Abstract:
Methods and apparatus to convert images for computer-vision systems are disclosed. An example computer-vision system includes an image converter (112) to convert a near infrared light first image (202) to form a visible light image (206), and to update a coefficient of the image converter (112) based on a difference (214), an object recognizer (102) to recognize an object (208) in the first visible light image (206), and an object recognition analyzer (210) to determine the difference (214) between the object (208) recognized in the first visible light image (206) and an object (212) associated with the near infrared light image (202).
Abstract:
Techniques related to object detection using binary coded images are discussed. Such techniques may include performing object detection based on multiple spatial correlation mappings between a generated binary coded image and a binary coded image based object detection model and nesting look up tables such that binary coded representations are grouped and such groups are associated with confidence values for performing object detection.
Abstract:
System, apparatus, method, and computer readable media for on-the-fly captured image data object tracking. An image or video stream is processed to detect and track an object in concurrence with generation of the stream by a camera module. In one exemplary embodiment, HD image frames are processed at a rate of 30 fps, or more, to track one or more target object. In embodiments, object detection is validated prior to employing detected object descriptor(s) as learning data to generate or update an object model. A device platform including a camera module and comporting with the exemplary architecture may provide 3A functions based on objects robustly tracked in accordance with embodiments.
Abstract:
A mechanism is described for facilitating efficient free in-plane rotation landmark tracking of images on computing devices according to one embodiment. A method of embodiments, as described herein, includes detecting a first frame having a first image and a second frame having a second image, where the second image is rotated to a position away from the first image. The method may further include assigning a first parameter line and a second parameter line to the second image based on landmark positions associated with the first and second images, detecting a rotation angle between the first parameter line and the second parameter line, and rotating the second image back and forth within a distance associated with the rotation angle to verify positions of the first and second images.
Abstract:
An example computer-vision system to convert images includes an image converter (112) to convert a near infrared light first image (202) to form a visible light image (206), and to update a coefficient of the image converter (112) based on a difference (214), an object recognizer (102) to recognize an object (208) in the first visible light image (206), and an object recognition analyzer (210) to determine the difference (214) between the object (208) recognized in the first visible light image (206) and an object (212) associated with the near infrared light image (202).
Abstract:
According to a method for providing a notification on a face recognition environment of the present disclosure, the method includes obtaining an input image that is input in a preview state, comparing feature information for a face included in the input image with feature information for a plurality of reference images of people stored in a predetermined database to determine, in real-time, whether the input image satisfies a predetermined effective condition for photographing. The predetermined effective condition for photographing is information regarding a condition necessary for recognizing the face included in the input image at a higher accuracy level than a predetermined accuracy level. The method further includes providing a user with a predetermined feedback for photographing guidance that corresponds to whether the predetermined effective condition for photographing is satisfied. According to the method, a condition of a face image detected for face recognition is checked, and if there is an unsuitable element in recognizing the face, it is notified to a user such that an obstruction environment hindering the face recognition by the user is removed, for enhancing a success rate of the face recognition.
Abstract:
Attributes of large scale computer vision systems may be made available to users of more limited processor-based systems by dynamically and adaptively updating recognizers into a smaller scale device from a connected larger scale device, based on the user's situational context and behavior. A recognizer is a hardware, software or firmware module specialized to use computer vision to recognize a defined class of imaged objects.
Abstract:
Apparatuses, methods and storage medium associated with face detection are disclosed. In embodiments, an apparatus may comprise one or more processors, cache memory, a lookup table of feature patterns, and a face detector. The lookup table is to be loaded into the cache memory, during operation, for use to detect whether a face is present in an image. The feature patterns within the lookup table are organized within the lookup table in accordance with relative connectivity of the feature patterns. The face detector to detect whether a face is present in an image, may use the lookup table of feature patterns loaded into the cache memory during detection for the face. Other embodiments may be described and/or claimed.
Abstract:
Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor. The machine learning component may be trained using a set of training images that depict human facial expressions and avatar animation authored by professional animators to reflect facial expressions depicted in the set of training images.