Abstract: Abstract: A visual inspection system and method are provided in which a reference image of an item is processed and based on the processing a pre-tramed model is adjusted to obtain an adapted model. The adapted model, rather than the pre-tramed model, is then used to process an inspection image of a same-type item, to obtain inspection information which may then be output to a user.

Abstract: Embodiments of the invention provide a machine learning based detection system, in which defects can be detected even if the system was not trained for these defects and even in items that were not used to train the system, thereby offering an inherently flexible detection system.

Abstract: Systems, apparatuses and methods may provide for detecting a snapshot request to conduct a long range depth capture, wherein the snapshot request is associated with a short range depth capture. Additionally, an infrared (IR) projector may be activated at a first power level for a first duration in response to the snapshot request, wherein the first power level is greater than a second power level corresponding to the short range depth capture and the first duration is less than a second duration corresponding to the short range depth capture.

Abstract: Herein is disclosed a ride matching system comprising a memory configured to store a plurality of vehicle identifiers, each vehicle identifier being associated with a geographic information and a passenger factor of a current or planned passenger; one or more processors, configured to receive a user ride request comprising a user locational information and a user factor; and select in response to the user ride request a subset of vehicle identifiers based at least on a relationship between a geographic information and the user locational information and on a relationship between the passenger factor and the user factor.

Abstract: Systems, apparatuses and methods may provide for detecting a snapshot request to conduct a long range depth capture, wherein the snapshot request is associated with a short range depth capture. Additionally, an infrared (IR) projector may be activated at a first power level for a first duration in response to the snapshot request, wherein the first power level is greater than a second power level corresponding to the short range depth capture and the first duration is less than a second duration corresponding to the short range depth capture.

Abstract: Techniques are provided for image modification and enhancement based on recognition of objects in a scene image. An example system may include an image rendering circuit to render a number of image variations of an object based on a 3D model of the object. The 3D model may be generated by a computer aided design tool or a 3D scanning tool. The system may also include a classifier generation circuit to generate an object recognition classifier based on the rendered image variations. The system may further include an object recognition circuit to recognize the object from an image of a scene containing the object. The recognition is performed by the generated object recognition classifier. The system may still further include an image modification circuit to create a mask to segment the recognized object from the image of the scene and modify the masked segment of the image of the scene.

Abstract: A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.

Abstract: A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.

Abstract: There is provided a system for tracking a region of interest in a video includes an identifier for identifying the region of interest and determining a location of the region of interest in a first frame of a video sequence, and a tracker for locating the region of interest in at least a second frame, based on a location of the region of interest in the first frame. The system also includes a recovery manager for determining whether the tracker has correctly located the region of interest. There is also provided a method for tracking a region of interest in a video.