Abstract: A method for controlling a subsidence area caused by underground mining in an adjoining open-pit mine, applied in an open-pit and underground coordinated mining process. In the method, a ground subsidence area caused by underground mining and production is directly filled and covered with overburden materials such as soil and rock discharged from an adjoining open-pit mine; small and medium fracture zones and large fracture zones caused by mining are timely backfilled, tamped, and levelled according to areas before the ground subsidence area appears, the thickness of the levelled soil layer is kept above 1 m, and the area slope is controlled within 7°.

Abstract: An image processing apparatus and method for detecting and tracking multiple moving objects in successive frames includes circuitry configured to detect a plurality of moving regions within a plurality of frames based on an optical flow map of a plurality of pixels within the plurality of moving regions. A plurality of merged objects are identified from the detected plurality of moving regions. A plurality of split objects are identified from the detected plurality of moving regions and the identified plurality of merged objects. A location of each of the detected plurality of moving regions, the identified plurality of merged objects and the identified plurality of split objects is predicted and tracked within the captured plurality of frames.

Abstract: An apparatus and method for detection of moving objects in a sequence of frames, includes circuitry, a memory, and an image-capture device. The circuitry derives a first optical flow map based on motion information of a first frame and a second frame of a plurality of frames. A second optical flow map is further derived, based on motion information of the second frame and a third frame of the plurality of frames. A first foreground region is further identified that corresponds to the moving objects across the first frame and the second frame and the identified first foreground region is warped across the first frame and the second frame and the moving objects are detected, based on combination of a plurality of pixels in the warped first foreground region and the second foreground region.

Abstract: A method for controlling a subsidence area caused by underground mining in an adjoining open-pit mine, applied in an open-pit and underground coordinated mining process. In the method, a ground subsidence area caused by underground mining and production is directly filled and covered with overburden materials such as soil and rock discharged from an adjoining open-pit mine; small and medium fracture zones and large fracture zones caused by mining are timely backfilled, tamped, and levelled according to areas before the ground subsidence area appears, the thickness of the levelled soil layer is kept above 1 m, and the area slope is controlled within 7°.

Abstract: An image processing apparatus and method for detecting and tracking multiple moving objects in successive frames includes circuitry configured to detect a plurality of moving regions within a plurality of frames based on an optical flow map of a plurality of pixels within the plurality of moving regions. A plurality of merged objects are identified from the detected plurality of moving regions. A plurality of split objects are identified from the detected plurality of moving regions and the identified plurality of merged objects. A location of each of the detected plurality of moving regions, the identified plurality of merged objects and the identified plurality of split objects is predicted and tracked within the captured plurality of frames.

Abstract: An apparatus and method for detection of moving objects in a sequence of frames, includes circuitry, a memory, and an image-capture device. The circuitry derives a first optical flow map based on motion information of a first frame and a second frame of a plurality of frames. A second optical flow map is further derived, based on motion information of the second frame and a third frame of the plurality of frames. A first foreground region is further identified that corresponds to the moving objects across the first frame and the second frame and the identified first foreground region is warped across the first frame and the second frame and the moving objects are detected, based on combination of a plurality of pixels in the warped first foreground region and the second foreground region.

Abstract: A method and a device, such as an imaging device, for image processing for automatic detection of focus area are disclosed herein. In accordance with an embodiment, the device for image processing is the imaging device that is configured to extract a plurality of object features of a plurality of objects in a field-of-view (FOV) of the imaging device. A plurality of confidence maps are generated based on the extracted plurality of object features. The imaging device is further configured to determine a focus area corresponding to the FOV, based on the generated plurality of confidence maps by use of a pre-determined rule.

Abstract: Various aspects of a system and method for video processing is disclosed herein. The system comprises a video processing device that is configured to generate a spatial saliency map based on spatial information associated with a current frame of a video stream. A spatio-temporal saliency map is generated based on at least motion information associated with the current frame and a previous frame of the video stream. Based on a weighted combination of the generated spatial saliency map and the generated spatio-temporal saliency map, a combined saliency map is generated.

Abstract: Various aspects of a device and method for detection of regions based on sensor data for autofocus are disclosed herein. In accordance with an embodiment, the method includes receipt of a depth of filed (DOF) information of a scene viewed using an image-capturing device of the electronic device. The one or more images of the scene are segmented into a plurality of candidate segmented regions based on the received DOF information. One of the plurality of candidate segmented regions is determined as a region-of-interest to autofocus on the determined candidate segmented region.

Abstract: Various aspects of a system and a method for background subtraction in video content are disclosed herein. The system includes an electronic device, which generates an optical flow image of an image frame from a sequence of image frames. A first distribution is determined based on pixel values of a plurality of pixels of the generated optical flow image. A second distribution is determined based on pixel values of a set of pixels of the plurality of pixels of the generated optical flow image. The set of pixels correspond to pixels that touch one or more borders of the image frame. A third distribution is further determined based on the determined first distribution and the determined second distribution. One or more peak values corresponding to one or more background regions in the generated optical flow image, are determined within a specified threshold range in the determined third distribution.

