Abstract: An arc detection method includes classifying whether an arc fault is present in the power system by, for each of a plurality of bins of a current frame of a signal, marking the bin as a candidate bin if a magnitude spectrum of the bin meets first criteria; determining a number of candidate bins in the current frame; marking the number of candidate bins as candidate cluster bins if the number of candidate bins exceeds a minimum cluster size; for each of the candidate cluster bins, determining whether the candidate cluster bin is also a candidate cluster bin of a previous frame of the first signal and if so, identifying the current frame as a candidate frame and incrementing a candidate frame count; and if the candidate frame count exceeds a candidate frame count threshold, determining that an arc fault is present in the power system.

Abstract: An arc detection method includes classifying whether an arc fault is present in the power system by, for each of a plurality of bins of a current frame of a signal, marking the bin as a candidate bin if a magnitude spectrum of the bin meets first criteria; determining a number of candidate bins in the current frame; marking the number of candidate bins as candidate cluster bins if the number of candidate bins exceeds a minimum cluster size; for each of the candidate cluster bins, determining whether the candidate cluster bin is also a candidate cluster bin of a previous frame of the first signal and if so, identifying the current frame as a candidate frame and incrementing a candidate frame count; and if the candidate frame count exceeds a candidate frame count threshold, determining that an arc fault is present in the power system.

Abstract: A method includes determining a position and length of a non-zero run in a row of a pixel map. The method also includes determining a number of neighbors for the non-zero run in a preceding row, based at least in part on the position and the length. In addition, the method includes updating a correspondence map of the non-zero run and a correspondence map of a first neighbor of the non-zero run, based at least in part on a correspondence map of a second neighbor of the non-zero run, in response to a determination that the non-zero run has at least two neighbors in the preceding row.

Abstract: In one aspect, there is disclosed a digital signal processor and method performed by the same for performing object detection, including facial detection, in a reduced number of clock cycles. The method comprises using Sobel edge detection to identify regions with many edges, and classifying those regions as foreground candidates. Foreground candidates are further checked for vertical or horizontal symmetry, and symmetrical windows are classified as face candidates. Viola-Jones type facial detection is then performed only on those windows identified as face candidates.

Abstract: The Hough transform for circles can be implemented in a manner that avoids random access to the Hough accumulator array by successively identifying center candidates in each line of the image based on edge pixels in corresponding lines voting on the line of interest.

Abstract: In one aspect, there is disclosed a digital signal processor and method performed by the same for performing object detection, including facial detection, in a reduced number of clock cycles. The method comprises using Sobel edge detection to identify regions with many edges, and classifying those regions as foreground candidates. Foreground candidates are further checked for vertical or horizontal symmetry, and symmetrical windows are classified as face candidates. Viola-Jones type facial detection is then performed only on those windows identified as face candidates.

Abstract: A method for tracing edges of an image using hysteresis thresholding includes: (i) receiving an edge map of the image, (ii) scanning one row of the input edge map, (iii) assigning a label to each edge pixel in the row based at least in part on the presence or absence of a neighboring edge pixel, (iv) grouping contiguous labels, and (v) identifying groups of edge pixels.

Abstract: A method includes determining a position and length of a non-zero run in a row of a pixel map. The method also includes determining a number of neighbors for the non-zero run in a preceding row, based at least in part on the position and the length. In addition, the method includes updating a correspondence map of the non-zero run and a correspondence map of a first neighbor of the non-zero run, based at least in part on a correspondence map of a second neighbor of the non-zero run, in response to a determination that the non-zero run has at least two neighbors in the preceding row.

Abstract: A method for identifying objects in a digital image includes tracing chain codes associated with the contour of the object; a series of states is maintained, and the next chain codes in the contour are accepted only if they comply with allowed chain codes for each state. Certain chain codes trigger a transition into a next state. If a disallowed chain code is encountered, the process halts.

Abstract: The Hough transform for circles can be implemented in a manner that avoids random access to the Hough accumulator array by successively identifying center candidates in each line of the image based on edge pixels in corresponding lines voting on the line of interest.

Abstract: A method for identifying objects in a digital image includes tracing chain codes associated with the contour of the object; a series of states is maintained, and the next chain codes in the contour are accepted only if they comply with allowed chain codes for each state. Certain chain codes trigger a transition into a next state. If a disallowed chain code is encountered, the process halts.

Abstract: A method for tracing edges of an image using hysteresis thresholding includes: (i) receiving an edge map of the image, (ii) scanning one row of the input edge map, (iii) assigning a label to each edge pixel in the row based at least in part on the presence or absence of a neighboring edge pixel, (iv) grouping contiguous labels, and (v) identifying groups of edge pixels.