The present disclosure relates to navigation and to systems and methods for using a dual sensor readout channel to allow for frequency detection. In one implementation, at least one processing device may receive a plurality of images acquired by a camera onboard a host vehicle, wherein the plurality of images are received via a first channel and via a second channel, and wherein the first channel is associated with a first frame capture rate, and the second channel is associated with a second frame capture rate different from the first frame capture rate. The processing device may use images received via the first channel to detect flickering and non-flickering light sources in an environment of the host vehicle; and provide, based on images received via the second channel, images for showing on one or more human-viewable displays.

Described are examples for detecting objects in an image on a device including setting, based on a condition, a number of sparse proposals to use in performing object detection in the image, performing object detection in the image based on providing the sparse proposals as input to an object detection process to infer object location and classification of one or more objects in the image, and indicating, to an application and based on an output of the object detection process, the object location and classification of the one or more objects.

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

A machine vision method includes obtaining a first representation of an image captured by an image sensor array, analyzing the first representation for an assessment of whether the first representation is sufficient to support execution of a machine vision task by the processor, if the first representation is not sufficient, determining, based on the first representation, a region of the image of interest for the execution of the machine vision task, reusing the image captured by the image sensor array to obtain a further representation of the image by directing the image sensor array to sample the image captured by the image sensor array in a manner guided by the determined region of the image of interest and by the assessment, and analyzing the further representation to assess whether the further representation is sufficient to support the execution of the machine vision task by implementing a procedure for the execution of the machine vision task in accordance with the further representation.

Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.

An exemplary system for generating an image(s) of a sample(s) can include, for example, an imaging arrangement that can include a superluminescent diode (SLD) configured to generate a radiation(s) to be provided to the sample(s), and a spectrometer configured to (i) sample an A-line sampling rate of at least about 200 kHz, (ii) receive a resultant radiation from the sample(s) based on the sampling rate, and (iii) generate information based on the resultant radiation, and a computer hardware arrangement configured to generate the image(s) of the sample(s) based on the information received from the spectrometer. The imaging arrangement can be an interferometric imaging arrangement, which can be an optical coherence tomography imaging (OCT) arrangement. The computer hardware arrangement can be further configured to facilitate a plurality of b-scan acquisitions of the sample(s) and facilitate the b-scan acquisitions in order to generate the image(s).

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

Aspects of the present disclosure are directed to, for example, a method for object detection, recognition, classification and tracking using a distributed networked architecture. In some embodiments, the distributed network architecture may include one or more sensor units wherein the image acquisition and the initial feature extraction are performed and a gateway processor for further data processing. Some aspects of the present disclosure are also directed to a vision system for object detection, and to algorithms implemented in the vision system for executing the method acts for object detection, recognition, classification and/or tracking.

An image processing method including: capturing changes in a monitored scene; and performing a sparse feature calculation on the changes in the monitored scene to obtain a sparse feature map.

Implementations of the present disclosure provide a solution for text line detection. In this solution, a first text region comprising a first portion of at least a first text element and a second text region comprising a second portion of at least a second text element are determined from an image. A first feature representation is extracted from the first text region and a second feature representation is extracted from the second text region. The first and second feature representations comprise at least one of an image eature representation or a semantic feature representation of the image. A link relationship between the first and second text regions can then be determined based at least in part on the first and second feature representations. The link relationship can indicate whether the first and second portions of the first and second text elements are located in a same text line. In this way, by detecting text regions and determining the link relationship thereof based on their feature representations, the accuracy and efficiency for detecting text lines in various images can be improved