A lane keeping assist system includes: a camera configured to provide an image around a vehicle as image information; a lane information generator configured to generate image reliability information and first lane information, based on the image information; an image storage configured to store the image information for each predetermined time among predetermined times; a neural network learning device configured to generate second lane information based on the image reliability information and the image information stored for each predetermined time; and a steering controller configured to select either one of the first lane information and the second lane information as lane information, based on the image reliability information, and generate steering information based on the selected lane information.

Disclosed is an image analysis method including inputting analysis data, including information regarding an analysis target cell to a deep learning algorithm having a neural network structure, and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.

An image processing method, an image processing device, a training method and a computer-readable storage medium. The image processing method includes: extracting a characteristic vector in an image to be recognized; based on the characteristic vector of the image to be recognized, acquiring a predicted score value of the image to be recognized; and based on the predicted score value, determining a category of an image information of the image to be recognized; wherein the image to be recognized is a face image, and the image information is a facial expression.

Systems and methods for efficient implementation of a convolutional layer of a convolutional neural network are disclosed. In one aspect, weight values of kernels in a kernel stack of a convolutional layer can be reordered into a tile layout with tiles of runnels. Pixel values of input activation maps of the convolutional layer can be reordered into an interleaved layout comprising a plurality of clusters of input activation map pixels. The output activation maps can be determined using the clusters of the input activation map pixels and kernels tile by tile.

Systems and methods for medical image diagnoses in accordance with embodiments of the invention are illustrated. One embodiment includes a method for evaluating multimedia content. The method includes steps for receiving multimedia content and identifying a set of one or more image frames for each of several target views from the received multimedia content. For each target view, the method includes steps for evaluating the corresponding set of image frames to generate an intermediate result. The method includes steps for determining a composite result based on the intermediate results for each of the several target views.

Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

An approach is provided for an unknown moving object detection system. The approach, for instance, involves capturing a plurality of unknown object events indicating an unknown object detected by one or more computer vision systems. The approach also involves clustering the plurality of unknown object events into a plurality of clusters based on one or more clustering parameters. The approach further involves selecting at least one cluster of the plurality of clusters based on a selection criterion. The approach further involves determining at least one operating scenario for the one or more computer vision systems based on a combination of the one or more clustering parameters associated with the selected at least one cluster.

A method is disclosed including: receiving raw image data corresponding to a series of raw images; processing the raw image data with an encoder to generate encoded data, where the encoder is characterized by an input/output transformation that substantially mimics the input/output transformation of one or more retinal cells of a vertebrate retina; and applying a first machine vision algorithm to data generated based at least in part on the encoded data.

An aerial robot includes an image sensor for capturing images of an environment. The robot receives a first image captured at a first location. The robot identifies one or more first pixels in the first image. The first pixels correspond to one or more targeted features of an object identified in the first image. The robot receives a second image captured at the second location. The robot receives its distance data that estimates a movement of the robot from the first location to the second location. The robot identifies second pixels in the second image. The second pixels corresponding to the targeted features of the object as appeared in the second image. The robot determines an estimated distance between the robot and the object based on the changes of locations of the second pixels from the first pixels relative to the movement of the robot provided by the distance data.

A visual feature processing method in an encoding device is disclosed. The visual feature processing method comprises: performing feature extraction from picture data to be encoded based on a predetermined feature extraction method to thereby obtain a set of extracted features; sorting the features in the set of extracted features based on a predetermined criterion; iteratively dividing the sorted set of extracted features in a plurality of subsets of features, said plurality of subsets of features comprising a first subset of features and at least one further subset of features, wherein the first subset of features is assigned a priority value which is higher than the priority value of the at least one further subset of features; and multiplexing the features of each subset of features for outputting for compressing, wherein the multiplexing is based on the priority value assigned to each subset of features.