Abstract: Systems and methods of detecting an object using motion estimation may include a processor and motion estimation and object detection logic coupled to the processor. The motion estimation and object detection logic may be configured to include logic to detect an object in a frame of a video based on motion estimation. The video may include a first frame and a second frame. The motion estimation may be performed on a region of the second frame using sum of absolute difference between the region of the second frame and a corresponding region of the first frame.

Abstract: Systems, apparatus, articles, and methods are described related to a hardware-based convolution pre-filter to accelerate object detection.

Abstract: Techniques related to skin tone tuned image enhancement are described. Such techniques may include performing an enhancement of a pixel of an input image based on a skin tone detection score and local detail information of the pixel.

Abstract: One or more apparatus and method for compressing xvYCC or sRGB color to a narrower color gamut. Embodiments may be utilized in processing images/video image sequences provided in a wide gamut color space for display in standard RGB color space. In further embodiments, an sRGB or xvYCC compliant input is compressed to a gamut narrower than sRGB. Embodiments where a narrow output color gamut lacks a fully-defined 3D color space, semi-fixed-hue gamut compression may be performed by adjusting parameters provided to a fixed-hue compression module capable of mapping 3D color space representations between well-defined gamuts.

Abstract: Systems, apparatus and methods are described related to accelerated object detection filter using a video estimation module.

Abstract: Systems, apparatus, articles, and methods are described related to a hardware-based convolution pre-filter to accelerate object detection.

Abstract: Systems, apparatus, articles, and methods are described including operations for color enhancement via gamut expansion.

Abstract: Apparatuses, methods and storage medium associated with processing an image are disclosed herein. In embodiments, a method for processing one or more images may include generating a plurality of pairs of keypoint features for a pair of images. Each pair of keypoint features may include a keypoint feature from each image. Further, for each pair of keypoint features, corresponding adjoin features may be generated. Additionally, for each pair of keypoint features, whether the adjoin features are similar may be determined. Whether the pair of images have at least one similar object may also be determined, based at least in part on a result of the determination of similarity between the corresponding adjoin features. Other embodiments may be described and claimed.

Abstract: Systems, articles, and methods are described including bit-rate control for video coding using object-of-interest data.

Abstract: Systems, apparatus and methods are described related to accelerated object detection filter using a video estimation module.

Abstract: Systems and methods of detecting an object using motion estimation may include a processor and motion estimation and object detection logic coupled to the processor. The motion estimation and object detection logic may be configured to include logic to detect an object in a frame of a video based on motion estimation. The video may include a first frame and a second frame. The motion estimation may be performed on a region of the second frame using sum of absolute difference between the region of the second frame and a corresponding region of the first frame.

Abstract: Systems, apparatus, articles, and methods are described including operations for color enhancement via gamut expansion.

Abstract: Techniques are described that can be used to either compress or expand video. Color compression techniques are described that can be used to compress the wide color gamut content into lower color gamut for inclusion in a baseline layer. Color expansion techniques are described that convert lower color gamut data into wider color gamut format for inclusion in an enhancement layer. Both of the baseline video stream and enhancement layer video streams may be transmitted through a channel or stored in a memory device to be viewed later. Accordingly, both baseline and enhancement video layers are available so that either lower or higher quality displays can be used to display video.

Abstract: Techniques are disclosed involving contrast adjustment for images. For example, an input image is classified based on its pixel value characteristics, as expressed in an input brightness histogram. From such a classification, a target histogram distribution for a corresponding output image (i.e., a contrast-adjusted transformation of the input image) may be specified. With the target histogram of the output image specified, a transformation function may be derived that maps input image values to output image values. Moreover, transitions of such transformation functions may be smoothed. Such smoothing may provide advantages, such as a reduction in flickering associated with video data.

Abstract: Methods and systems to manipulate color processing parameters to allow the detection of an arbitrary color of interest. Such reconfigurations may enable general point-of-interest color processing. Color mapping curves may also be configured, to accomplish the tasks of color correction, enhancement, de-saturation, and color compression.

Abstract: Techniques are described that can be used to either compress or expand video. Color compression techniques are described that can be used to compress the wide color gamut content into lower color gamut for inclusion in a baseline layer. Color expansion techniques are described that convert lower color gamut data into wider color gamut format for inclusion in an enhancement layer. Both of the baseline video stream and enhancement layer video streams may be transmitted through a channel or stored in a memory device to be viewed later. Accordingly, both baseline and enhancement video layers are available so that either lower or higher quality displays can be used to display video.

Abstract: Techniques are disclosed involving contrast adjustment for images. For example, an input image is classified based on its pixel value characteristics, as expressed in an input brightness histogram. From such a classification, a target histogram distribution for a corresponding output image (i.e., a contrast-adjusted transformation of the input image) may be specified. With the target histogram of the output image specified, a transformation function may be derived that maps input image values to output image values. Moreover, transitions of such transformation functions may be smoothed. Such smoothing may provide advantages, such as a reduction in flickering associated with video data.

Abstract: System, method, and computer program product to adaptively blend the interpolation results from an 8-tap Lanczos filter and the interpolation results from a bilinear filter, according to the local transitions of the input content. Artifacts may occur, which may be identified as such and corrected. Pixels that represent artifacts in the blended image may be replaced with the pixel for that location taken from the bilinear interpolation.

Abstract: Methods and systems to manipulate color processing parameters to allow the detection of an arbitrary color of interest. Such reconfigurations may enable general point-of-interest color processing. Color mapping curves may also be configured, to accomplish the tasks of color correction, enhancement, de-saturation, and color compression.

Abstract: A method is provided for performing a classification. The method includes ranking a plurality of features of a training set according to how closely they are correlated to their corresponding classifications, extracting a plurality of features of from input data, selecting a subset of the plurality of features such that a computational resource cost of the subset is less than a predefined computational resource maximum and a degree of utility achieved by a classification of the subset by a selected classifier is optimized and exceeds a predefined utility minimum, predicting one of the features of the sensor data that is not selected for the subset of features from a predefined number of past samples of the feature and adding the predicted feature to the subset of features, and classifying, by a processor, using the selected classifier and the resulting subset of features.