Abstract:
Systems, devices, methods, computer-readable media, techniques, and methodologies are disclosed for generating a template iris pattern using multiple image frames containing image data corresponding to detected light at different wavelengths along the electromagnetic (EM) spectrum including light in the infrared, near-infrared, and/or visible light bands.
Abstract:
This application describes techniques for providing computer vision for manual services. In some instances, a remote system may determine that a current time is within a threshold period of time of a scheduled service and, based on the determination, send a first message requesting image data to a camera apparatus located within an environment. After sending the first message, the remote system may receive image data associated with the environment from the camera apparatus and use the image to detect an object within the environment. The remote system can then send a second message to a device of a guest and a third message to a device of a user, where each of the second message and the third message indicates that the object is within the environment. In some instances, the object can include an animal while in some instances, the object can include a person.
Abstract:
Systems and techniques are generally described for pattern recognition in video data. In some examples, a first set of pixels representing a first person in the first video data may be determined. First feature representation data of the first set of pixels may be generated. The first feature representation data may be determined to correspond to second feature representation data stored in memory. An identity of the first person associated with the second feature representation data may be determined. A first action associated with the first person may be determined. A notification may be sent to a computing device accessible by the first person. The notification may include a reminder for the first person to engage in the first action.
Abstract:
Systems, methods, and computer-readable media are disclosed for determining a depth or reflectance of objects. Example methods may include illuminating a scene within a field of view of a device at a first illuminance value, detecting a reflected illuminance value, and determining a first reflectance value for a first object in the scene. Example methods may include identifying the first object, determining an orientation of the first object, and determining an estimated distance between the device and the first object based at least in part on the first illuminance value, the reflected illuminance value, and the first reflectance value.
Abstract:
A first device may emit a first modulated signal within an environment that includes a second device that is located close in proximity to an object. The second device may capture the first modulated signal and determine a second modulated signal having a phase that is different from that of the first modulated signal. In response to capturing the second modulated signal, the first device may determine a phase difference corresponding to the first modulated signal and the second modulated signal using time-of-flight (TOF). Using the phase difference, the first device may determine the distance between the first device and the object. In embodiments where the phase difference is zero, the first device may be unable to detect the presence of the object.
Abstract:
Various embodiments provide a method of enhancing images in low lighting conditions wherein a color image is captured with a color camera, a first monochromatic image is captured with a first monochromatic camera, and a second monochromatic image is captured with a second monochromatic camera. Image registration is performed to align the first and second monochromatic images and the luminance information from the registered monochromatic images is fused with chrominance information from the color image.
Abstract:
A method of classifying motion from video frames involves generating motion frames indicative of changes in pixel values between pairs of frames. The method also involves determining one-dimensional feature values based on the video frames or motion frames, such as the statistical values or linear transformation coefficients. Each one-dimensional feature value may be stored in a buffer, from which additional temporal feature values can be extracted indicative of the change of the one-dimensional feature values across a set of frames. A classifier may receive the one-dimensional feature values and the additional temporal feature values as inputs, and determine the class of motion present in the video frames. Some classes of motion, such as irrelevant motion, may be considered irrelevant to the execution of certain motion-triggered actions, such that the method may involve suppressing the performance of a motion-triggered action based on the determined class of motion.
Abstract:
Techniques are generally described for scene change detection. A first and second histogram representing frames of a video may be received. A Euclidean distance between the first and second histogram may be determined. A third histogram of a third frame may be received. Values of the third histogram may be compared to corresponding values of a background model of the environment. A fourth frame and a fifth frame of image data of the video may be received. A flow value between a first block of pixels of the fourth frame and a corresponding second block of pixels of the fifth frame may be determined. The flow value may be determined based on a motion vector and a sum of absolute differences between the first and second block. A portion of the video may be streamed to a remote computing device if the flow value exceeds a threshold value.
Abstract:
Systems, methods and devices for automatically cropping images taken by an electronic device in order to determine the identity of a product contained in the image are described herein. A number of different techniques may be applied to perform the automatic cropping, including a focus sweep technique in which a first image is analyzed for the presence of a human being and then a second image is taken in a plane closer to the camera than the first image. The two frames are analyzed and a resultant image is provided that avoids the regions in which the human being is present to focus on the product. In other embodiments, a motion vector calculation is made between two images in which an individual is holding a product. The motion vectors related to the human are removed and a bounding box is calculated to reduce the size of the image to include a higher percentage of the product, such that the product can be more easily identified.
Abstract:
Provided are systems and methods for smoke and fire detection. An example method includes receiving two or more image frames that represent images of two or more areas, combining the two or more image frames to generate a composite image frame that represents an image of an area comprising the two or more areas, comparing the composite image frame to a background frame associated with the area to determine one or more pixels of the composite frame having a pixel value that is different from corresponding pixels of the background frame, determining a smoke frame, wherein pixels of the smoke frame that correspond to the one or more pixels of the composite frame comprise a pixel value associated with smoke, determining a smoke value corresponding to pixel values of the pixels of the smoke frame, determining that the smoke value exceeds a smoke threshold value, determining a smoke or fire condition based at least in part on determining that the smoke value exceeds a smoke threshold value, and providing an indication of the smoke or fire condition.