Abstract: Example systems and methods for controlling an illumination source are described. An example apparatus for controlling an illumination source may include a memory and a processor implemented in circuitry and coupled to the memory. The processor may be configured to process a first frame captured by a video capture device when an illumination source is active, process a second frame captured by the video capture device when the illumination source is inactive, and compare the first frame and the second frame. The processor may also be configured to instruct the illumination source to be active or inactive, based on the comparison, for capture of a third frame by the video capture device.

Abstract: Example systems and methods for controlling an illumination source are described. An example apparatus for controlling an illumination source may include a memory and a processor implemented in circuitry and coupled to the memory. The processor may be configured to process a first frame captured by a video capture device when an illumination source is active, process a second frame captured by the video capture device when the illumination source is inactive, and compare the first frame and the second frame. The processor may also be configured to instruct the illumination source to be active or inactive, based on the comparison, for capture of a third frame by the video capture device.

Abstract: A controller can selectively activate an illumination source. The controller includes a processor in communication with a memory configured to store video frames. The controller is configured to selectively activate an illumination source during capture of two successive video frames in a video capture session, so the illumination source illuminates a scene while a video capture device is capturing one of the successive video frames, and the illumination source does not illuminate the scene while the video capture device is capturing the other of the two successive video frames. The controller can determine whether a difference in image quality between the two successive video frames reaches at least a threshold difference. Based on the determination of whether the difference reaches the threshold difference, the controller can determine whether to activate or deactivate the illumination source while capturing subsequent video frames during the video capture session.

Abstract: A method, apparatus, and manufacture for smiling face detection is provided. For each frame, a list of new smiling faces for the frame is generated by performing smiling face detection employing an object classifier that trained is to distinguish between smiling faces and all objects in the frame that are not smiling faces. For the first frame, the list of new smiling faces is employed as an input smiling face list for the next frame. For each frame after the first frame, a list of tracked smiles for the frame is generated by tracking smiling faces in the frame from the input smiling list for the frame. Further, a list of new smiling faces is generated for the next frame by combining the list of new smiling faces for the frame with the list of tracked smiles for the frame.

Abstract: A method, apparatus, and manufacture for smiling face detection is provided. For each frame, a list of new smiling faces for the frame is generated by performing smiling face detection employing an object classifier that trained is to distinguish between smiling faces and all objects in the frame that are not smiling faces. For the first frame, the list of new smiling faces is employed as an input smiling face list for the next frame. For each frame after the first frame, a list of tracked smiles for the frame is generated by tracking smiling faces in the frame from the input smiling list for the frame. Further, a list of new smiling faces is generated for the next frame by combining the list of new smiling faces for the frame with the list of tracked smiles for the frame.

Abstract: Improved face tracking is provided during determination of an image by an imaging device using a low power face tracking unit. In one embodiment, image data associated with a frame and one or more face detection windows from a face detection unit may be received by the face tracking unit. The face detection windows are associated with the image data of the frame. A face list may be determined based on the face detection windows and one or more faces may be selected from the face list to generate an output face list. The output face list may then be provided to a processor of an imaging device for the detection of an image based on at least one of coordinate and scale values of the one or more faces on the output face list.

Abstract: Improved face tracking is provided during determination of an image by an imaging device using a low power face tracking unit. In one embodiment, image data associated with a frame and one or more face detection windows from a face detection unit may be received by the face tracking unit. The face detection windows are associated with the image data of the frame. A face list may be determined based on the face detection windows and one or more faces may be selected from the face list to generate an output face list. The output face list may then be provided to a processor of an imaging device for the detection of an image based on at least one of coordinate and scale values of the one or more faces on the output face list.

Abstract: A method, apparatus, and manufacture for smiling face detection is provided. For each frame, a list of new smiling faces for the frame is generated by performing smiling face detection employing an object classifier that trained is to distinguish between smiling faces and all objects in the frame that are not smiling faces. For the first frame, the list of new smiling faces is employed as an input smiling face list for the next frame. For each frame after the first frame, a list of tracked smiles for the frame is generated by tracking smiling faces in the frame from the input smiling list for the frame. Further, a list of new smiling faces is generated for the next frame by combining the list of new smiling faces for the frame with the list of tracked smiles for the frame.

Abstract: Improved face tracking is provided during determination of an image by an imaging device using a low power face tracking unit. In one embodiment, image data associated with a frame and one or more face detection windows from a face detection unit may be received by the face tracking unit. The face detection windows are associated with the image data of the frame. A face list may be determined based on the face detection windows and one or more faces may be selected from the face list to generate an output face list. The output face list may then be provided to a processor of an imaging device for the detection of an image based on at least one of coordinate and scale values of the one or more faces on the output face list.

Abstract: A method, apparatus, and manufacture for smiling face detection is provided. For each frame, a list of new smiling faces for the frame is generated by performing smiling face detection employing an object classifier that trained is to distinguish between smiling faces and all objects in the frame that are not smiling faces. For the first frame, the list of new smiling faces is employed as an input smiling face list for the next frame. For each frame after the first frame, a list of tracked smiles for the frame is generated by tracking smiling faces in the frame from the input smiling list for the frame. Further, a list of new smiling faces is generated for the next frame by combining the list of new smiling faces for the frame with the list of tracked smiles for the frame.

