Abstract: A method and an image processing entity for applying a convolutional neural network to an image are disclosed. The image processing entity processes the image while using the convolutional kernel to render a feature map, whereby a second feature map size of the feature map is greater than a first feature map size of the feature maps with which the feature kernel was trained. Furthermore, the image processing entity repeatedly applies the feature kernel to the feature map in a stepwise manner, wherein the feature kernel was trained to identify the feature based on the feature maps of the first feature maps, wherein the feature kernel has the first feature map size.

Abstract: Methods and apparatus, including computer program products, for creating a quality annotated training data set of images for training a quality estimating neural network. A set of images depicting a same object is received. The images in the set of images have varying image quality. A probe image whose quality is to be estimated is selected from the set of images. A gallery of images is selected from the set of images. The gallery of images does not include the probe image. The probe image is compared to each image in the gallery and a match score is generated for each image comparison. Based on the match scores, a quality value is determined for the probe image. The probe image and its associated quality value are added to a quality annotated training data set for the neural network.

Abstract: Described is a method and apparatus for controlling a degree of compression of a digital image, and more specifically to such method and apparatus which receives a stereoscopic digital image, and controls a degree of compression of the digital image based on block matching characteristics between the two images of the stereoscopic digital image.

Abstract: A method and a controller for controlling a video processing unit to facilitate detection of newcomers in a first environment. The method comprises: capturing a thermal image of a human object in the first environment, the first environment being associated with a first climate; calculating, based on the thermal image, a thermal signature of a portion of the human object; determining that the human object has entered the first environment from a second environment when the thermal signature of the portion of the human object deviates from a predetermined thermal signature associated with the first environment, wherein the second environment is associated with a second, different, climate; and controlling the video processing unit to prioritize the human object over other human objects when processing video frames depicting the human object together with the other human objects.

Abstract: A method for finding one or more candidate digital images being likely candidates for depicting a specific object comprising: receiving an object digital image depicting the specific object; determining, using a classification subnet of a convolutional neural network, a class for the specific object depicted in the object digital image; selecting, based on the determined class for the specific object depicted in the object digital image, a feature vector generating subnet from a plurality of feature vector generating subnets; determining, by the selected feature vector generating subnet, a feature vector of the specific object depicted in the object digital image; locating one or more candidate digital images being likely candidates for depicting the specific object depicted in the object digital image by comparing the determined feature vector and feature vectors registered in a database, wherein each registered feature vector is associated with a digital image.

Abstract: A method for generating an alert signal in a surveillance system comprising: detecting a targeted individual in a video stream, selecting the targeted individual, and tracking the targeted individual, as first steps. The method also comprises classifying actions of the detected individual over a plurality of image frames in the video stream in response to the identification of the detected object as being a targeted person, and generating an alert signal if the classified action of the object is classified as a predefined alert-generating action.

Abstract: A method, device and computer program product for training neural networks being adapted to process image data and output a vector of values forming a feature vector for the processed image data. The training is performed using feature vectors from a reference neural network as ground truth. A system of devices for tracking an object using feature vectors outputted by neural networks running on the devices.

Abstract: A method and system are disclosed. The method may include receiving instructions in a hardware accelerator coupled to a computing device. The instructions may describe operations and data dependencies between the operations. The operations and the data dependencies may be predetermined. The method may include performing a splitter operation in the hardware accelerator, performing an operation in each of a plurality of branches, and performing a combiner operation in the hardware accelerator.

Abstract: A method and system for action recognition in a video sequence is disclosed. The system comprises a camera configured to capture the video sequence and a server configured to perform action recognition. The camera comprises an object identifier that identifies an object of interest in an object image frame of the video sequence; an action candidate recognizer configured to apply a first action recognition algorithm to the object image frame to detect presence of an action candidate; an video extractor configured to produce action image frames of an action video sequence by extracting video data pertaining to a plurality of image frames from the video sequence; and a network interface configured to transfer the action video sequence to the server. The server comprises an action verifier configured to apply a second action recognition algorithm to the action video sequence to verify or reject that the action candidate is an action.

Abstract: A method and system for tracking objects in a defined area compares image data of a detected object to profiles of persons that have entered the defined area to find the best match and connect the profile of the best match to the detected object. Identification profiles of persons that have been identified, by presenting their credentials, when entering the defined area are registered as candidates and are later matched with objects detected in the defined area. The system and method use the physical access control system of the defined area to reduce the number of candidates for the detected objects to the most likely candidates. The processing time and need for resources of the object tracking in the defined area are thereby reduced.

Abstract: A method, computer program, computer and system for training a neural network that receives a plurality of input digital images and, for each specific input digital image, outputs data for determining a relevance level of groups of pixels in the specific input digital image.

Abstract: A method may include determining a value indicative of an average intensity of blocks in an image. The blocks include a primary and outer blocks. Each of the outer blocks may have three, five, or more than five pixels. The image may describe an external pixel lying between the primary and at least one of the outer blocks. The external pixel may not contribute to the value indicative of the average intensity of any of the blocks. The image may also describe a common internal pixel lying within two of the blocks. The common pixel may contribute to the value indicative of the average intensity of the two of the blocks. The method may include comparing the value indicative of the average intensity of the primary block to the values of the outer blocks, and quantifying a feature represented by the image by generating a characteristic number.

Abstract: A method may include determining a value indicative of an average intensity of blocks in an image. The blocks include a primary and outer blocks. Each of the outer blocks may have three, five, or more than five pixels. The image may describe an external pixel lying between the primary and at least one of the outer blocks. The external pixel may not contribute to the value indicative of the average intensity of any of the blocks. The image may also describe a common internal pixel lying within two of the blocks. The common pixel may contribute to the value indicative of the average intensity of the two of the blocks. The method may include comparing the value indicative of the average intensity of the primary block to the values of the outer blocks, and quantifying a feature represented by the image by generating a characteristic number.

Abstract: Described is a method and apparatus for controlling a degree of compression of a digital image, and more specifically to such method and apparatus which receives a stereoscopic digital image, and controls a degree of compression of the digital image based on block matching characteristics between the two images of the stereoscopic digital image.

Abstract: A method and system are disclosed. The method may include receiving instructions in a hardware accelerator coupled to a computing device. The instructions may describe operations and data dependencies between the operations. The operations and the data dependencies may be predetermined. The method may include performing a splitter operation in the hardware accelerator, performing an operation in each of a plurality of branches, and performing a combiner operation in the hardware accelerator.

Abstract: Methods and apparatus for providing automatic gain control (AGC) for received multi-carrier signals are disclosed. A receiver circuit comprises a common analog signal path, which includes an analog variable-gain circuit and an analog-to-digital converter, and further comprises first and second carrier-specific, digital variable-gain circuits corresponding to first and second carriers of the received multi-carrier signal, respectively. The receiver circuit further includes a gain control circuit configured to control the analog and digital variable-gain circuits and to allot gain adjustments to the analog variable-gain circuit based on a difference between carrier signal levels for the first and second carriers.

Abstract: Methods and apparatus for providing automatic gain control (AGC) for received multi-carrier signals are disclosed. A receiver circuit comprises a common analog signal path, which includes an analog variable-gain circuit and an analog-to-digital converter, and further comprises first and second carrier-specific, digital variable-gain circuits corresponding to first and second carriers of the received multi-carrier signal, respectively. The receiver circuit further includes a gain control circuit configured to control the analog and digital variable-gain circuits and to allot gain adjustments to the analog variable-gain circuit based on a difference between carrier signal levels for the first and second carriers.