ADAS includes a processing circuit and a memory which stores instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to receive, from a vehicle that is in motion, a video sequence, generate a position image including at least one object included in the stereo image, generate a second position information associated with the at least one object based on reflected signals received from the vehicle, determine regions each including at least a portion of the at least one object as candidate bounding boxes based on the stereo image and the position image, and selectively adjusting class scores of respective ones of the candidate bounding boxes associated with the at least one object based on whether a respective first position information of the respective ones of the candidate bounding boxes matches the second position information.

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.

The present disclosure provides a method and apparatus for detecting an abnormal event from a monitoring image accurately and speedily in a video surveillance system. A method of detecting an abnormal event in a series of temporally successive images includes: generating a predicted current frame based on a previous frame temporally ahead of an actual current frame and a subsequent frame temporally behind the actual current frame; calculating an anomaly score indicating a difference between the predicted current frame and the actual current frame; and determining that an abnormality is included in the actual current frame when the anomaly score satisfies a predetermined condition.

Disclosed herein is a method for encoding a feature map. The method may include arranging multiple channels based on similarity therebetween for a feature map having the multiple channels, rearranging the arranged multiple channels so as to be adjacent to each other in a feature map channel having a matrix form, and generating an encoded feature map by converting a feature value corresponding to the feature map channel from a real number to an integer.

A facial recognition method using online sparse learning includes initializing target position and scale, extracting positive and negative samples, and extracting high-dimensional Haar-like features. A sparse coding function can be used to determine sparse Haar-like features and form a sparse feature matrix, and the sparse feature matrix in turn is used to classify targets.

A solution is proposed for assisting a medical procedure. A corresponding method comprises acquiring a luminescence image (205F), based on a luminescence light, and an auxiliary image (205R), based on an auxiliary light different from this luminescence light, of a field of view (103); the field of view (103) contains a region of interest comprising a target body of the medical procedure (containing a luminescence substance) and one or more foreign objects. An auxiliary informative region (210Ri) representative of the region of interest without the foreign objects is identified in the auxiliary image (205R) according to its content, and a luminescence informative region (210Fi) is identified in the luminescence image (205F) according to the auxiliary informative region (210Ri). The luminescence image (205F) is processed limited to the luminescence informative region (210Fi) for facilitating an identification of a representation of the target body therein. A computer program and a corresponding computer program product for implementing the method are also proposed. Moreover, a computing device for performing the method and an imaging system comprising it are proposed. A medical procedure based on the same solution is further proposed.

Methods, systems, electronic devices, and computer-readable storage media for image normalization processing are provided. In one aspect, an image normalization processing method includes: normalizing a feature map by respectively using K normalization factors to obtain K candidate normalized feature maps; for each of the K normalization factors, determining a first weight value for the normalization factor; and determining a target normalized feature map corresponding to the feature map based on the candidate normalized feature map corresponding to each of the K normalization factors and the first weight value for each of the K normalization factors. The K candidate normalized feature maps and the K normalization factors have a one-to-one correspondence, and K is an integer greater than 1.

A learning device makes an object detection device learn how to detect an object from an input image. A feature extraction unit performs feature extraction from input images including real images and pseudo images to generate feature maps, and the object detection unit detects objects included in the input images based on the feature maps. The domain identification unit identifies the domains forming the input images and generates domain identifiability information. Then, the feature extraction unit and the object detection unit learn common features that do not depend on the difference in domains, based on the domain identifiability information.

An information processing system (1) includes an operation amount data acquisition unit (109), an image data acquisition unit (101), a difference expression extraction unit (108), and a mirror surface region identification unit (111). The operation amount data acquisition unit (109) acquires operation amount data of an object. The image data acquisition unit (101) acquires image data captured by an imaging device mounted on the object. The difference expression extraction unit (108) extracts a difference expression representing feature information about a difference between two images based on two pieces of image data captured before and after the operation of the object. The mirror surface region identification unit (111) identifies a mirror surface region based on a correlation between the difference expression and the operation amount data.

The present disclosure provides a multi-task deep Hash learning-based retrieval method for massive logistics product images. According to the idea of multi-tasking, Hash codes of a plurality of lengths can be learned simultaneously as high-level image representation. Compared with single-tasking in the prior art, the method overcomes shortcomings such as waste of hardware resources and high time cost caused by model retraining under single-tasking. Compared with the traditional idea of learning a single Hash code as an image representation and using it for retrieval, information association among Hash codes of a plurality of lengths is mined, and the mutual information loss is designed to improve the representational capacity of the Hash codes, which addresses the poor representational capacity of a single Hash code, and thus improves the retrieval performance of Hash codes.