A method includes receiving a first image that is captured at a first time. The method also includes detecting a location of a first object in the first image. The method also includes determining a region of interest based at least partially upon the location of the first object in the first image. The method also includes receiving a second image that is captured at a second time. The method also includes identifying the region of interest in the second image. The method also includes detecting a location of a second object in a portion of the second image that is outside of the region of interest.

Disclosed herein is a method for detecting an anomaly state based on screen output. The method includes receiving the output screen of a target device to be monitored, setting a target region to be examined in the output screen of the target device to be monitored, calculating a feature value vector corresponding to the state of the target region to be examined, calculating an anomaly score using a pretrained auto-encoder by receiving the feature value vector as input, and determining whether the target device to be monitored is anomalous using the anomaly score.

A high-resolution image matching method and system are provided. The method includes performing regional fidelity down-sampling on an initial high-resolution image to obtain a multi-level low-resolution image, performing local matching on the obtained multi-level low-resolution images using a method with global probes to obtain a matching result of the low-resolution images, and performing reverse refinement on the obtained matching result of the low-resolution image using overall consistency of the image matching, to obtain the matching results of the high-resolution images at all levels until the matching results of the initial resolution images are obtained, so as to reduce the computational complexity of the image matching process and improve the accuracy of the matching result, and then the matching result of the high-resolution image is obtained through reverse refinement based on the overall consistency.

An image matching method includes extracting, from a first image of an object, a landmark patch including a landmark point of the object, extracting, from a second image of the object, a target patch corresponding to the landmark patch; and determining a target point in the second image corresponding to the landmark point based on a matching between the landmark patch and the target patch.

A method generates an image processing model to calculate a virtually stained image from a microscope image. The image processing model is trained using training data comprising microscope images as input data into the image processing model and target images that are formed via chemically stained images registered locally in relation to the microscope images. The image processing model is trained to calculate virtually stained images from the input microscope images by optimizing an objective function that captures a difference between the virtually stained images and the target images. After a number of training steps, at least one weighting mask is defined using one of the chemically stained images and an associated virtually stained image calculated after the number of training steps. In the weighting mask, one or more image regions are weighted based on differences between locally corresponding image regions in the virtually stained image and in the chemically stained image. Subsequent training considers the weighting mask in the objective function.

A device for verifying a subject includes: a device body comprising a processor and a biometric system; wherein the biometric system comprises a first image capture device and a second image capture device, in which the first image capture device is configured to define a spatial region and the second image capture device is configured to capture an image of a subject within said spatial region, and the processor is configured to conduct an identification process on the captured image of the subject within the spatial region.

An image processing apparatus according to the present invention includes: an extracting unit configured to extract a candidate image, which is an image of a candidate region specified in accordance with a preset criterion, from a target image to be a target for an annotation process, and also extract a corresponding image, which is an image of a corresponding region corresponding to the candidate region, from a reference image that is an image corresponding to the target image; a displaying unit configured to display the candidate image and the corresponding image so as to be able to compare the images with each other; and an input accepting unit configured to accept input of input information for the annotation process for the candidate image.

Disclosed are an apparatus and a method for recognizing traffic signs. The apparatus and the method for recognizing traffic signs includes an image sensor, a neuromorphic system in which a plurality of neurons storing a feature pattern vector and a content pattern vector of the traffic sign are connected by a parallel bus, and a control unit that normalizes a window of a predetermined size for a region of interest set in an image frame inputted from the image sensor unit by making the window slide in such a way to overlap by a predetermined pixel value, generates a first input vector that vectorizes, extracts a candidate region of the traffic sign based on feature pattern information of a neuron having a feature pattern vector most similar to the inputted first input vector among the plurality of neurons stored in the neuromorphic system, stores the coordinates of the extracted traffic sign candidate region, converts the image size of the extracted candidate region, normalizes a window of a predetermined size for the candidate region of the converted image size, by making the window slide in such a way to overlap by a predetermined pixel value, generates a second input vector that vectorizes the normalized window, determines traffic sign content information of a neuron having a content pattern vector most similar to the inputted second input vector among the plurality of neurons stored in the neuromorphic system, stores the determined traffic sign content information, and recognizes the location and content of the traffic sign based on the coordinates of the stored candidate regions and the content information of the stored traffic sign when the traffic sign disappears.

An image acquisition unit acquires a plurality of medical images, and a common region determination unit determines common regions commonly present in the plurality of medical images. An enlargement ratio determination unit determines an enlargement ratio, which is for displaying the plurality of common regions in the plurality of medical images with the same size, for each of the plurality of medical images. A display control unit applies the determined enlargement ratio to the plurality of common regions, and displays images of the plurality of common regions after applying the enlargement ratio on a display.

Images may be analyzed to determine a visually cohesive color palette, for example by comparing a subset of the colors most frequently appearing in the image to a plurality of color schemes (e.g., complementary, analogous, etc.), and potentially modifying one or more of the subset of colors to more accurately fit the selected color scheme. Various regions of the image are selected and portions of the regions having one or more colors of the color palette are extracted and classified to generate and compare feature vectors of the patches to previously-determined feature vectors of items to identify visually similar items. The visually similar items are selected for presentation in various ways, such as by choosing an outfit of visually-similar apparel items based on the locations of the corresponding colors in the image, etc.