Abstract: Various aspects of a system and a method for background subtraction in video content are disclosed herein. The system includes an electronic device, which generates an optical flow image of an image frame from a sequence of image frames. A first distribution is determined based on pixel values of a plurality of pixels of the generated optical flow image. A second distribution is determined based on pixel values of a set of pixels of the plurality of pixels of the generated optical flow image. The set of pixels correspond to pixels that touch one or more borders of the image frame. A third distribution is further determined based on the determined first distribution and the determined second distribution. One or more peak values corresponding to one or more background regions in the generated optical flow image, are determined within a specified threshold range in the determined third distribution.

Abstract: An apparatus and method for optical flow and sensor input based background subtraction in video content, includes one or more processors configured to compute a plurality of first motion vector values for a plurality of pixels in a current image frame with respect to a previous image frame using an optical flow map. A plurality of second motion vector values are computed for the plurality of pixels in the current image frame based on an input received from a sensor provided in the apparatus. A confidence score is determined for the plurality of first motion vector values based on a set of defined parameters. One or more background regions are extracted from the current image frame based on the determined confidence score and a similarity parameter between the plurality of first motion vector values and the plurality of second motion vector values.

Abstract: Various aspects of a device and method for detection of regions based on sensor data for autofocus are disclosed herein. In accordance with an embodiment, the method includes receipt of a depth of filed (DOF) information of a scene viewed using an image-capturing device of the electronic device. The one or more images of the scene are segmented into a plurality of candidate segmented regions based on the received DOF information. One of the plurality of candidate segmented regions is determined as a region-of-interest to autofocus on the determined candidate segmented region.

Abstract: A method and a device, such as an imaging device, for image processing for automatic detection of focus area are disclosed herein. In accordance with an embodiment, the device for image processing is the imaging device that is configured to extract a plurality of object features of a plurality of objects in a field-of-view (FOV) of the imaging device. A plurality of confidence maps are generated based on the extracted plurality of object features. The imaging device is further configured to determine a focus area corresponding to the FOV, based on the generated plurality of confidence maps by use of a pre-determined rule.

Abstract: Various aspects of a system and method for video processing is disclosed herein. The system comprises a video processing device that is configured to generate a spatial saliency map based on spatial information associated with a current frame of a video stream. A spatio-temporal saliency map is generated based on at least motion information associated with the current frame and a previous frame of the video stream. Based on a weighted combination of the generated spatial saliency map and the generated spatio-temporal saliency map, a combined saliency map is generated.

Abstract: The thick lens calibration method enables better calibration of complex camera devices such as devices with thick lens systems. The thick lens calibration method includes a two step process of calibrating using the distance between a second nodal point and an image sensor, and calibrating using the distance between the first and second nodal point.

Abstract: A blur estimation method and apparatus which utilizes Gaussian differences at multiple resolutions, upon which iterative convolution is performed to match sharpness measures between two images. Blur differences are combined with an iteration number selected from multiple resolution levels based on comparing a sharpness ratio as the blur difference estimate. Blur difference estimate is then applied through a depth model to arrive at a depth estimate. Blur difference depth estimation can be utilized for controlling operation of focus control hardware in an imaging device.

Abstract: Focus detection is to determine whether an image is in focus or not. Focus detection is able to be used for improving camera autofocus performance. Focus detection by using only one feature does not provide enough reliability to distinguish in-focus and slightly out-of-focus images. A focus detection algorithm of combining multiple features used to evaluate sharpness is described herein. A large image data set with in-focus and out-of-focus images is used to develop the focus detector for separating the in-focus images from out-of-focus images. Many features such as iterative blur estimation, FFT linearity, edge percentage, wavelet energy ratio, improved wavelet energy ratio, Chebyshev moment ratio and chromatic aberration features are able to be used to evaluate sharpness and determine big blur images.

Abstract: Focus detection is to determine whether an image is in focus or not. Focus detection is able to be used for improving camera autofocus performance. Focus detection by using only one feature does not provide enough reliability to distinguish in-focus and slightly out-of-focus images. A focus detection algorithm of combining multiple features used to evaluate sharpness is described herein. A large image data set with in-focus and out-of-focus images is used to develop the focus detector for separating the in-focus images from out-of-focus images. Many features such as iterative blur estimation, FFT linearity, edge percentage, wavelet energy ratio, improved wavelet energy ratio, Chebyshev moment ratio and chromatic aberration features are able to be used to evaluate sharpness and determine big blur images.

Abstract: A defocus estimation algorithm is described herein. The defocus estimation algorithm utilizes a single image. A Laplacian of Gaussian approximation is determined by computing a difference of Gaussian. Defocus blur is able to be estimated by computing a blur difference between two images using the difference of Gaussian.