Abstract:
In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of an image of a scene and determining a graph based on connecting nodes of the image. The nodes are either pixels or superpixels of the image. The graph is determined by determining one or more connections of a node to one or more nodes belonging to a pre-defined image region around the node in the image. The connections are associated with edge weights that are determined based on at least one of similarity parameters and spatial distances between the node and the one or more nodes. The method includes determining disparity values at the nodes of the image based at least on performing tree based aggregation of a cost volume on the graph, where the cost volume is associated with the image and at least one view image of the scene.
Abstract:
In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of primary depth map and plurality of color segments, the primary depth map associated with a first image and a second image that are stereoscopic pair of images of a scene, and the color segments associated with the first image. The method includes generating plurality of disparity planes based on the color segments and the primary depth map. The method includes determining aggregated cost volume between pixels of the first and second images for the disparity planes. The method includes assigning plane labels corresponding to the disparity planes to pixels of the first and second images based on the aggregated cost volume, an individual pixel being assigned a plane label. The method includes generating secondary depth map based on the plane labels assigned to the pixels of the first and second images.
Abstract:
In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of a first image and a second image of a scene comprising one or more objects. The method includes detecting the objects in the first image by detecting object point of the objects in the first image. The method includes detecting the object points of the objects in the second image based on detection of the object points of the objects in the first image. Detection of an object point in the second image that corresponds to an object point of the first image comprises searching for the object point on an epipolar line in the second image corresponding to the object point of the first image. The method includes determining disparity values between the objects points in the first image and the object points in the second image.
Abstract:
In an example embodiment, a method, apparatus and computer program product are provided. The method includes computing a first cost volume for a light-field image. A first depth map comprising depth information of the plurality of sub-images of the light-field image is computed based on the first cost volume. A first view image comprising reconstruction information is reconstructed based on the depth information of the plurality of sub-images. A second cost volume corresponding to the first cost volume is computed based on the reconstruction information. The second cost volume is filtered based on the first view image to generate an aggregated cost volume. A second depth map is generated based on the aggregated cost volume. The second depth map facilitates generation of a second view image that is associated with a resolution higher than a resolution of the first view image.
Abstract:
In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating receipt of a first and a second image. A first and a second aggregated cost volume associated with pixels of the first and the second images are determined for a plurality of disparity values. A first and a second disparity maps are generated based on the first and the second aggregated cost volume. A confidence map for disparity values of the first image is generated based on the first aggregated cost volume. One or more infinity regions in the first image are determined based on a number of confident pixels in color segments of the first image. A third disparity map is generated by determining filtered disparity values for the pixels of the first image where filtered disparity values for pixels of the one or more infinity regions are a pre-defined disparity value.
Abstract:
In an example embodiment, method, apparatus and computer program product for improving image and video captures using depth maps of viewfinder depth map, are provided. The method includes facilitating receipt of a viewfinder depth map of a scene, the viewfinder depth map comprising depth information of a plurality of objects in the scene. One or more objects are selected from the plurality of objects based on depth information of the one or more objects in the viewfinder depth map. Two or more images of the scene are facilitated to be captured by at least adjusting focus of a camera corresponding to the depth information of the one or more objects that are selected. In an example, a method also includes facilitating capture of an image of the scene by at least adjusting focus of a camera corresponding to the depth information of the two or more objects that are selected.
Abstract:
A method, apparatus and computer program for: receiving a spatial audio input; determining a direction of interest from the spatial audio input; and generating a spatial audio output dependent on the spatial audio input and the direction of interest.
Abstract:
In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating receipt of a first and a second image. A first and a second aggregated cost volume associated with pixels of the first and the second images are determined for a plurality of disparity values. A first and a second disparity maps are generated based on the first and the second aggregated cost volume. A confidence map for disparity values of the first image is generated based on the first aggregated cost volume. One or more infinity regions in the first image are determined based on a number of confident pixels in color segments of the first image. A third disparity map is generated by determining filtered disparity values for the pixels of the first image where filtered disparity values for pixels of the one or more infinity regions are a pre-defined disparity value.
Abstract:
In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating selection of a region of interest (ROI) in a plurality of frames of a multimedia content. The ROI is associated with a motion of at least one object. An object mobility data matrix associated with the ROI is determined in the plurality of frames. The object mobility data matrix is indicative of a difference in motion of the at least one object in the plurality of frames. A projection of the object mobility data matrix is determined on a line. The motion of the at least one object in the ROI is determined across the plurality of frames to as a periodic motion or a non-periodic motion based on the projection of the object mobility data matrix.
Abstract:
In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating selection of a region of interest (ROI) in a plurality of frames of a multimedia content. The ROI is associated with a motion of at least one object. An object mobility data matrix associated with the ROI is determined in the plurality of frames. The object mobility data matrix is indicative of a difference in motion of the at least one object in the plurality of frames. A projection of the object mobility data matrix is determined on a line. The motion of the at least one object in the ROI is determined across the plurality of frames to as a periodic motion or a non-periodic motion based on the projection of the object mobility data matrix.