Systems and methods are provided for image classification using histograms of oriented gradients (HoG) in conjunction with a trainer. The efficiency of the process is greatly increased by first establishing a bitmap which identifies a subset of the pixels in the HoG window as including relevant foreground information, and limiting the HoG calculation and comparison process to only the pixels included in the bitmap.

MMTTTTA method and system for face alignment. The method may include obtaining an image processing model set including M (M≥2) candidate models, and obtaining a test image including a target face. The method may also include conducting T (T≥1) stages of model set updating operation. Each stage of the T stages of model set updating operation may include conducting a performance evaluation to each candidate model of the image processing model set with respect to the test image, and updating the image processing model set by excluding at least one model from the image processing model set based on the performance evaluation. The method may further include designating, after completing the T stages of model set updating operation, at least one candidate model of the image processing model set as a target model, and determining, based on the target model, a result shape as a shape of the target face.

The present disclosure relates to a method and system for determining Total Count (TC) of RBCs in a Peripheral Blood Smear (PBS). The system receives a plurality of images from the monolayer of the PBS. Further, the system extracts, segments and identifies RBCs in each of the plurality of images using deep learning models. The system computes a value of each variable of a set of variables for each of the plurality of images. The set of variables includes foreground non-pallor area, density of RBCs, cell count, cell count ratio, foreground area and foreground hole-filled area. Furthermore, the system computes statistical parameters for each variable, over the plurality of images. The statistical parameters are provided as an input to supervised learning model, to determine TC of RBCs. Thus, the TC estimation system provides an efficient and robust method for estimating TC of RBCs using plurality of images of the PBS.

A method and system for analyzing and connecting computer-based actions into sentences may include for a series of computer-based actions, determining the case ID for the action for each action where an identifier or case ID can be determined, creating sequences of subsets of the series of computer-based actions using the case ID, and merging sequences having computer-based actions having the same case ID. A set of case IDs may be extracted from the actions using a clustering algorithm based on features of potential case IDs such as gaps in appearance of potential case IDs in a sequence of actions and consecutive appearances of potential case IDs in a sequence of actions. The extracted case IDs may be used when creating sequences.

The specification describes methods and systems for increasing a dimensional depth of a two-dimensional image of a face to yield a face image that appears three dimensional. The methods and systems identify key points on the 2-D image, obtain a texture map for the 2-D image, determines one or more proportions within the 2-D image, and adjusts the texture map of the 3-D model based on the determined one or more proportions within the 2-D image.

Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation.

MMTTTTA method and system for face alignment. The method may include obtaining an image processing model set including M (M≥2) candidate models, and obtaining a test image including a target face. The method may also include conducting T (T≥1) stages of model set updating operation. Each stage of the T stages of model set updating operation may include conducting a performance evaluation to each candidate model of the image processing model set with respect to the test image, and updating the image processing model set by excluding at least one model from the image processing model set based on the performance evaluation. The method may further include designating, after completing the T stages of model set updating operation, at least one candidate model of the image processing model set as a target model, and determining, based on the target model, a result shape as a shape of the target face.

An object detection and classification verification system for a vehicle includes a projection system configured to project a three-dimensional (3D) scene pre-captured at a known distance and comprising at least one known object onto a surface in front of the vehicle a controller configured to verify a performance of an object detection and classification routine by performing the object detection and classification routine on the projected 3D scene to generate a set of results, comparing the set of results to a set of expected results associated with the projected 3D scene, and based on the comparing, determining whether the performance of the object detection and classification routine satisfies a predetermined threshold metric.

A method and system for analyzing and connecting computer-based actions into sentences may include for a series of computer-based actions, determining the case ID for the action for each action where an identifier or case ID can be determined, creating sequences of subsets of the series of computer-based actions using the case ID, and merging sequences having computer-based actions having the same case ID. A set of case IDs may be extracted from the actions using a clustering algorithm based on features of potential case IDs such as gaps in appearance of potential case IDs in a sequence of actions and consecutive appearances of potential case IDs in a sequence of actions. The extracted case IDs may be used when creating sequences.

A method and system for face alignment. The method may include obtaining an image processing model set including M (M≥2) candidate models, and obtaining a test image including a target face. The method may also include conducting T (T≥1) stages of model set updating operation. Each stage of the T stages of model set updating operation may include conducting a performance evaluation to each candidate model of the image processing model set with respect to the test image, and updating the image processing model set by excluding at least one model from the image processing model set based on the performance evaluation. The method may further include designating, after completing the T stages of model set updating operation, at least one candidate model of the image processing model set as a target model, and determining, based on the target model, a result shape as a shape of the target face.