The current document is directed to automated methods and systems, controlled by various constraints and parameters, that identify contours in digital images, including curved contours. Certain of these parameters constrain contour identification to those contours in which the local curvature of a contour does not exceed a threshold local curvature and to those contours orthogonal to intensity gradients of at least threshold magnitudes. The currently described methods and systems identify seed points within a digital image, extend line segments from the seed points as an initial contour coincident with the seed point, and then iteratively extend the initial contour by adding line segments to one or both ends of the contour. The identified contours are selectively combined and filtered in order to identify a set of relevant contours for use in subsequent image-processing tasks.

Various embodiments are generally directed to systems for summarizing data visualizations (i.e., images of data visualizations), such as a graph image, for instance. Some embodiments are particularly directed to a personalized graph summarizer that analyzes a data visualization, or image, to detect pre-defined patterns within the data visualization, and produces a textual summary of the data visualization based on the pre-defined patterns detected within the data visualization. In various embodiments, the personalized graph summarizer may include features to adapt to the preferences of a user for generating an automated, personalized computer-generated narrative. For instance, additional pre-defined patterns may be created for detection and/or the textual summary may be tailored based on user preferences. In some such instances, one or more of the user preferences may be automatically determined by the personalized graph summarizer without requiring the user to explicitly indicate them. Embodiments may integrate machine learning and computer vision concepts.

An imaging system is provided herein. An image sensor is configured to acquire one or more images of a scene external and forward of a controlled vehicle and to generate image data corresponding to the acquired images. A controller is communicatively connected to the image sensor and is configured to receive and analyze the image data. The controller detects an object of interest in the image data and generates an ON signal or an OFF signal based on the detection of the object of interest in the image data, or lack thereof. A high beam control of the vehicle is turned ON based on the ON signal or turned OFF based on the OFF signal. The controller modifies a future response time at which the OFF signal is generated based on an external overriding of the ON signal or the OFF signal.

An analysis method for breast image and an electronic apparatus using the same are provided. The method includes the following steps. A breast image scanned by an ultrasound wave is obtained. Based on rectangular features of the breast image, a region of interest including an aberrant region in the breast image is obtained by applying a detection model. The aberrant region is further acquired from the region of interest, and a plurality of feature parameters of the aberrant region are extracted for a property analysis of the aberrant region.

Systems, methods, and non-transitory computer-readable media can acquire an image that depicts at least one character. A set of pixels, within the image, through which the at least one character is depicted can be identified. At least one linear portion, within the image, can be identified based on the set of pixels. For each sub-portion within the at least one linear portion, a respective first confidence score representing a respective first likelihood that a respective sub-portion depicts the at least one character can be determined.

In one aspect, the present disclosure implements a method of ranking images in real-time as the images are being received. In this regard, the method comprises receiving a first and a second images from end users. Then, the first and second images are made available to two or more human annotators from network accessible computing devices. The method provided by the present disclosure then receives designations from each of the two or more human annotators regarding whether the first or second image is preferred. From the received input, a determination is made, in the aggregate, whether the two or more human annotators preferred the first or second image. If the two or more human annotators preferred the first image, the method allocates a rank to the first image that is higher than the second image. On the other hand, if the two or more human annotators preferred the second image, the method allocates a rank to the second image that is higher than the first image.

In various example embodiments, a system and method are provided for automated estimation of a saliency map for an image based on a graph structure comprising nodes corresponding to respective superpixels on the image, the graph structure including boundary-connecting nodes that connects each non-boundary node to one or more boundary regions. Each non-boundary node is in some embodiments connected to all boundary nodes by respective boundary-connecting edges forming part of the graph. Edge weights are calculated to generate a weighted graph. Saliency map estimation comprises bringing respective nodes for similarity to a background query. The edge weights of at least some of the edges are in some embodiments calculated as a function of a geodesic distance or shortest path between the corresponding nodes.

A method of determining a summation of pixel characteristics for a rectangular region of a digital image includes determining if a base address for a data element in an integral image buffer is aligned for an SIMD operation by a processor embedded in an electronic assembly configured to perform Haar-like feature calculations. The data element represents a corner of the rectangular region of an integral image. The integral image is a representation of the digital image. The integral image is formed by data elements stored in the integral image buffer. The data element is loaded from the integral image buffer to the processor when the base address is aligned for the SIMD operation. An offset data element of an offset integral image is loaded from an offset integral buffer when the base address is non-aligned for the SIMD operation. The offset data element represents the corner of the rectangular region.

Systems and methods for recognition of unreadable characters on printed circuit boards. In some embodiments, a method for recognizing characters can be utilized for recognition of damaged characters on a printed circuit board. The method can include obtaining a digital image for each of a plurality of characters on the printed circuit board. The method can further include dividing each digital image into an array of regions. The method can further include generating a data structure from the arrays of the digital images. The data structure can include gradient features based on stroke shapes on small distances, structural features based on stroke trajectories on extended distances, and concavity features based on stroke relationships.

A method for recognizing an object in a video stream may include receiving a video stream from a video source, the video stream comprising a plurality of video frames. The method may also include selecting at least one video frame from the video frames according to a frame selection rate. The method may also include partitioning the selected video frame into a first plurality of image blocks. The method may also include recognizing, out of the first plurality of image blocks, a second plurality of image blocks which comprise an image of an object, the recognition being based on an image recognition parameter determined by a machine-learning algorithm. The method may also include determining that at least one of the second plurality of image blocks corresponds to the object based on a likelihood metric, the likelihood metric being determined by the processor based on at least the frame selection rate. The method may further include displaying, on a display, information identifying the object. A system and non-transitory computer-readable medium may also be provided.