testing software on at least one virtualized testbed machine using the computer vision-based testing package.

Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.

Abstract: Systems and methods for using geometric primitives to identify background in video data. In one embodiment, a method obtains video data depicting at least a portion of a user. The video data is processed with at least one persona identification module comprising a geometric primitive module for generating a first persona probability map at least in part by: detecting at least one geometric primitive within the video data; identifying a respective region within the video data associated with each of the at least one detected geometric primitives; and assigning the respective regions an increased background-probability in the first persona probability map; and outputting a persona image by extracting pixels from the video data based on the persona probability map.

Abstract: Embodiments disclose extracting a user persona from a video of arbitrary duration and associating that persona with text for a chat session. Embodiments cause the persona to be extracted at the moment text is sent or received to convey the body language associated with the text.

Abstract: Disclosed herein are systems and methods for persona identification using combined probability maps. An embodiment takes the form of a method that includes obtaining at least one frame of pixel data; processing the at least one frame of pixel data to generate a hair-identification probability map; and generating a persona image by extracting pixels from the at least one frame of pixel data based at least in part on the generated hair-identification probability map.

Abstract: A color image and a depth image of a live video are received. Each of the color image and the depth image are processed to identify the foreground and the background of the live video. The background of the live video is removed in order to create a foreground video that comprises the foreground of the live video. A control input may be received to control the embedding of the foreground video into a second background from a background feed. The background feed may also comprise virtual objects such that the foreground video may interact with the virtual objects.

Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.

Abstract: A color image and a depth image of a live video are received. Each of the color image and the depth image are processed to identify the foreground and the background of the live video. The background of the live video is removed in order to create a foreground video that comprises the foreground of the live video. A control input may be received to control the embedding of the foreground video into a second background from a background feed. The background feed may also comprise virtual objects such that the foreground video may interact with the virtual objects.

Abstract: Disclosed herein are methods and systems for generating a user-hair-color model. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user. The process also includes determining a set of line segments of pixels of the video data, wherein each line segment in the determined set of line segments intersects an upper contour of the depicted head of the user. The process also includes grouping at least some of the pixels of at least one of the line segments in the determined set of line segments into three sets of pixels based at least in part on respective color data of the pixels. The three sets of pixels include a skin-pixel set, a hair-pixel set, and a background-pixel set. The process also includes updating a user hair-color model based at least in part on the skin-pixel set.

Abstract: Disclosed herein are methods and systems for generating a user-hair-color model. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user. The process also includes determining a set of line segments of pixels of the video data, wherein each line segment in the determined set of line segments intersects an upper contour of the depicted head of the user. The process also includes grouping at least some of the pixels of at least one of the line segments in the determined set of line segments into three sets of pixels based at least in part on respective color data of the pixels. The three sets of pixels include a skin-pixel set, a hair-pixel set, and a background-pixel set. The process also includes updating a user hair-color model based at least in part on the skin-pixel set.

Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.