Embodiments of the present disclosure disclose a method and a system for video anomaly detection. The system is configured to collect a sequence of input video frames of an input video of a scene. In addition, the system is configured to partition each input video frame of the sequence of input video frames into a plurality of input video patches. Further, the system is configured to process each of the plurality of input video patches with one or more classifiers. Each of the one or more classifiers corresponds to a deep neural network trained to estimate one or more attributes of the plurality of input video patches from an output of a penultimate layer of the deep neural network. Furthermore, the system is configured to compare the output of the penultimate layer. The system is further configured to detect an anomaly based on the output of the penultimate layer.

Methods, systems, and apparatus for receiving, by a wildfire modeling system, region of interest (ROI) data representative of pixels that represent a geographical ROI, generating, by the wildfire modeling system, transition probabilities for each pixel in the ROI data, determining, by the wildfire modeling system, chained probabilities along each path in a set of paths within the ROI, adjusting, by the wildfire modeling system, chained probabilities based on a likelihood of ignition of a starting pixel represented in the ROI data, combining, by the fire modeling system, the adjusted chained probabilities to provide connectivity data that represents respective likelihood of spread of a wildfire from the starting pixel to each other pixel within the ROI, and displaying a connectivity map that graphically represents connectivity data of each pixel within the ROI.

Provided are an inspection support device, an inspection support method, and a program capable of reducing complexity of creating a damage diagram. The inspection support device includes a processor that supports creation of a damage diagram of a structure, in which the processor acquires image data of information including a structural drawing of a target structure on a medium and damage information related to damage added by a user on the medium, recognizes the damage information by image recognition from the acquired image data, acquires drawing data corresponding to the structural drawing, aligns the structural drawing of the image data with the drawing data, and draws the damage information as a damage graphic at a corresponding position of the drawing data to create the damage diagram.

A method of tracking an object includes performing a hybrid search over a sequence of frames. The hybrid search includes periodically performing a global search on selected frames of the sequence of frames and performing a local search on frames between the selected frames of the global search. The method also includes updating a similarity function based on a result of the hybrid search. The method further includes tracking the object based on the hybrid search.

Provided are an image detection device, an image detection method and a program, which are capable of improving correspondence to a target deformation by optimizing a template shape, when performing target detection using template matching. An image detection device 100 for detecting a target from an input image comprises: a template generation unit 10 that generates a template for detecting a target; a mask generation unit 20 that generates a mask which shields a portion of the template, on the basis of temporal variations of a feature point extracted from an area including the image target; and a detection unit 30 that detects the target from the image using the template a portion of which is shielded by the mask.

Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation by: determining the vectors pixels that correspond to the image pixels affected by the image editing operation and mapping the pixel values of the image pixels represented by the determined offset vectors to the affected pixels. According to this mapping, the pixel values of the affected pixels are set, effective to perform the image editing operation.

A facial recognition method includes identifying a first makeup pattern in a region of interest (ROI) of a first image, applying one of the first makeup pattern and a candidate makeup pattern to a ROI of a second image corresponding to the ROI of the first image to generate a third image and recognizing a face based on the first image and the third image.

Provided are an image processing device and the like which implement personal privacy protection while suppressing a reduction in visibility for an image. The image processing device is provided with: a memory storing instructions; and one or more processors configured to execute the instructions to: detect a person region that is a region where a person appears in an image captured by a camera device; and perform, on the person region, privacy processing a strength of which differs according to a depth associated with coordinates of the person region or a predetermined index related to the depth.

An apparatus includes at least one processor, and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the at least one processor to identify a partial image corresponding to an area in an image in which performance of inference by a learning model that performs predetermined inference on an input image is less than or equal to a threshold, collect a similar image similar to the identified partial image, and based on additional images including the collected similar image, improve a result of the inference by the learning model targeted at a test environment different from a training environment of the learning model.

Detecting fine-grained similarity in image includes determining a core area of a search image by generating an image salient map from a plurality of layers of the search image and determining a connected area based on the image salient map. Feature descriptors are generated from the core area of the search image. A plurality of capsule vectors are generated from different ones of a plurality of keypoints of the feature descriptors. Capsule vectors of the search image are compared with capsule vectors of each image of the dataset to generate a top-K matrix. Similarity scores for the top-K matrix are calculated. One or more image of the dataset having fine-grained similarity with the search image are selected based a bundled similarity score for each image of the dataset. The bundled similarity score is a summation of the similarity scores of the image.