Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle may comprise at least one processor. The processor may be programmed to receive from a first camera at least a first captured image representative of an environment of the host vehicle. The processor may be programmed to receive from a second camera at least a second captured image representative of the environment of the host vehicle. Both the first captured image and the second image includes a representation of the traffic light, and wherein the second camera is configured to operate in a primary mode where at least one operational parameter of the second camera is tuned to detect at least one feature of the traffic light. The processor may be further programmed cause at least one navigational action by the vehicle based on analysis of the representation of the traffic light.

Provided is an image sensor evaluation method using a computing device including a processor, the method including receiving, by the processor, image data obtained by capturing a plurality of neighboring lines by an image sensor, performing, by the processor, a spatial domain analysis on the image data to generate a first quality score of the image sensor, performing, by the processor, a frequency domain analysis on the image data to generate a second quality score of the image sensor, and generating, by the processor, a final quality score of the image sensor based on the first quality score and the second quality score.

A modulator of a distance measuring sensor unit 102 has a photographing pattern that includes a plurality of patterns different from each other, and modulates intensity of light. A gesture detecting unit divides image data obtained by receiving light transmitted through the modulator by the image sensor; restores an image by calculation based on a developing pattern corresponding to the photographing pattern in a divided unit; and obtains an image for detecting gesture of a user. This makes it possible to obtain an image of an object in an outside world without using a lens for forming an image. Therefore, it is possible to reduce a load on a user when the user wears the apparatus.

A modulator of a distance measuring sensor unit 102 has a photographing pattern that includes a plurality of patterns different from each other, and modulates intensity of light. A gesture detecting unit divides image data obtained by receiving light transmitted through the modulator by the image sensor; restores an image by calculation based on a developing pattern corresponding to the photographing pattern in a divided unit; and obtains an image for detecting gesture of a user. This makes it possible to obtain an image of an object in an outside world without using a lens for forming an image. Therefore, it is possible to reduce a load on a user when the user wears the apparatus.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, at a neural network, frequency filtered spatial domain data, compare an output generated by the neural network to a loss function including a frequency-based feature consistency constraint, and update at least one weight of the neural network according to the loss function.

The inventive method involves receiving as input a representation of an ordered set of two dimensional images. The ordered set of two dimensional images is analyzed to determine at least one first view of an object in at least two dimensions and at least one motion vector. The next step is analyzing the combination of the first view of the object in at least two dimensions, the motion vector, and the ordered set of two dimensional images to determine at least a second view of the object; generating a three dimensional representation of the ordered set of two dimensional images on the basis of at least the first view of the object and the second view of the object. Finally, the method involves providing indicia of the three dimensional representation as an output.

The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.

A device comprising a circuitry configured to obtain a sequence of digital images from an image sensor; select a region of interest within a digital image of the sequence of digital images; perform motion compensation on the region of interest to obtain a motion compensated region of interest based on motion information obtained from the sequence of digital images and a predefined accumulated time interval; define a mask pattern based on the compensated region of interest; apply the mask pattern to an electronic light valve.

A device comprising a circuitry configured to obtain a sequence of digital images from an image sensor; select a region of interest within a digital image of the sequence of digital images; perform motion compensation on the region of interest to obtain a motion compensated region of interest based on motion information obtained from the sequence of digital images and a predefined accumulated time interval; define a mask pattern based on the compensated region of interest; apply the mask pattern to an electronic light valve.

A vision transformer includes L layers, and H attention heads in each layer. An h′ of the attention heads include an attention mask added before a Softmax operation, and an h of the attention heads include unmasked attention heads in which H=h′+h. Each attention mask multiplies a Query vector and a Key vector for form element-wise products. At least one attention mask is a hard mask that selects closest neighbors of a patch and ignores patches further away than the closest neighbors of the patch. Alternatively, at least one attention mask includes a soft mask that multiplies weights of closest neighbors of a patch by a magnification factor and passes weights of patches that are further away than the closest neighbors of the patch. A learnable bias α may be added to diagonal elements of the at least one attention map.