Abstract: Virtual aircraft simulators are used to educate and train aircraft pilots flying solo or with another pilot, instructor, and air-traffic controller. The device contains a capsule installed on a computerized mechanical platform providing up to six degrees of freedom of real-time movement, and a pilot seat. To simulate real sensations of the pilot more closely, the capsule may also be equipped with a control stick, one or more thrust levers, and pedals. The stereo glasses are used to create virtual reality. The invention improves the functionality of the simulator by introducing a virtual avatar with artificial intelligence, which, when flying with a trainee, can replicate the actions of a captain, co-pilot, air-traffic controller, or instructor. The avatar also can maintain a verbal dialogue with the trainee within the scope of a standard pilot communication protocol thesaurus. The device is suitable for any aircraft type without changing the hardware.

Abstract: Various aspects of an image-processing apparatus and method for object boundary stabilization in an image of a sequence of image frames are disclosed. The image-processing apparatus includes an image processor that receives a depth image of a scene from a first-type of sensor and a color image of the scene from the second-type of sensor. The scene may comprise at least an object-of-interest. A first object mask of the object-of-interest is generated by a depth thresholding operation on the received depth image. Dangling-pixels artifact present on a first object boundary of the first object mask, are removed. The first object boundary is smoothened using a moving-template filter on the color image. A second object mask having a second object boundary is generated based on the smoothening of the first object boundary. The object-of-interest from the color image is extracted based on the generated second object mask.

Abstract: Various aspects of an image-processing apparatus and method to track and retain an articulated object in a sequence of image frames are disclosed. The image-processing apparatus is configured to segment each image frame in the sequence of image frames into different segmented regions that corresponds to different super-pixels. An articulated object in a first motion state is detected by non-zero temporal derivatives between a first image frame and a second image frame. A first connectivity graph of a first set of super-pixels of the first image frame, is constructed. A second connectivity graph of a second set of super-pixels of the second image frame, is further constructed. A complete object mask of the articulated object in a second motion state is generated based on the first connectivity graph and the second connectivity graph, where at least a portion of the articulated object is stationary in the second motion state.

Abstract: Various aspects of an image-processing apparatus and method to track and retain an articulated object in a sequence of image frames are disclosed. The image-processing apparatus is configured to segment each image frame in the sequence of image frames into different segmented regions that corresponds to different super-pixels. An articulated object in a first motion state is detected by non-zero temporal derivatives between a first image frame and a second image frame. A first connectivity graph of a first set of super-pixels of the first image frame, is constructed. A second connectivity graph of a second set of super-pixels of the second image frame, is further constructed. A complete object mask of the articulated object in a second motion state is generated based on the first connectivity graph and the second connectivity graph, where at least a portion of the articulated object is stationary in the second motion state.

Abstract: Various aspects of an image-processing apparatus and method for object boundary stabilization in an image of a sequence of image frames are disclosed. The image-processing apparatus includes an image processor that receives a depth image of a scene from a first-type of sensor and a color image of the scene from the second-type of sensor. The scene may comprise at least an object-of-interest. A first object mask of the object-of-interest is generated by a depth thresholding operation on the received depth image. Dangling-pixels artifact present on a first object boundary of the first object mask, are removed. The first object boundary is smoothened using a moving-template filter on the color image. A second object mask having a second object boundary is generated based on the smoothening of the first object boundary. The object-of-interest from the color image is extracted based on the generated second object mask.

Abstract: An automated process of transferring an object (e.g., the head with hair) from an image into another image, or to a different location in the same image is described. The implementation is a comprehensive and fully automated approach enabling the object's transfer without intermediate intervention and support from the user. The process automates both steps: the object's delineation, and its blending into the new background as well.

Abstract: Various aspects of an apparatus and a method to generate a shadow of an object embedded in a target image are disclosed herein. The method includes generation of an object mask of the embedded object in the target image. The object mask is categorized into a first category or a second category based on a pre-determined set of rules. Based on the categorization, a shadow of the object in the target image is generated. The generation of the shadow comprises dilating the categorized object mask by a predetermined multiplication factor when the categorized object mask corresponds to the first category. The generation of the shadow further comprises determination of depth information of the categorized object mask at a pre-determined height when the categorized object mask corresponds to the second category.

Abstract: Various aspects of an apparatus and a method to generate a shadow of an object embedded in a target image are disclosed herein. The method includes generation of an object mask of the embedded object in the target image. The object mask is categorized into a first category or a second category based on a pre-determined set of rules. Based on the categorization, a shadow of the object in the target image is generated. The generation of the shadow comprises dilating the categorized object mask by a predetermined multiplication factor when the categorized object mask corresponds to the first category. The generation of the shadow further comprises determination of depth information of the categorized object mask at a pre-determined height when the categorized object mask corresponds to the second category.

