Abstract: The present disclosure provides a computer-implemented method and system for hardware, channel, language and ad length agnostic detection of multi-lingual televised advertisements. The detection is performed across live streams of media content of one or more broadcasted channels. The method includes selection of a set of frames per second from a pre-defined set of frames. The method includes extraction of a pre-defined number of keypoints from each selected frame and derivation of a pre-defined number of binary descriptors from the extracted keypoints. The method includes creation of a special pyramid of the binary descriptors and accessing a second vocabulary of binary descriptors. The method includes comparison of each spatially identifiable binary descriptor from the first vocabulary with spatially identifiable binary descriptors in clusters of the second vocabulary. The method includes progressively scoring each selected frame and detection of the first ad in the live streams of the media content.

Abstract: An object recognition system is provided that includes a device configured to capture a video sequence formed from unlabeled testing video frames. The system includes a processor configured to pre-train a recognition engine formed from a reference set of CNNs on a still image domain that includes labeled training still image frames. The processor adapts the recognition engine to a video domain to form an adapted recognition engine, by applying a non-reference set of CNNs to a set of domains that include the still image and video domains and a degraded image domain. The degraded image domain includes labeled synthetically degraded versions of the labeled training still image frames included in the still image domain. The video domain includes random unlabeled training video frames. The processor recognizes, using the adapted engine, a set of objects in the video sequence. A display device displays the set of recognized objects.

Abstract: According to one embodiment, a monitoring system includes a monitoring terminal and a server. The monitoring terminal includes a detector, a tracking unit, a first selector, and a transmitter. The server includes a receiver, a second selector, a collation unit, and an output unit. The receiver receives a first best shot images from the monitoring terminal. The second selector performs second selection processing, as part of a predetermined selection processing other than a first selection processing, of selecting a second best shot image suitable for collation with a predetermined image from among the first best shot images. The collation unit performs collation processing of collating the second best shot image with the predetermined image. The output unit outputs a result of the collation processing.

Abstract: An image processing device includes a memory, and a processor coupled to the memory, wherein the processor is configured to execute acquiring a captured image including an original document region, detecting a color component value of a predetermined area in the captured image, detecting an edge in the predetermined area to acquire an edge amount indicating any one or both of a density of the edge and an edge intensity, and identifying, based on the color component value and the edge amount, a difference between a background of the original document region and a background of a background region obtained by removing the original document region from the captured image.

Abstract: A distance-based score approximation having improved computational efficiency is provided. Responsive to receiving a score request, a computing entity identifies an observation point based on a location indicated in the score request and defines a set of annuli comprising a plurality of concentric annuli centered on the observation point and defined by a predetermined maximum radius. The computing entity queries a geographic database for map information corresponding to geometry elements located within the predetermined maximum radius of the observation point and determines an intersection of each geometry element with each annulus. The computing entity determines a contribution for each intersection based at least in part on a size of the intersection, a measure assigned to the corresponding geometry element, and a representative radius of the corresponding annulus.

Abstract: The technology described herein recalibrates a structured light sensor in the field using time-of-flight sensor data. Structured light sensors are sensitive to mechanical changes that result in decreased accuracy. A structured light system calculates the range to an object by comparing a reference image to the actual image of the scene. The reference image is what the projected light pattern would look like on a flat object at a known distance. When the projected image changes, the reference image no longer matches the projected pattern. The calibration technology described herein captures a new reference image based on the current sensor characteristics using a time-of-flight capable sensor as the structured light imaging sensor.

Abstract: Artistic styles extracted from one or more source images may be applied to one or more target images, e.g., in the form of stylized images and/or stylized video sequences. The extracted artistic style may be stored as a plurality of layers in a neural network, which neural network may be further optimized, e.g., via the fusion of various elements of the network's architectures. An optimized network architecture may be determined for each processing environment in which the network will be applied. The artistic style may be applied to the obtained images and/or video sequence of images using various optimization methods, such as the use of scalars to control the resolution of the unstylized and stylized images, temporal consistency constraints, as well as the use of dynamically adjustable or selectable versions of Deep Neural Networks (DNN) that are responsive to system performance parameters, such as available processing resources and thermal capacity.

