Nokia Technologies Oy

Tinghuai Wang

Yu You

Lixin Fan

Lingyu Zhu

Sounak Bhattacharya

Apparatus for processing image data associated with at least one input image, including a convolutional neural network, CNN,-based encoder configured to provide a plurality of hierarchical feature maps based on the image data, a decoder configured to provide output data based on the plurality of feature maps, wherein the decoder includes a convolutional long short-term memory, Conv-LSTM, module configured to sequentially process at least some of the plurality of hierarchical feature maps.

Apparatus and method for processing image data

An apparatus configured to, in respect of virtual reality content comprising video imagery configured to provide a virtual reality space wherein a virtual reality view presented to a user provides for viewing of the VR space; based on a comment made by and a virtual location of a commenting-user in the virtual reality space when the comment was made; provide for determination of a point of interest in the virtual reality space, the point of interest identified based on, at least, the virtual location of the commenting-user when the comment was made and semantic analysis of the comment to identify the point of interest surrounding the virtual location to which the comment refers, the point of interest associated with the comment thereby enabling the comment to be overlaid over the virtual reality view of the video imagery.

Apparatus and associated methods

In an exemplary embodiment, the present invention is an apparatus which includes at least one processor, and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to perform obtaining an image for segmentation; forming a trimap of at least a portion of the image, the trimap comprising a three-region map having a definite foreground, a definite background, and an unknown region separating the foreground and the background, said unknown region initially having a preselected width; in a plurality of iterations, changing the width of the unknown region; for each of the plurality of iterations, determining a quality measure; and selecting the width for the unknown region having the best quality measure. The apparatus may also include at least one camera for obtaining the image for segmentation.

Fast automatic trimap generation and optimization for segmentation refinement

An apparatus configured to, in respect of a video provided to a user and wherein a plurality of comments are displayed such that they scroll across the video, based on the comments and one or more comment filtering rules, provide for comment filtering as each of the plurality of comments scrolls to meet a filter line, wherein the filter line extends across at least part of the video to define a first area overlaying the video and a non-overlapping second area overlaying the video, such that in the first area the plurality of comments are provided for display scrolling therethrough towards the filter line prior to comment filtering and in the second area the plurality of comments are provided for display with any comments of the plurality of comments that satisfy the one or more comment filtering rules removed from display.

Apparatus and method for displaying video and comments

Aspects described herein relate to various methods, systems and apparatuses that may improve the accuracy of object classifications and object boundary definitions for a semantic segmentation technique. For example, the semantic segmentation technique may be based on a hierarchy of two or more layers. The two or more layers may include neural networks that analyze image data at different resolution scales. Each layer of the hierarchy may determine object boundary features and object class features. Each layer of the hierarchy may share its object boundary features and/or its object class features with one or more other layers in the hierarchy. In turn, each of other layers of the hierarchy may determine its object boundary features and/or its object class features based on the shared features.

Semantic segmentation based on a hierarchy of neural networks

A method, comprising: receiving a video comprising video frames as an input; generating set of object proposals from the video; generating object tracklets comprising regions appearing in consecutive frames of the video, said regions corresponding to object proposals with a predetermined level of confidence; constructing a graph comprising the object proposals and superpixels grouped from the pixels of the frame; calculating a first cost function for superpixel likelihoods in terms of the object proposals in an object tracklet; calculating a second cost function for object proposal likelihoods in terms of the superpixels in the frame; minimizing the first and the second cost function with each other; computing posterior probabilities of each superpixel; and assigning each superpixel with an object class with a maximum posterior probability to constitute semantic object segmentation.

Method, an apparatus and a computer program product for object detection

An apparatus configured to, in respect of first and second virtual reality content () each configured to provide imagery for a respective first and second virtual reality space for viewing in virtual reality; and based on first-user-viewing-experience information defining an appearance of an object of interest () that appears in the first virtual reality content as viewed, in virtual reality, by a first user (), and defining a time-variant point of view from which the first user () viewed the object of interest (); providing for display to a second user (), the second user provided with a virtual reality view of the second virtual reality content (), of imagery of the object of interest () superimposed into the virtual reality space of the second virtual reality content () such that the second user () is able, while viewing the second virtual reality content, to witness the object of interest () as it was viewed by the first user ().

Apparatus for sharing objects of interest and associated methods

A method comprising: enabling a first user to define a message for display to at least a second user in association with a first three-dimensional scene viewed by the first user and viewed by or viewable by the second user, wherein the message comprises user-defined message content for display and message metadata, not for display, defining first three-dimensional spatial information; and enabling rendering of the user-defined message content in a second three-dimensional scene viewed by the second user, wherein the user-defined message content moves, within the second three-dimensional scene, along a three-dimensional trajectory dependent upon the first three-dimensional spatial information and three-dimensional spatial information of the second user.

Rendering of user-defined message having 3D motion information

A method comprising performing image re-projection on each of a plurality of first images of a scene, thereby to generate a plurality of re-projected second images of the scene, wherein each first image of the scene is captured by a respective camera of a first multi-directional image capture apparatus and each second image of the scene is associated with a respective virtual camera; processing the plurality of second images based on a previously generated virtual three dimensional model of the scene, thereby to generate respective positions of the virtual cameras associated with the second images; and determining a position of the first multi-directional image capture apparatus based on one or more of the generated positions of the virtual cameras.

Methods and apparatuses for determining positions of multi-directional image capture apparatuses

The invention relates to a method and an apparatus implementing the method. The method comprises extracting region proposals from a media content; selecting a set of region proposals corresponding to an object in the media content; identifying objects of interest; determining an object-specific representation by an iterative tracking method; sampling positive examples from the set of tracked region proposal groups obtained from the iterative tracking method; and performing object segmentation.

Method, an apparatus and a computer program product for video object segmentation

In an example embodiment a method, apparatus and computer program product are provided. The method includes extracting a first set of target-object regions and at least one set of non-target object regions from a plurality of regions of a video content based at least on likelihood information. A plurality of unlabeled regions of the video content are classified based on the first set of target-object regions and the at least one set of non-target object regions to generate a second set of target object regions that is denser than the first set of target object regions. A model is learned for modelling at least one of the target object and non-target objects of the video content based at least on the second set of target object regions. The target object in the video content is segmented based on the model and the second set of target object regions.

Method, apparatus and computer program product for segmentation of objects in media content

There is provided methods, apparatuses and computer program products for image segmentation in which pixel data of an image and information of a first set of pixels of the image indicative of pixels belonging to a foreground of the image are obtained. A color model of the image is estimated. To obtain a geodesic distance map at least one geodesic distance from a pixel of the first set of pixel to another pixel of the image which is not in the first set of pixels is determined. An energy function is optimized on the basis of the geodesic distance map to obtain a segmentation of the image.

Apparatus, a method and a computer program for image processing

In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of a plurality of superpixels of an input image and an object selection input for selecting part of a foreground object in the input image. The method includes determining first set of superpixels as first set of foreground seeds and second set of superpixels as first set of background seeds. The method includes filtering the first set of background seeds to generate second set of background seeds based on geodesic distances. The method includes adding superpixels of first set of unclassified superpixels to the second set of background seeds to generate third set of background seeds, and adding superpixels of second set of unclassified superpixels to the third set of background seeds to generate fourth set of background seeds based on applying a classifier on the second set of unclassified superpixels.

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Tampere, Finland

Tinghuai Wang