Abstract: In a method of tracking an object, a plurality of images of a target object is obtained. A super-resolved image of the target object is calculated from the plurality of images. A further image of the target object is obtained. The further image is correlated with the super-resolved image, in order to identify the location of the target object in the further image.

Abstract: An image-processing method includes obtaining an image including a target object, the image being formed by an array of pixels. A current frame portion is extracted from the image, the frame portion being at least a portion of the pixels forming the image, corresponding to a region of interest in the image, the region of interest comprising the target object. A previously calculated current super-resolved frame portion is provided, corresponding to the region of interest in the image. An updated super-resolved frame portion is calculated from the current frame portion and the current super-resolved frame portion.

Abstract: In an image-processing method, a stack is provided for storing a predetermined number of frame portions. An image including a target object is obtained, the image being formed by an array of pixels. A frame portion is extracted from the image, the frame portion being at least a portion of the pixels forming the image, corresponding to a region of interest in the image, the region of interest comprising the target object. The frame portion is stored in the stack, the storing including discarding an oldest previously stored frame portion from the stack if the number of frame portions stored in the stack has reached the predetermined number. The steps of the method are repeated a plurality of times. Frame portions in the stack having a phase substantially equal to a given phase are averaged. A super-resolved image is calculated from the plurality of stored frame portions.

Abstract: In a method of tracking an object, a plurality of images of a target object is obtained. A super-resolved image of the target object is calculated from the plurality of images. A further image of the target object is obtained. The further image is correlated with the super-resolved image, in order to identify the location of the target object in the further image.

Abstract: In an image-processing method, a stack is provided for storing a predetermined number of frame portions. An image including a target object is obtained, the image being formed by an array of pixels. A frame portion is extracted from the image, the frame portion being at least a portion of the pixels forming the image, corresponding to a region of interest in the image, the region of interest comprising the target object. The frame portion is stored in the stack, the storing including discarding an oldest previously stored frame portion from the stack if the number of frame portions stored in the stack has reached the predetermined number. The steps of the method are repeated a plurality of times. Frame portions in the stack having a phase substantially equal to a given phase are averaged. A super-resolved image is calculated from the plurality of stored frame portions.

Abstract: An image-processing method includes obtaining an image including a target object, the image being formed by an array of pixels. A current frame portion is extracted from the image, the frame portion being at least a portion of the pixels forming the image, corresponding to a region of interest in the image, the region of interest comprising the target object. A previously calculated current super-resolved frame portion is provided, corresponding to the region of interest in the image. An updated super-resolved frame portion is calculated from the current frame portion and the current super-resolved frame portion.

Abstract: An image-processing method comprising convolving a selected feature of interest (FOI) within the image with a mask of a first size, repeating the convolution with a mask of a second size, and calculating the ratio of the convolution responses, as an indication of the size of the FOI. Preferably the convolution masks are Laplacian of Gaussian. The method can be useful for prioritizing potential targets in a field of view for presentation to an operator.

Abstract: A device having a plurality of functionalities includes an event detector for detecting a functionality setting event, a functionality setting device for referring to functionality data and setting a first functionality of the plurality of functionalities based on the functionality data, and a controller for controlling a feature associated with the first functionality to be operable in response to the setting of the first functionality.

Abstract: An image-processing method comprising convolving a selected feature of interest (FOI) within the image with a mask of a first size, repeating the convolution with a mask of a second size, and calculating the ratio of the convolution responses, as an indication of the size of the FOI. Preferably the convolution masks are Laplacian of Gaussian. The method can be useful for prioritising potential targets in a field of view for presentation to an operator.