Disclosed herein is a method for encoding a feature map. The method may include arranging multiple channels based on similarity therebetween for a feature map having the multiple channels, rearranging the arranged multiple channels so as to be adjacent to each other in a feature map channel having a matrix form, and generating an encoded feature map by converting a feature value corresponding to the feature map channel from a real number to an integer.

A system capable of determining which recognition algorithms should be applied to regions of interest within digital representations is presented. A preprocessing module utilizes one or more feature identification algorithms to determine regions of interest based on feature density. The preprocessing modules leverages the feature density signature for each region to determine which of a plurality of diverse recognition modules should operate on the region of interest. A specific embodiment that focuses on structured documents is also presented. Further, the disclosed approach can be enhanced by addition of an object classifier that classifies types of objects found in the regions of interest.

A method of training an image-text retrieval model, a method of multimodal image retrieval, an electronic device and a storage medium, each relating to the technical field of artificial intelligence, and in particular, to fields of computer vision and deep learning technologies. Sample data including a sample text and a sample image is acquired. The sample text includes a sample text in a first language and a sample text in a second language. The sample text in the first language and the sample text in the second language are processed by using the text encoding sub-model to obtain a sample text feature of the sample data. The sample image is processed by using the image encoding sub-model to obtain a sample image feature of the sample data. The image-text retrieval model is trained according to the sample text feature and the sample image feature.

A data storage device and method for efficient image searching are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of images and a plurality of keys in the memory, wherein each key of the plurality of keys is generated from a respective image of the plurality of images; receive, from a host, a key generated from a target image desired by the host; and return, to the host, an image from the stored plurality of images that is associated with a key that matches the key received from the host. Other embodiments are provided.

An image processing circuit capable of detecting an edge component includes: a selecting circuit acquiring the brightness values of pixels of an image according to the position of a target pixel and a processing region, wherein the pixels include N horizontal lines and M vertical lines; a brightness-variation calculating circuit generating N horizontal-line-brightness-variation values according to brightness variation of the N horizontal lines, and generating M vertical-line-brightness-variation values according to brightness variation of the M vertical lines; a brightness-variation determining circuit choosing a horizontal-line-brightness-variation representative value among the N horizontal-line-brightness-variation values, choosing a vertical-line-brightness-variation representative value among the M vertical-line-brightness-variation values, and choosing a brightness-variation representative value between the two representative values; an energy-variation calculating circuit generating an energy-variation value according to the brightness values of the pixels; and an edge-score calculating circuit generating an edge score of the target pixel according to the brightness-variation representative value and energy-variation value.

A quantification system (700) is described that includes: at least one input (710) configured to provide two input medical images and two locations of interest in said input medical images that correspond to a same anatomical region; and a mapping circuit (725) configured to compute a direct quantification of change of said input medical images from the at least one input (710).

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

This disclosure provides methods, devices, and systems for low-light imaging. The present implementations more specifically relate to selecting images that can be used for training a neural network to infer denoised representations of images captured in low light conditions. In some aspects, a machine learning system may obtain a series of images of a given scene, where each of the images is associated with a different SNR (representing a unique combination of exposure and gain settings). The machine learning system may identify a number of saturated pixels in each image and classify each of the images as a saturated image or a non-saturated image based on the number of saturated pixels. The machine learning system may then select the non-saturated image with the highest SNR as the ground truth image, and the non-saturated images with lower SNRs as the input images, to be used for training the neural network.

A method, system, and device for planning a path for a forced landing of an aircraft based on image recognition are provided. The method includes: calculating an endurance distance of an aircraft based on sensor data and meteorological information; obtaining an alternative landing area by a satellite image containing contour information and a terrain image recognition model; obtaining a current satellite image of the alternative landing area and determining a landing area; and selecting a landing site by a landing site decision model and generating a path for a forced landing, such that the aircraft completes a forced landing task according to the path for the forced landing. The method, system, and device can automatically recognize image information, select a best landing site, and generate a path for a forced landing to assist a pilot in performing a forced landing task.

A data storage device and method for efficient image searching are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of images and a plurality of keys in the memory, wherein each key of the plurality of keys is generated from a respective image of the plurality of images; receive, from a host, a key generated from a target image desired by the host; and return, to the host, an image from the stored plurality of images that is associated with a key that matches the key received from the host. Other embodiments are provided.