Abstract: The likelihood of a particular type of object, such as a human face, being present within a digital image, and its location in that image, are determined by comparing the image data within defined windows across the image in sequence with two or more sets of data representing features of the particular type of object. The evaluation of each set of features after the first is preferably performed only on data of those windows that pass the evaluation with respect to the first set of features, thereby quickly narrowing potential target windows that contain at least some portion of the object. Correlation scores are preferably calculated by the use of non-linear interpolation techniques in order to obtain a more refined score. Evaluation of the individual windows also preferably includes maintaining separate feature set data for various positions of the object around one axis and rotating the feature set data with respect to the image data for the individual windows about another axis.

Abstract: A device for noise reduction is provided. The device includes a noise reduction three-dimensional look-up table (LUT) and a noise reduction unit. The noise reduction LUT transforms an input image into a noise reduction factor and noise reduction threshold for each color component of each pixel of the input image. The noise reduction unit performs noise reduction on the input image based on the noise reduction factors and noise reduction thresholds determined from the noise reduction LUT.

Abstract: In a method of mapping data from a source space to a target space, a space transformation look-up table (LUT) that contains a plurality of locations storing information is maintained, wherein each of the plurality of locations includes information specifying a function to be evaluated. First data defined according to a multi-dimensional source space is input, and second data defined according to a multi-dimensional target space is generated, by applying information contained in the LUT to the first data.

Abstract: The likelihood of a particular type of object, such as a human face, being present within a digital image, and its location in that image, are determined by comparing the image data within defined windows across the image in sequence with two or more sets of data representing features of the particular type of object. The evaluation of each set of features after the first is preferably performed only on data of those windows that pass the evaluation with respect to the first set of features, thereby quickly narrowing potential target windows that contain at least some portion of the object. Correlation scores are preferably calculated by the use of non-linear interpolation techniques in order to obtain a more refined score. Evaluation of the individual windows also preferably includes maintaining separate feature set data for various positions of the object around one axis and rotating the feature set data with respect to the image data for the individual windows about another axis.

Abstract: The likelihood of a particular type of object, such as a human face, being present within a digital image, and its location in that image, are determined by comparing the image data within defined windows across the image in sequence with two or more sets of data representing features of the particular type of object. The evaluation of each set of features after the first is preferably performed only on data of those windows that pass the evaluation with respect to the first set of features, thereby quickly narrowing potential target windows that contain at least some portion of the object. Correlation scores are preferably calculated by the use of non-linear interpolation techniques in order to obtain a more refined score. Evaluation of the individual windows also preferably includes maintaining separate feature set data for various positions of the object around one axis and rotating the feature set data with respect to the image data for the individual windows about another axis.

Abstract: A method of performing color space conversion includes maintaining a data structure that has a plurality of dimensions and that contains a plurality of locations storing information in each of the dimensions. Each of the dimensions corresponds to a different one of a plurality of input color signal components. The number of locations which the data structure contains in a first one of the dimensions is different from the number of locations which the data structure contains in a second one of the dimensions. The method further includes inputting first data defined according to a first color space, and generating second data defined according to a second color space, by applying information contained in the data structure to the first data.

Abstract: In a method of mapping data from a source space to a target space, a space transformation look-up table (LUT) that contains a plurality of locations storing information is maintained, wherein each of the plurality of locations includes information specifying a function to be evaluated. First data defined according to a multi-dimensional source space is input, and second data defined according to a multi-dimensional target space is generated, by applying information contained in the LUT to the first data.

Abstract: Improved face tracking is provided during determination of an image by an imaging device using a low power face tracking unit. In one embodiment, image data associated with a frame and one or more face detection windows from a face detection unit may be received by the face tracking unit. The face detection windows are associated with the image data of the frame. A face list may be determined based on the face detection windows and one or more faces may be selected from the face list to generate an output face list. The output face list may then be provided to a processor of an imaging device for the detection of an image based on at least one of coordinate and scale values of the one or more faces on the output face list.

Abstract: A device for noise reduction is provided. The device includes a noise reduction three-dimensional look-up table (LUT) and a noise reduction unit. The noise reduction LUT transforms an input image into a noise reduction factor and noise reduction threshold for each color component of each pixel of the input image. The noise reduction unit performs noise reduction on the input image based on the noise reduction factors and noise reduction thresholds determined from the noise reduction LUT.

Abstract: The likelihood of a particular type of object, such as a human face, being present within a digital image, and its location in that image, are determined by comparing the image data within defined windows across the image in sequence with two or more sets of data representing features of the particular type of object. The evaluation of each set of features after the first is preferably performed only on data of those windows that pass the evaluation with respect to the first set of features, thereby quickly narrowing potential target windows that contain at least some portion of the object. Correlation scores are preferably calculated by the use of non-linear interpolation techniques in order to obtain a more refined score. Evaluation of the individual windows also preferably includes maintaining separate feature set data for various positions of the object around one axis and rotating the feature set data with respect to the image data for the individual windows about another axis.