Abstract: A method is provided that creates a lecture video capsule containing highlights of an original instructional video based on visual quality and content. The method includes segmenting and recognizing activities in the instructional video using a hidden Markov model (HMM). The activities are classified into three categories: talking head, writing hand and slideshow. The talking head frames are classified as non-content frames, while the writing hand and slideshows are classified as content frames. A non-reference based objective quality assessment of the non-content frames may be performed to detect high quality frames. Statistical parameters of an intensity histogram and a horizontal projection profile (HPP) of the content frames may be used to derive an objective quality measure of the content frames that is used to extract high quality content frames. The selected high quality non-content and content frames form a video clip or capsule, which is a temporally compressed representation of the video.

Abstract: A system and method for generating a fused image is provided. The system comprises processing circuitry configured to access a plurality of images, group the images into a plurality of sets, fuse the images of each set to form a plurality of fused images and fuse the fused images to form a final fused image.

Abstract: A system and method for generating a fused image is provided. The system comprises processing circuitry configured to receive a plurality of images, filter each received image using an edge preserving filter, compute a weight for each received image based on the corresponding filtered image and the received image and generate a fused image based on the weights of each received image. The system further comprises a memory device configured to store the fused image.

Abstract: A method of image object tracking and segmentation is provided. The method includes defining an initial contour for tracking an image object and partitioning the initial contour into a plurality of contour segments. The method also includes estimating a weighted length of each of the plurality of contour segments and generating a desired contour by converging the plurality of contour segments to a plurality of edges of the image object using the estimated weighted length.

Abstract: A device and method for automatically recreating a content preserving and compression efficient lecture video is provided. The device comprises of a computer based video recreating means (3) connected to a video receiving means (2) to an input side thereof and to a video reproducing means (4) at the output side thereof, wherein the video recreating means (3) is designed to split the video into visual and audio data, split the visual data into a plurality of scenes, classify each scene into a number of activity scenes, select activity scenes pre-determined to be critical for preserving the semantics of the lecture video and determine a key frame thereof, recreate the visual data by effecting a time based merger of the key frames of the selected activity scenes, recreate the audio data by removing voiceless data and noise therein and recreate the lecture video by effecting a synchronized time based merger of the recreated visual and audio data.

Abstract: A method is provided that creates a lecture video capsule containing highlights of an original instructional video based on visual quality and content. The method includes segmenting and recognizing activities in the instructional video using a hidden Markov model (HMM). The activities are classified into three categories: talking head, writing hand and slideshow. The talking head frames are classified as non-content frames, while the writing hand and slideshows are classified as content frames. A non-reference based objective quality assessment of the non-content frames may be performed to detect high quality frames. Statistical parameters of an intensity histogram and a horizontal projection profile (HPP) of the content frames may be used to derive an objective quality measure of the content frames that is used to extract high quality content frames. The selected high quality non-content and content frames form a video clip or capsule, which is a temporally compressed representation of the video.

Abstract: A method of tracking an object that appears in a plurality of image frames is provided. The method includes (a) dividing an identified object of one of the plurality of image frames into a plurality of object segments and (b) tracking a location of each of the plurality of object segments in the image frame. The method also includes (c) estimating at least one of scale and orientation of the object using the location of each of the plurality of object segments and (d) obtaining position of the object using the estimated scale and orientation.

Abstract: A device and method for automatically recreating a content preserving and compression efficient lecture video is provided. The device comprises of a computer based video recreating means (3) connected to a video receiving means (2) to an input side thereof and to a video reproducing means (4) at the output side thereof, wherein the video recreating means (3) is designed to split the video into visual and audio data, split the visual data into a plurality of scenes, classify each scene into a number of activity scenes, select activity scenes pre-determined to be critical for preserving the semantics of the lecture video and determine a key frame thereof, recreate the visual data by effecting a time based merger of the key frames of the selected activity scenes, recreate the audio data by removing voiceless data and noise therein and recreate the lecture video by effecting a synchronized time based merger of the recreated visual and audio data.

Abstract: A system and method for generating a fused image is provided. The system comprises processing circuitry configured to receive a plurality of images, filter each received image using an edge preserving filter, compute a weight for each received image based on the corresponding filtered image and the received image and generate a fused image based on the weights of each received image. The system further comprises a memory device configured to store the fused image.

Abstract: A method of image object tracking and segmentation is provided. The method includes defining an initial contour for tracking an image object and partitioning the initial contour into a plurality of contour segments. The method also includes estimating a weighted length of each of the plurality of contour segments and generating a desired contour by converging the plurality of contour segments to a plurality of edges of the image object using the estimated weighted length.

Abstract: A system and method for generating a fused image is provided. The system comprises processing circuitry configured to access a plurality of images, group the images into a plurality of sets, fuse the images of each set to form a plurality of fused images and fuse the fused images to form a final fused image.

Abstract: A method of tracking an object that appears in a plurality of image frames is provided. The method includes (a) dividing an identified object of one of the plurality of image frames into a plurality of object segments and (b) tracking a location of each of the plurality of object segments in the image frame. The method also includes (c) estimating at least one of scale and orientation of the object using the location of each of the plurality of object segments and (d) obtaining position of the object using the estimated scale and orientation.

Abstract: A method is provided that creates a lecture video capsule containing highlights of an original instructional video based on visual quality and content. The method includes segmenting and recognizing activities in the instructional video using a hidden Markov model (HMM). The activities are classified into three categories: talking head, writing hand and slideshow. The talking head frames are classified as non-content frames, while the writing hand and slideshows are classified as content frames. A non-reference based objective quality assessment of the non-content frames may be performed to detect high quality frames. Statistical parameters of an intensity histogram and a horizontal projection profile (HPP) of the content frames may be used to derive an objective quality measure of the content frames that is used to extract high quality content frames. The selected high quality non-content and content frames form a video clip or capsule, which is a temporally compressed representation of the video.