Abstract: Embodiments include systems and methods that may be performed by a processor of a computing device. Embodiments may be applied for keypoint detection in an image. In embodiments, the processor of the computing device may apply to an image a first-stage neural network to define and output a plurality of regions, apply to each of the plurality of regions a respective second-stage neural network to output a plurality of keypoints in each of the plurality of regions, and apply to the plurality of keypoints a third-stage neural network to determine a correction for each of the plurality of keypoints to provide corrected keypoints.

Abstract: A method is presented. The method includes determining a number of landmarks in an image comprising multiple pixels. The method also includes determining a number of channels for the image based on a function of the number of landmarks. The method further includes determining, for each one of the number of channels, a confidence of each pixel of the multiple pixels corresponding to a landmark. The method still further includes identifying the landmark in the image based on the confidence.

Abstract: Embodiments include systems and methods that may be performed by a processor of a computing device. Embodiments may be applied for keypoint detection in an image. In embodiments, the processor of the computing device may apply to an image a first-stage neural network to define and output a plurality of regions, apply to each of the plurality of regions a respective second-stage neural network to output a plurality of keypoints in each of the plurality of regions, and apply to the plurality of keypoints a third-stage neural network to determine a correction for each of the plurality of keypoints to provide corrected keypoints.

Abstract: Embodiments include systems and methods for keypoint detection in an image. In embodiments, a processor of a computing device may apply to an image a first neural network that has been trained to define and output a plurality of regions. The processor may apply to each of the plurality of regions a respective second neural network to that has been trained to output a plurality of keypoints in each of the plurality of regions. The processor may apply to the plurality of keypoints a third neural network that has been trained to determine a correction for each of the plurality of keypoints to provide corrected keypoints suitable for the execution of an image processing function.

Abstract: A method is presented. The method includes determining a number of landmarks in an image comprising multiple pixels. The method also includes determining a number of channels for the image based on a function of the number of landmarks. The method further includes determining, for each one of the number of channels, a confidence of each pixel of the multiple pixels corresponding to a landmark. The method still further includes identifying the landmark in the image based on the confidence.

Abstract: Embodiments include systems and methods for keypoint detection in an image. In embodiments, a processor of a computing device may apply to an image a first neural network that has been trained to define and output a plurality of regions. The processor may apply to each of the plurality of regions a respective second neural network to that has been trained to output a plurality of keypoints in each of the plurality of regions. The processor may apply to the plurality of keypoints a third neural network that has been trained to determine a correction for each of the plurality of keypoints to provide corrected keypoints suitable for the execution of an image processing function.

Abstract: Methods, systems, and devices for object detection are described. A device may extract features from an image and identify a region within the image for object detection. The device may apply an object model to a first set of features corresponding to positions within the region and one or more channels of the image. The first set of features may be selected so that the object model can detect a first orientation of an object. The device may also apply the object model to a second set of features, from the region, that are different from the first set of features. The second set of features may be selected so that the object model can detect a second orientation of the object (e.g., a flipped or rotated orientation of the object with respect to the first orientation).

Abstract: Methods, systems, and devices for object detection are described. A device may extract features from an image and identify a region within the image for object detection. The device may apply an object model to a first set of features corresponding to positions within the region and one or more channels of the image. The first set of features may be selected so that the object model can detect a first orientation of an object. The device may also apply the object model to a second set of features, from the region, that are different from the first set of features. The second set of features may be selected so that the object model can detect a second orientation of the object (e.g., a flipped or rotated orientation of the object with respect to the first orientation).

Abstract: A classifier for detecting objects in images can be configured to receive features of an image from a feature extractor. The classifier can determine a feature window based on the received features, and allows access by each decision tree of the classifier to only a predetermined area of the feature window. Each decision tree of the classifier can compare a corresponding predetermined area of the feature window with one or more thresholds. The classifier can determine an object in the image based on the comparisons. In some examples, the classifier can determine objects in a feature window based on received features, where the received features are based on color information for an image.