Abstract: A method is provided for determining number of times in which a specific advertisement is displayed to a specific viewer on digital billboards. The method comprises: providing data which relates to geographical locations of the digital billboards and a displaying time when that advertisement was displayed thereat; retrieving data relating to a group of viewers who were present within a viewing range from a respective digital billboard at the time the advertisement was displayed thereat; determining whether the viewer is included among this group of viewers; and if so, storing information that relates to that determination. Also provided is a method for preventing display of a specific advertisement to the viewer if the total number of times in which that advertisement has already been displayed to that viewer on different displaying means, is equal to a pre-determined value.

Abstract: A method is provided for logging a first user in to a mobile device being in a locked mode. The method comprising: storing a password associated with the first user and information that relates to a facial image of that first user; when the mobile device is in a locked mode: receiving a password inserted by a user; comparing the received password with the stored password and determining whether they match; if a match is found, prompting an image capturing device to capture an image; retrieving information that relates to a facial image from the captured image and comparing that information with the stored information; if the retrieved information matches the stored information, unlocking the mobile device.

Abstract: A method is provided for logging a first user in to a mobile device being in a locked mode. The method comprising: storing a password associated with the first user and information that relates to a facial image of that first user; when the mobile device is in a locked mode: receiving a password inserted by a user; comparing the received password with the stored password and determining whether they match; if a match is found, prompting an image capturing device to capture an image; retrieving information that relates to a facial image from the captured image and comparing that information with the stored information; if the retrieved information matches the stored information, unlocking the mobile device.

Abstract: A method is provided for rating areas in video frames. The method comprises: (a) providing a video signal comprising a plurality of consecutive video frames; (b) selecting a frame from among these frames and indentifying a first plurality of objects comprised in the selected frame; (c) upon comparing the selected frame with other frames, dividing the first plurality of the objects into a second plurality of background objects and a third plurality of foreground objects; (d) dividing objects of the third plurality of foreground objects into one or more connected components, where a connected component comprises group(s) of pixels wherein a change in each of these pixels is associated with a change in each of the remaining pixels of the respective group(s); and (e) associating an interest level with the connected components based upon a pre-determined set of criteria, thereby providing a rating of areas comprised within the video signal.

Abstract: A method is provided for rating areas in video frames. The method comprises: (a) providing a video signal comprising a plurality of consecutive video frames; (b) selecting a frame from among these frames and indentifying a first plurality of objects comprised in the selected frame; (c) upon comparing the selected frame with other frames, dividing the first plurality of the objects into a second plurality of background objects and a third plurality of foreground objects; (d) dividing objects of the third plurality of foreground objects into one or more connected components, where a connected component comprises group(s) of pixels wherein a change in each of these pixels is associated with a change in each of the remaining pixels of the respective group(s); and (e) associating an interest level with the connected components based upon a pre-determined set of criteria, thereby providing a rating of areas comprised within the video signal.

Abstract: A method for enhancing video data comprises a) extracting said video data into a plurality of segments; b) analyzing said extracted video data with regard to one or more features within or between said segments; c) finding at least one location for placing at least one message in one of said segments; and d) integrating a message at said at least one location according to information obtained from user consumption and/or interaction with the video.

Abstract: A method for illumination-independent change detection in a pair of original registered gray images based on blob extraction. Blobs are extracted from the two original images and their negatives, using an enhanced blob extraction algorithm based on connectivity analysis along gray-levels. Blobs extracted from the first original and negative images are compared with blobs extracted from the second original and negative images to determine whether each blob has a corresponding blob, i.e. whether it is a matched or unmatched blob. All unmatched blobs are tested for significance as “blobs of change” using a fitness measure based on either a ratio of saliency gradients, or a product of this ratio and a gradient distribution measure.

Abstract: A method for automatic, stable and robust object extraction of moving objects in color video frames, achieved without any prior knowledge of the video content. For high rate video, the method includes providing at least a first and a second high frame rate video frames, performing a reciprocal illumination correction of the first and second video frames to yield respective first and second smoothed frames, performing a change detection operation between the first and second smoothed frames to obtain a difference image, and performing a local adaptive thresholding operation on the difference image to generate a binary image containing extracted objects, the local thresholding operation using a weight test to determine a boundary of each of the extracted objects. For an extracted object with a fragmented boundary, the method further comprises re-unifying the boundary.

Abstract: A method for illumination-independent change detection in a pair of original registered gray images based on blob extraction. Blobs are extracted from the two original images and their negatives, using an enhanced blob extraction algorithm based on connectivity analysis along gray-levels. Blobs extracted from the first original and negative images are compared with blobs extracted from the second original and negative images to determine whether each blob has a corresponding blob, i.e. whether it is a matched or unmatched blob. All unmatched blobs are tested for significance as “blobs of change” using a fitness measure based on either a ratio of saliency gradients, or a product of this ratio and a gradient distribution measure.

Abstract: A method for automatic, stable and robust object extraction of moving objects in color video frames, achieved without any prior knowledge of the video content. For high rate video, the method includes providing at least a first and a second high frame rate video frames, performing a reciprocal illumination correction of the first and second video frames to yield respective first and second smoothed frames, performing a change detection operation between the first and second smoothed frames to obtain a difference image, and performing a local adaptive thresholding operation on the difference image to generate a binary image containing extracted objects, the local thresholding operation using a weight test to determine a boundary of each of the extracted objects. For an extracted object with a fragmented boundary, the method further comprises re-unifying the boundary.

Abstract: A method for illumination-independent change detection in a pair of original registered gray images based on blob extraction. Blobs are extracted from the two original images and their negatives, using an enhanced blob extraction algorithm based on connectivity analysis along gray-levels. Blobs extracted from the first original and negative images are compared with blobs extracted from the second original and negative images to determine whether each blob has a corresponding blob, i.e. whether it is a matched or unmatched blob. All unmatched blobs are tested for significance as “blobs of change” using a fitness measure based on either a ratio of saliency gradients, or a product of this ratio and a gradient distribution measure.