Abstract: An abnormality detection device includes: an estimator which estimates the amount of movement of a mobile body based on an image taken by a camera mounted on the mobile body; and a determiner which determines an abnormality in the camera by obtaining estimated information on the amount of movement of the mobile body as obtained in the estimator and actually observed information on the movement of the mobile body as detected by an external sensor, other than the camera, mounted on the mobile body.

Abstract: This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.

Abstract: An augmented reality AR device may be communicatively connected to a remote network management platform configured to support a managed network. The AR device may capture an image of a real object in the field of view of an imaging component of the AR device. The real object may be recognized as a known managed object of the managed network. The AR device may also concurrently determine context information indicating a location or physical environment. The AR device may then transmit an identifier of the known managed object and the context information in a message to the management platform. In response, the AR device may receive data associated with the known managed. The AR device may then display a virtual object in a virtual space superimposed on the captured image of the real object, where the virtual object and the virtual space are based on the received management data.

Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.

Abstract: An image difference detection device includes a detection unit configured to detect presence or absence of a difference between a first region within a first image and a second region corresponding to the first region within a second image on the basis of one or both of a code amount of each of encoded blocks in first and second encoded data obtained by encoding the first image and the second image and an index value obtained from encoding information of each of the encoded blocks.

Abstract: Systems, apparatuses, and methods may provide for technology to process multi-resolution images by identifying pixels at a boundary between pixels of different resolutions, and selectively smoothing the identified pixels.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: A system, according to various embodiments, receives images such as photographs and videos from one or more portable computing devices associated with one or more individuals (e.g., construction workers or landscapers) while the portable computing devices are in a particular position within a particular location at a particular time. The system determines a virtual position within a 3-D representation of the particular location that generally corresponds to the particular position and combines the images with the 3-D representation to generate an enhanced 3-D representation of the particular location. This may allow, for example, owners of a particular property to track and quickly understand construction and landscaping work that has been done on their property and to easily contact those individuals regarding that work.

Abstract: Images of a scene may be captured by a set of image capture devices. Overlapping areas of the images may be identified based on the topology of the set of image capture devices. Frontiers within the overlapping areas of the images may be identified based on borders of the images. Sample points for the images may be distributed along the frontiers. Warp parameters including an anti-symmetric warping portion and a symmetric warping portion may be determined at the sample points. Displacement values may be determined at the sample points based on the warp parameters. Warp maps for the images may be determined based on diffusion of the displacement values. Displacement maps for the images may be determined based on interpolation of the warp maps. The images may be modified based on the displacement maps.

Abstract: An image processing method includes steps of receiving an image sequence; when at least one object appears in the image sequence, analyzing a moving trajectory of each object; extracting at least one characteristic point from each moving trajectory; classifying the at least one characteristic point of each moving trajectory within a predetermined time period into at least one cluster; and storing at least one characteristic parameter of each cluster.

Abstract: The method according to the invention is based on a first image of a first eye region of a person and a second image of a second eye region of the person, wherein the first eye region contains one of the eyes of the person, for example the right eye, and the second eye region contains the other eye of the person, for example the left eye; one of the images is mirrored, and the mirrored and the non-mirrored image are combined in the position space and/or in the feature space, in order to generate a template of an overlaid image. The template contains biometric features for person recognition.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: Field extraction from a form image includes identifying a target field of the form image, defining a patch from the form image based on the target field, and encoding the patch using a color encoding scheme to obtain an encoded patch. Field extraction further includes applying a trained classifier to the encoded patch to identify a relationship between a field value and a field identifier, and extracting the field value from the form image according to the relationship.