Abstract: A digital image of a first garment having one or more first garment portions is received. A user has identified the first garment portions as matching one or more corresponding second garment portions of a second garment. The probability of accidental match of the first garment within the digital image in relation to the second garment is determined, by using a statistical model based on one or more parameters and based on analyses of the first garment portions. The probability of accidental match is output.

Abstract: A digital image of a first garment having one or more first garment portions is received. A user has identified the first garment portions as matching one or more corresponding second garment portions of a second garment. The probability of accidental match of the first garment within the digital image in relation to the second garment is determined, by using a statistical model based on one or more parameters and based on analysis of the first garment portions. The probability of accidental match is output.

Abstract: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.

Abstract: A video stream of people within a venue like a movie theater is received. The people within the video stream are analyzed. Based on analysis of the people within the video stream, virtual content is overlaid onto the video stream. The video stream, with the virtual content overlaid thereon, is then displayed onto a screen within the venue. As such, the virtual content and one or more of the people within the venue can appear to be interacting with one another as if the virtual content were real and present within the venue.

Abstract: A video stream of people within a venue like a movie theater is received. The people within the video stream are analyzed. Based on analysis of the people within the video stream, virtual content is overlaid onto the video stream. The video stream, with the virtual content overlaid thereon, is then displayed onto a screen within the venue. As such, the virtual content and one or more of the people within the venue can appear to be interacting with one another as if the virtual content were real and present within the venue.

Abstract: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.

Abstract: Road signs are recognized within field-of-view (FOV) video data having frames. Within a first stage, one or more candidate road signs within the FOV video data are identified, by statically analyzing each frame of the FOV video data independently to detect the one or more candidate road signs within the FOV video data. Within a second stage, each candidate road sign is confirmed or rejected as an actual candidate road sign within the FOV video data by dynamically analyzing the frames of the FOV video data interdependently. The first stage is a static analysis that considers each frame of the FOV video data independently. The second stage is a dynamic analysis that considers the frames of the FOV video data interdependently.

Abstract: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.

Abstract: A digital image of a first garment having one or more first garment portions is received. A user has identified the first garment portions as matching one or more corresponding second garment portions of a second garment. The probability of accidental match of the first garment within the digital image in relation to the second garment is determined, by using a statistical model based on one or more parameters and based on analyses of the first garment portions. The probability of accidental match is output.

Abstract: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.

Abstract: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.

Abstract: A video stream of people within a venue like a movie theater is received. The people within the video stream are analyzed. Based on analysis of the people within the video stream, virtual content is overlaid onto the video stream. The video stream, with the virtual content overlaid thereon, is then displayed onto a screen within the venue. As such, the virtual content and one or more of the people within the venue can appear to be interacting with one another as if the virtual content were real and present within the venue.

Abstract: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.