A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangementssome involving designing classifiers so as to combat classifier copyingare also detailed.

A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

An object classification method includes: acquiring image data of an image including feature information indicating a feature of an object; and classifying the object included in the image, based on the feature information. The image data is acquired by causing a first image capture device to capture the image. The first image capture device includes: an image sensor; and a filter array that is arranged on an optical path of light that is incident on the image sensor and that includes translucent filters two-dimensionally arrayed along a plane that crosses the optical path, the translucent filters including two or more filters in which wavelength dependencies of light transmittances are different from each other, and light transmittance of each of the two or more filters having local maximum values in a plurality of wavelength ranges.

A system identifying anomalies in an image of an object is first trained using first sets of images corresponding to first anomaly types for the object. A model of the object is formed in a latent space. A label for each anomalous image is used to calculate vectors containing means and standard deviations for each first anomaly types. The means and standard deviations are used to calculate a log-likelihood loss for each first anomaly type. The system is retrained using second sets of images corresponding to second anomaly types for the object. The vectors are supplemented using labels for each second anomaly types. A statistically sufficient sample of information in the means and standard deviations vectors is supplied to the latent space. A log-likelihood loss for each of the first and second anomaly types is calculated based on their respective mean and standard deviation.

For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.

Systems and methods are provided for image enhancement using self-examples in combination with external examples. In one embodiment, an image manipulation application receives an input image patch of an input image. The image manipulation application determines a first weight for an enhancement operation using self-examples and a second weight for an enhancement operation using external examples. The image manipulation application generates a first interim output image patch by applying the enhancement operation using self-examples to the input image patch and a second interim output image patch by applying the enhancement operation using external examples to the input image patch. The image manipulation application generates an output image patch by combining the first and second interim output image patches as modified using the first and second weights.

A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method selects an entity from a plurality of entities identifying characteristics of a video item, where the video item has associated metadata. The computer-implemented method receives probabilities of existence of the entity in video frames of the video item, and selects a video frame determined to comprise the entity responsive to determining the video frame having a probability of existence of the entity greater than zero. The computer-implemented method determines a scaling factor for the probability of existence of the entity using the metadata of the video item, and determines an adjusted probability of existence of the entity by using the scaling factor to adjust the probability of existence of the entity. The computer-implemented method labels the video frame with the adjusted probability of existence.

A system and method enable similarity measures to be computed between pairs of images and between a color name and an image in a common feature space. Reference image representations are generated by embedding color name descriptors for each reference image in the common feature space. Color name representations for different color names are generated by embedding synthesized color name descriptors in the common feature space. For a query including a color name, a similarity is computed between its color name representation and one or more of the reference image representations. For a query which includes a query image, a similarity is computed between a representation of the query image and one or more of reference image representations. The method also enables combined queries which include both a query image and a color name to be performed. One or more retrieved reference images, or information based thereon, is then output.

A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangementssome involving designing classifiers so as to combat classifier copyingare also detailed.

Systems and methods for performing a medical analysis task using a trained machine learning based task network are provided. Input medical data is received. A medical analysis task is performed using a trained machine learning based task network based on the input medical data. Results of the medical analysis task are output. The trained machine learning based task network is trained by: receiving unannotated training medical data; generating weakly-supervised labels for the unannotated training medical data using one or more trained machine learning based supervised learning networks; training the machine learning based task network for performing the medical analysis task based on 1) the unannotated training medical data, 2) self-supervised labels for the unannotated training medical data learned via self-supervised learning, and 3) the generated weakly-supervised labels for the unannotated training medical data; and outputting the trained machine learning based task network.