An image processing system includes a first memory, a second memory, an area sorting unit and a merging unit. The first memory is configured to store a number of rider object boxes and a number of two-wheel carrier object boxes. The area sorting unit is configured to: read the rider object boxes in the first memory; obtain an rider object box area of each rider object box; and arrange the rider object boxes according to the order of the rider object box areas from large to small, and store them in the second memory. The merging unit is configured to: for each rider object box in the second memory, obtain the optimal-matching two-wheel carrier object box from the two-wheel carrier object boxes in the first memory; and create an emerged object box, wherein the emerged object box surrounds the rider object box and its optimal-matching two-wheel carrier object box.

Methods, system, and media for comparing a set of images to determine the existence and location of any differences between the image set. The differences may be located using image comparison techniques such as SURF and Blob Detection, as well as through techniques used to identify areas of data sliding and match probabilities. A logical match probability, as well as a physical match probability, may be included in an output report with a result image highlighting the differences between the comparison images in the image set.

A system and method of identifying a region of interest using a low-field magnetic resonance imaging (MRI) system is disclosed. The method comprises obtaining a T2-weighted image from the low-field MRI system, wherein the T2-weighted image comprises a slice, annotating a first region on the slice, wherein the first region corresponds to a suspicious region, and annotating a second region on the slice, wherein the second region corresponds to a non-suspicious region. The second region comprises the same size as the first region. The method further comprises computing a first texture feature value for the first region, computing a second texture feature value for the second region, and comparing the first texture feature value to the second texture feature value.

Methods and systems for identifying a region of interest in breast tissue utilize artificial intelligence to confirm that a target lesion identified during imaging the breast tissue using a first imaging modality (e.g. x-ray imaging) has been identified using a second imaging modality (e.g. ultrasound imaging). A computing system operating a lesion matching engine utilizes a machine learning classifier algorithm trained on cases of x-ray images and corresponding ultrasound images in which lesions were identified for further analysis. The lesion matching engine analyzes a target lesion identified with x-ray imaging and a potential lesion identified with ultrasound imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

An authenticity collation apparatus for collating authenticity of an information medium on which a collation image is printed is proposed. The authenticity collation apparatus includes a processor configured to acquire a second captured image obtained by capturing the collation image at a time of collation, and determine a collation region for collation between a first captured image obtained by capturing the collation image at a time of issuance of the information medium and the second captured image, based on prediction information of a density change at a time point of acquiring the second captured image, the prediction information being based on an elapsed time from a time point of acquiring the first captured image.

Systems and methods for image processing, and specifically for generating object-agnostic image representations, are described. Embodiments of the present disclosure receive a training image including a foreground object and a background, remove the foreground object from the training image to obtain a modified training image, inpaint a portion of the modified training image corresponding to the foreground object to obtain an inpainted training image, encode the training image and the inpainted training image using a machine learning model to obtain an encoded training image and an encoded inpainted training image, and update parameters of the machine learning model based on the encoded training image and the encoded inpainted training image.

An information processing apparatus includes an acquisition unit configured to acquire images captured in time series and distance information in a depth direction on a plurality of areas in each of the images, a detection unit configured to detect a candidate area of an object to be a tracking target from each of the images based on an image feature of each of the images, an estimation unit configured to estimate an occlusion state indicating whether the object to be the tracking target is occluded by another object different from the tracking target for the candidate area detected from each of the images based on time-series data of the distance information, and a determination unit configured to determine the candidate area of the object to be the tracking target from the candidate area detected from each of the images based on an estimation result of the occlusion state.

A method is disclosed that includes receiving, by a processing device, a plurality of images of a test assembly. The processing device selects a component in the test assembly and an image of the plurality of images of the test assembly as received. For the component as selected and the image as selected, the processing device compares a plurality of portions of the image as selected to a corresponding plurality of portions of a corresponding profile image and computing a matching score for each of the plurality of portions. The processing device selects a largest matching score from the matching score for each of the plurality of portions as a first matching score for the component as selected and the image as selected. The first matching score is stored for the component as selected and the image as selected.

An apparatus for determining low visibility of an environment around a vehicle is disclosed. The apparatus obtains location information indicating a location of the vehicle. The apparatus further detects, by using a map database, a road segment satisfying a road attribute requirement proximate to the location based on the location information. The apparatus further obtains, from the map database, at least one attribute associated with the road segment. The apparatus further determines a position of the vehicle with respect to the road segment based on the at least one road attribute. The apparatus further acquires at least one image via at least one image capture sensor equipped by the vehicle based on the position. The apparatus further determines visibility information indicative of visibility of the environment of the vehicle based on the at least one image. The apparatus further outputs the visibility information.

A computer implemented method and system are provided for parsing a multi-dimensional space based on a series of observations and displacements performed by an agent in the space, to find an attribute of the space. The method includes making sequential observations and displacements from locations of the agent in the space, and comparing the observations and displacements to a set of stored observations and displacements to identify a hypothesis of the attribute of the space and to test the hypothesis, by obtaining an observation comparison measure, and/or a displacement comparison measure, and adjusting, maintaining or confirming the hypothesis based on the observation comparison measure and/or the displacement comparison measure.