A method includes: capturing one or more images of an unorganized collection of items inside a first machine; determining one or more item types of the unorganized collection of items from the one or more images, comprising: dividing a respective image in the one or more images into a respective plurality of sub-regions; performing feature detection on the respective plurality of sub-regions to obtain a respective plurality of regional feature vectors, wherein a regional feature vector for a sub-region indicates characteristics for a plurality of predefined local item features for the sub-region; generating an integrated feature vector by combining the respective plurality of regional feature vectors; and applying a plurality of binary classifiers to the integrated feature vector; and selecting a machine setting for the first machine based on the determined one or more clothes type in the unorganized collection of items.

Methods, apparatus, and processor-readable storage media for automated image analysis using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data associated with at least one event; characterizing a plurality of attributes of the image data by processing at least a portion of the image data using one or more artificial intelligence techniques and processing metadata associated with at least a portion of the image data using one or more rule-based techniques; generating a determination as to whether one or more portions of the image data have been modified based at least in part on the plurality of characterized attributes of the image data; and performing one or more automated actions, based at least in part on the determination, in connection with at least one user request related to the image data associated with the at least one event.

A method of segmenting objects in an image using artificial intelligence includes obtaining, by an analysis device, an image containing at least one object; inputting the image acquired by the analysis device into a segmentation model; and segmenting, by the analysis device, objects in the acquired image based on values output by the segmentation model, wherein an image containing at least one object is used as learning data, the size of objects in the learning data is estimated as part of the learning process, different weightings are given to pixels of which the objects consist according to the estimated size of the objects, and the segmentation model is trained based on a loss function in which the given weightings are considered, a size-weighted loss function.

The present disclosure provides a method and an apparatus for detecting a 3D image, an electronic device, and a computer-readable medium. The method for detecting a 3D image includes layering a 3D image to obtain at least one 3D subimage. The 3D subimage contains a plurality of 2D images. The method includes performing an intra-layer clustering on the 3D subimage to obtain a superpixel grid. The method includes inputting the superpixel grid into a neural network for detecting. The method includes detecting, in response to detecting an object in the superpixel grid, the 3D subimage forming the superpixel grid containing the object to obtain and output a detection result.

The feature extraction device according to one aspect of the present disclosure comprises: a reliability determination unit that determines a degree of reliability with respect to a second region, which is a region that has been extracted as a foreground region of an image and is within a first region that has been extracted from the image as a partial region containing a recognition subject, said degree of reliability indicating the likelihood of being the recognition subject; a feature determination unit that, on the basis of the degree of reliability, uses a first feature which is a feature extracted from the first region and a second feature which is a feature extracted from the second region to determine a feature of the recognition subject; and an output unit that outputs information indicating the determined feature of the recognition subject.

A computer-implemented method for automatically generating metadata tags for a medical image includes receiving a medical image and automatically identifying a set of body landmarks in the medical image using one or more machine learning models. A set of rules are applied to the set of body landmarks to identify anatomical objects present in the image. As an alternative to using the set of rules, in some embodiments, one or more machine learning models to the set of body landmarks to identify anatomical objects present in the image. Once the anatomical objects are identified, metadata tags corresponding to the anatomical objects are generated and stored in the medical image. Then, the medical image with the metadata tags is transferred to a data repository.

A method for the clustering and identification of animals in acquired images based on physical traits is provided, where a trait feature is a scalar or vector quantity that is a property of a trait and trait distance is a measure of discrepancy between the same trait features of two animals, and also introduced are several different ways of implementing clustering using trait features.

Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines. Computer-readable apparatus and computing systems are also disclosed.

Embodiments provide systems, methods, and non-transitory computer storage media for providing search result images based on associations of keywords and depth-levels of an image. In embodiments, depth-levels of an image are identified using depth-map information of the image to identify depth-segments of the image. The depth-segments are analyzed to determine keywords associated with each depth-segment based on objects, features, or content in each depth-segment. An image depth-level data structure is generated by matching keywords generated for the entire image with the keywords at each depth-level and assigning the depth-level to the keyword in the image depth-level data structure for the entire image. The image depth-level data structure may be queried for images that contain keywords and depth-level information that match the keywords and depth-level information specified in a search query.

A system and method is provided for using rules to perform a set of actions on video data when conditions are satisfied by the video data. The system receives rules to select a theme, portions of the video data and/or a type of output. For example, based on annotation data associated with the video data, the system may apply rules to select one or more themes, with each of theme associated with a portion of the video data. In some examples, the system may apply rules to determine the portion of the video data associated with the theme. The system may apply rules to generate various types of output data associated with each of the selected themes, the types of output data may include a video summarization, individual video clips, individual video frames, a photo album including video frames selected from the video data or the like.