Abstract: Disclosed is an electronic device providing optimal image quality according to an image characteristic. The electronic device includes: interface circuitry and a processor configured to correct an image based on a plurality of frames acquired from a signal received through the interface circuitry based on whether the image has a predefined characteristic, wherein the processor is further configured to perform recognition of the predefined characteristic based on a frame in which a pixel change between two consecutive frames among the plurality of frames is greater than or equal to a threshold.

Abstract: Embodiments are generally directed to methods and apparatuses for determining a game status. An embodiment of a method for determining a game status comprises: detecting players and a ball in a plurality of continuous frames captured by a plurality of cameras; obtaining tracking data of the players and the ball of the plurality of continuous frames; and determining a game status for each of the plurality of continuous frames based on the tracking data of the players and the ball of the plurality of continuous frames.

Abstract: A computer-vision-based fatigue crack detection approach using a short video is described. Feature tracking is applied to the video for tracking the surface motion of the monitored structure under repetitive load. Then, a crack detection and localization algorithm is established to search for differential features at different frames in the video. The effectiveness of the proposed approach is validated through testing two experimental specimens with in-plane and out-of-plane fatigue cracks. Results indicate that the proposed approach can robustly identify fatigue cracks, even when the cracks are under ambient lighting conditions, surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes due to crack closure. The approach enables accurate quantification of crack openings under fatigue loading with good accuracy.

Abstract: An imaging apparatus according to an embodiment includes: an imaging unit having a pixel region in which a plurality of pixels is arranged; a readout controller that controls readout of pixel signals from pixels included in the pixel region; a first unit-of-readout setting unit that sets a unit of readout as a part of the pixel region, for which the readout controller performs readout of the pixel signal; an image output unit that outputs a first image based on the pixel signal read out from the unit of readout to a subsequent stage; a second unit-of-readout controller that controls the unit of readout in which the readout controller performs readout of the pixel signal; and a recognition unit that learns training data for each of the units of readout, performs a recognition process on the pixel signal for each of the units of readout, and outputs a recognition result.

Abstract: A system, computer readable storage medium, and computer-implemented method presents video search results responsive to a user keyword query. The video hosting system uses a machine learning process to learn a feature-keyword model associating features of media content from a labeled training dataset with keywords descriptive of their content. The system uses the learned model to provide video search results relevant to a keyword query based on features found in the videos. Furthermore, the system determines and presents one or more thumbnail images representative of the video using the learned model.

Abstract: A self-contained, low-cost, low-weight guidance system for vehicles is provided. The guidance system can include an optical camera, a case, a processor, a connection between the processor and an on-board control system, and computer algorithms running on the processor. The guidance system can be integrated with a vehicle control system through “plug and play” functionality or a more open Software Development Kit. The computer algorithms re-create 3D structures as the vehicle travels and continuously updates a 3D model of the environment. The guidance system continuously identifies and tracks terrain, static objects, and dynamic objects through real-time camera images. The guidance system can receive inputs from the camera and the onboard control system. The guidance system can be used to assist vehicle navigation and to avoid possible collisions. The guidance system can communicate with the control system and provide navigational direction to the control system.

Abstract: Image processing apparatus responsive to successive groups of images of an object captured at the same time by two or more spaced apart image capturing devices and to depth information indicating a three-dimensional location of the object relative to at least one distance measuring device includes: a frame selecting unit configured to select a set of the successive image groups; a model generating unit configured to generate a three dimensional model, for each image group in the set of image groups, from images captured at the same time by the two or more spaced apart image capturing devices and from the depth information, and to map a texture to the generated three dimensional model; and a composition unit configured to generate a stroboscopic image, with respect to a stroboscopic image viewpoint, by superposing the three dimensional models generated by the model generating unit on a predetermined background.

Abstract: An image processing device includes circuitry configured to perform an effect process on at least one 3D model of a plurality of 3D models generated from a plurality of viewpoint images captured from a plurality of viewpoints at different times.

