Abstract: A system includes at least one processor configured to detect, based on point cloud information, portions of a particular object, and determine, based on the detected portions, at least a first portion having a first reflectivity corresponding to a license plate, and at least two additional spaced-apart portions corresponding to locations on the particular object other than a location of the first portion. The at least two additional portions have reflectivity substantially lower than the first reflectivity. The at least one processor is further configured to classify the particular object as a vehicle, based on a spatial relationship and a reflectivity relationship between the first portion and the at least two additional portions.

Abstract: There is provided a method of matching features depicted in images, comprising: detecting a first object depicted in a first image, projecting a first epipolar line, from the first object of the first image, to a second image, selecting second objects along the first epipolar line of the second image, projecting second epipolar lines, from the second objects of the second image, to a third image, projecting a third epipolar line from the first object of the first image to the third image, identifying on the third image, a third object along an intersection of the third epipolar line and a certain second epipolar line of the second epipolar lines, and generating an indication of the first object depicted in first image and the third object depicted in the third image as matches of a same physical object.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings. The system includes: at least one memory configured to store classification information for classifying a plurality of objects; and at least one processor configured to receive a plurality of detection results associated with light detection and ranging system (LIDAR) detection results, each detection result including location information, and further information indicative of at least two of the following detection characteristics: object surface reflectivity; temporal spreading of signal reflected from the object; object surface physical composition; ambient illumination measured at a LIDAR dead time; difference in detection information from a previous frame; and confidence level associated with another detection characteristic.

Abstract: The subject matter discloses a method of asset change detection using images, the method comprising steps executed by processing circuitry, the steps comprising: receiving at least one image of an asset captured by an image capturing device; receiving at least one attribute of a task of detecting a change in the asset using the received at least one image, at least one of the at least one attribute being one of the group consisting of: an attribute measured by a sensor, an attribute extracted from a website, an attribute retrieved from a database, an attribute input by a user, and an attribute encoded in computer code; selecting a reference image among a plurality of reference images of the asset according to at least one criterion based on the received at least one attribute of the task of detecting the change in the asset; computing an asset-difference pixel map, using the selected reference image and the image captured by the image capturing device; and detecting the change in the asset, using the computed

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Abstract: A method including receiving at least one image stream depicting a scene, processing the image stream to detect multiple parameters associated with at least (i) one or more persons in the scene, (ii) one or more objects in the scene, and (iii) one or more environmental conditions in the scene, where each of the parameters has an associated score, calculating an alert value with respect to the scene based on a weighted sum of the scores, and issuing an alert when the alert value is above a predefined threshold.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Abstract: A system for hand gesture recognition includes a display, camera, memory, and processor. When the processor is to execute instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor may also estimate an average motion vector of the object, determine a first histogram of optical flow (HOOF) based on the one or more motion vectors and the average motion vector, determine depth values based on motion vectors from the first HOOF, and determine a second histogram of optical flow (HOOF) based on the depth values. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors, a shape descriptor, and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: A system for hand gesture recognition is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors and the average motion vector and classify the descriptor as a gesture.

Abstract: An example apparatus for classifying target objects using three-dimensional geometric filtering includes a patch receiver to receive patches with objects to be classified. The apparatus also includes a geometric filter to filter out patches including objects with sizes outside a target range using three dimensional geometry to generate filtered patches. The apparatus further includes a background remover to remove background pixels from the filtered patches to generate preprocessed patches. The apparatus includes a classification score calculator to calculate a classification score for each of the preprocessed patches.

Abstract: An example apparatus for classifying target objects using three-dimensional geometric filtering includes a patch receiver to receive patches with objects to be classified. The apparatus also includes a geometric filter to filter out patches including objects with sizes outside a target range using three dimensional geometry to generate filtered patches. The apparatus further includes a background remover to remove background pixels from the filtered patches to generate preprocessed patches. The apparatus includes a classification score calculator to calculate a classification score for each of the preprocessed patches.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: A system for hand gesture recognition includes a display, camera, memory, and processor. When the processor is to execute instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor may also estimate an average motion vector of the object, determine a first histogram of optical flow (HOOF) based on the one or more motion vectors and the average motion vector, determine depth values based on motion vectors from the first HOOF, and determine a second histogram of optical flow (HOOF) based on the depth values. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors, a shape descriptor, and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: A system for hand gesture recognition is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: A method, a system and a computer program product for evaluating an actual structural element of an electrical circuit. The method includes: detecting an actual structural element contour by processing a scanning electron microscope image of the actual structural element; aligning the actual structural element contour with a simulated contour to provide an aligned actual structural element contour; wherein the simulated contour is obtained by simulating a lithographic process that is responsive to a design contour; and comparing between the aligned actual structural element contour and reference information.

Abstract: A system and method for computer vision based tracking of a hand may include receiving a sequence of images, the images including at least one object having a shape of a hand. A first selected feature is tracked from within the hand shaped object. Shape recognition algorithms are applied at a suspected location of the hand shaped object in an image from the sequence of images to detect a shape of a hand in the image and a second feature from within the detected shape of the hand is then selected and tracked, thereby providing verification and updating of the location of the hand shaped object.