Abstract: An automated process of transferring an object (e.g., the head with hair) from an image into another image, or to a different location in the same image is described. The implementation is a comprehensive and fully automated approach enabling the object's transfer without intermediate intervention and support from the user. The process automates both steps: the object's delineation, and its blending into the new background as well.

Abstract: Image segmentation utilizing the color and brightness of sections of the image to generate super-pixels is described herein. The image segmentation is implemented pixel-wise. A color triangle is utilized in the classification of the pixels. By using a hierarchical classification table, the memory usage is significantly reduced, and the image segmentation is able to be performed on-the-fly in real-time.

Abstract: Automatic scaling of image objects being moved or copied to a target object location is presented. An original image and its depth map are received within which the user selects an original object and a target location for the object to be moved or copied. A center of scaling is found and a base location selected at which depth value is to be obtained at the target location for use in scaling. Target object is then scaled in response to a ratio between original object depth and target object depth. An occlusion check is made, after which non-occluded pixels are pasted to the target location to complete the move/copy which is in proper proportion and accounts for surrounding structures.

Abstract: Various aspects of a method and apparatus for video processing may include a computing device communicably coupled to an external device. The computing device may be operable to determine an average vertical velocity and an average horizontal velocity of a subset of pixels in an image frame and determine a depth value for each pixel of the subset of pixels based on calculated motion vectors of the pixel of the subset of pixels, the average vertical velocity of the subset of pixels and the average horizontal velocity of the subset of pixels.

Abstract: An image processing device determines a first brightness value of a first pre-specified area in proximity to an object mask of an object in a source image and a first average color value of the source image, and determines a second brightness value of a second pre-specified area in proximity to an object mask of the object in a target image and a second average color value of the target image. The brightness value of each pixel of the object in the target image is adjusted based on a ratio of the second brightness value and the first brightness value. A color value of each pixel of an object in the target image is adjusted when a difference value between the first average color value and the second average color value exceeds a threshold color value.

Abstract: Image segmentation utilizing the color and brightness of sections of the image to generate super-pixels is described herein. The image segmentation is implemented pixel-wise. A color triangle is utilized in the classification of the pixels. By using a hierarchical classification table, the memory usage is significantly reduced, and the image segmentation is able to be performed on-the-fly in real-time.

Abstract: Automatic scaling of image objects being moved or copied to a target object location is presented. An original image and its depth map are received within which the user selects an original object and a target location for the object to be moved or copied. A center of scaling is found and a base location selected at which depth value is to be obtained at the target location for use in scaling. Target object is then scaled in response to a ratio between original object depth and target object depth. An occlusion check is made, after which non-occluded pixels are pasted to the target location to complete the move/copy which is in proper proportion and accounts for surrounding structures.

Abstract: A natural user interface system and method of operation thereof including: providing a display screen having a range camera connected to the display screen in a known location relative to the display screen; determining a user's pointing vector as pointing towards the display screen; determining the user's pointing vector as motionless; and initializing a cursor in the center of the display screen and simultaneously calibrating the user's pointing vector as an initial pointing vector pointing at the center of the display screen.

Abstract: Various aspects of a method and apparatus for video processing may include a computing device communicably coupled to an external device. The computing device may be operable to determine an average vertical velocity and an average horizontal velocity of a subset of pixels in an image frame and determine a depth value for each pixel of the subset of pixels based on calculated motion vectors of the pixel of the subset of pixels, the average vertical velocity of the subset of pixels and the average horizontal velocity of the subset of pixels.

Abstract: A system for generating orientation information includes a reflector, an image collection system, and a processor. The image collection system is configured to collect at least two sets of image data, where one set of image data includes a stronger indication of the reflector than the other set of image data. The two sets of image data are collected using at least one reflector with some orientation-specific characteristic or a reflector integrated into a device that includes some orientation-specific structural feature. Once collected, the two sets of image data are used to generate orientation information related to the reflector. In particular, orientation information related to the reflector is generated by taking the difference between the two sets of image data.

Abstract: A system for generating orientation information includes a reflector, an image collection system, and a processor. The image collection system is configured to collect at least two sets of image data, where one set of image data includes a stronger indication of the reflector than the other set of image data. The two sets of image data are collected using at least one reflector with some orientation-specific characteristic or a reflector integrated into a device that includes some orientation-specific structural feature. Once collected, the two sets of image data are used to generate orientation information related to the reflector. In particular, orientation information related to the reflector is generated by taking the difference between the two sets of image data.

Abstract: A system for generating orientation information includes a reflector, an image collection system, and a processor. The image collection system is configured to collect at least two sets of image data, where one set of image data includes a stronger indication of the reflector than the other set of image data. The two sets of image data are collected using at least one reflector with some orientation-specific characteristic or a reflector integrated into a device that includes some orientation-specific structural feature. Once collected, the two sets of image data are used to generate orientation information related to the reflector. In particular, orientation information related to the reflector is generated by taking the difference between the two sets of image data.