Abstract: Methods, systems, and/or apparatuses are described for detecting relevant motion of objects of interest (e.g., persons and vehicles) in surveillance videos. As described herein input data based on a plurality of captured images and/or video is received. The input data may then be pre-processed and used as an input into a convolution network that may, in some instances, have elements that perform both spatial-wise max pooling and temporal-wise max pooling. Based on The convolution network may be used to generate a plurality of prediction results of relevant motion of the objects of interest.

Abstract: A computer-implemented method performed in connection with a video comprising a plurality of video frames, the computer-implemented method being performed in a computerized system comprising a processing unit and a memory, the computer-implemented method involving: using the processing unit to detect a plurality of content components within at least one of the plurality of video frames; based on a video modification instruction received from a user, modifying at least one of the plurality of the detected content components; storing an information on the modified at least one of the plurality of the detected content components in the memory; using the central processing unit to overlay the modified at least one of the plurality of the detected content components over the video; and causing the video with the overlaid modified content component to be displayed to the user.

Abstract: An image processing device configured to synthesize a first image and a second image shot in two or more exposure times different in length in a parallel way within one frame time, includes: a level adjuster configured to adjust the first image and output a corrected first image having a luminance level substantially aligned with a luminance level of the second image; a movement detector configured to calculate absolute values of differences between positionally corresponding pixel pairs in the corrected first image and the second image and accumulate the calculated absolute value on an image block basis to obtain a movement detection amount on the image block basis; a movement blending ratio calculator configured to smoothen the amounts of detected movement and calculate a movement blending ratio between positionally corresponding pixel pairs based on the smoothened amounts of detected movement; and an image synthesizer configured to generate a movement-adaptive image by synthesizing pixels at the correspond

Abstract: The present disclosure is directed to apparatuses, systems and methods for automatically compressing digital image data. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically compressing digital image data that is representative of a series of digital images.

Abstract: A video classification method and apparatus relate to the field of electronic and information technologies, so that precision of video classification can be improved. The method includes: segmenting a video in a sample video library according to a time sequence, to obtain a segmentation result, and generating a motion atom set; generating, by using the motion atom set and the segmentation result, a motion phrase set that can indicate a complex motion pattern, and generating a descriptive vector, based on the motion phrase set, of the video in the sample video library; and determining, by using the descriptive vector, a to-be-detected video whose category is the same as that of the video in the sample video library. The method is applicable to a scenario of video classification.

Abstract: The subject matter disclosed herein relates to distribution of media content.

Abstract: The techniques and systems described herein are directed to isolating part-centric motion in a visual scene and stabilizing (e.g., removing) motion in the visual scene that is associated with camera-centric motion and/or object-centric motion. By removing the motion that is associated with the camera-centric motion and/or the object-centric motion, the techniques are able to focus motion feature extraction mechanisms (e.g., temporal differencing) on the isolated part-centric motion. The extracted motion features may then be used to recognize and/or detect the particular type of object and/or estimate a pose or position of a particular type of object.

Abstract: An obstacle detection apparatus includes an image input unit inputting a image sequence captured by an image sensor, a velocity detector deriving velocity vectors representing motions of a to-be-detected point and a to-be-compared point on each image of the image sequence, a predictive velocity vector calculator calculating a predictive velocity vector of the to-be-detected point using a detected velocity vector of the to-be-compared point, an obstacle detector detecting the to-be-detected point as a point on the obstacle when a horizontal component of a detected velocity vector of the to-be-detected point is larger than a horizontal component of the predictive velocity vector by a given threshold, and an obstacle information output unit outputting information on the to-be-detected point detected as the obstacle.

Abstract: A method for measuring motion at a horizontal and vertical position between video fields of opposite parity comprising the steps of measuring the signal values of at least two vertically adjacent pixels from a video field of one parity and at least two vertically adjacent pixels from a video field of the opposite parity such that when taken together, the pixels represent contiguous samples of an image at said horizontal and vertical position, and determining whether the signal value of any of the pixels lies between the signal values of adjacent pixels in the field of opposite parity and in response outputting a zero motion value, otherwise, outputting a motion value equal to the lowest absolute difference between any of the pixels and its closest adjacent pixel in the field of opposite parity.