A method displays information graphically on an image captured by a vehicular optical system. The method includes capturing the image and identifying an object in the image. The method the assigns a priority level to the object based on a predetermined criterion. The object is altered based on its priority level to create an altered object. An altered object may have a changed color, a colored halo surrounding it, or a colored object inside it. Other possible ways to alter the way the object looks are possible. The altered object is displayed in the image in place of the object on a mobile device to alert a person of its presence and the priority level associated therewith.

An autonomous vehicle vision system for estimating a category of a detected object in an object pose unknown to the system includes a neural network to apply a mapping process to a region of interest in an image including the detected object in the object pose to obtain a point in a 3D manifold space. The system includes an object detector to estimate the category of the detected object in the object pose in the region of interest based on a relationship between the point representing the detected object in the object pose and a plurality of separate object clusters in the 3D manifold space. The system further includes a planner to select an improved route based on a predicted behavior of the category of the detected object in the object pose. The system also includes a controller to control operation of an autonomous vehicle according to the improved route.

Various embodiments of a facial recognition system are provided. In one embodiment, a processor determines a value for a lighting parameter associated with a captured facial image, determines whether any previously obtained images in a biometric database includes a similar value for the lighting parameter and, if not, stores the newly captured image in the database along with the lighting parameter value. In another embodiment, the processor calculates a score indicative of the likelihood that the face in the captured facial image is identical to the face of a previously obtained image in the database, determines whether the score exceeds a threshold value and, if so, generates a signal indicating a match. The processor adjusts the threshold based on one or more parameter values.

In various embodiments, a training application generates a trained encoder that automatically generates shape embeddings having a first size and representing three-dimensional (3D) geometry shapes. First, the training application generates a different view activation for each of multiple views associated with a first 3D geometry based on a first convolutional neural network (CNN) block. The training application then aggregates the view activations to generate a tiled activation. Subsequently, the training application generates a first shape embedding having the first size based on the tiled activation and a second CNN block. The training application then generates multiple re-constructed views based on the first shape embedding. The training application performs training operation(s) on at least one of the first CNN block and the second CNN block based on the views and the re-constructed views to generate the trained encoder.

A method displays information graphically on an image captured by an optical system used to simulate a rear-view mirror system of a vehicle. The method includes capturing the image and identifying an object in the image. The method the assigns a priority level to the object based on a predetermined criterion. The object is altered based on its priority level to create an altered object. An altered object may have a changed color, a colored halo surrounding it, or a colored object inside it. Other possible ways to alter the way the object looks are possible. The altered object is displayed in the image in place of the object to alert a vehicle operator of its presence and the priority level associated therewith.

This application relates to the field of artificial intelligence technologies, and in particular, to a training method of an image-text matching model, a bi-directional search method, and a relevant apparatus. The training method includes extracting a global feature and a local feature of an image sample; extracting a global feature and a local feature of a text sample; training a matching model according to the extracted global feature and local feature of the image sample and the extracted global feature and local feature of the text sample, to determine model parameters of the matching model; and determining, by the matching model, according to a global feature and a local feature of an inputted image and a global feature and a local feature of an inputted text, a matching degree between the image and the text.

Disclosed are methods, systems, and non-transitory computer-readable medium for color and pattern analysis of images including wearable items. For example, a method may include receiving an image depicting a wearable item, identifying the wearable item within the image by identifying a face of an individual wearing the wearable item or segmenting a foreground silhouette of the wearable item from background image portions of the image, determining a portion of the wearable item identified within the image as being a patch portion representative of the wearable item depicted within the image, deriving one or more patterns of the wearable item based on image analysis of the determined patch portion of the image, deriving one or more colors of the wearable item based on image analysis of the determined patch portion of the image, and transmitting information regarding the derived one or more colors and information regarding the derived one or more patterns.

A method for processing digital image recognition of invariant representations of hierarchically structured entities can be performed by a computer using an artificial neural network. The method involves learning a sparse coding dictionary on an input signal to obtain a representation of low-complexity components. Possible transformations are inferred from the statistics of the sparse representation by computing a correlation matrix. Eigenvectors of the Laplacian operator on the graph whose adjacency matrix is the correlation matrix from the previous step are computed. A coordinate transformation is performed to the base of eigenvectors of the Laplacian operator, and the first step is repeated with the next higher hierarchy level until all hierarchy levels of the invariant representations of the hierarchically structured entities are processed and the neural network is trained. The trained artificial neural network can then be used for digital image recognition of hierarchically structured entities.

An illustrative method includes accessing, by a computing device, a model simulating light scattered by a simulated target, the model comprising a plurality of parameters. The method further includes generating, by the computing device, a set of possible histogram data using the model with a plurality of values for the parameters. The method further includes determining, by the computing device, a set of components that represent the set of possible histogram data, the set of components having a reduced dimensionality from the set of possible histogram data.

A hand-raising detection device includes a converter and a detection unit. The converter performs conversion of a predetermined space including a person into an overhead view image by using a result of three dimensional measurement performed on the predetermined space. The detection unit performs detection of a hand-raising action by using a silhouette image of the person in the overhead view image resulting from the conversion performed by the converter.