| Patent application number | Description | Published |
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| 20080279467 | Learning image enhancement - Image enhancement techniques are described to enhance an image in accordance with a set of training images. In an implementation, an image color tone map is generated for a facial region included in an image. The image color tone map may be normalized to a color tone map for a set of training images so that the image color tone map matches the map for the training images. The normalized color tone map may be applied to the image to enhance the in-question image. In further implementations, the procedure may be updated when the average color intensity in non-facial regions differs from an accumulated mean by a threshold amount. | 11-13-2008 |
| 20080317371 | VIDEO NOISE REDUCTION - A video noise reduction technique is presented. Generally, the technique involves first decomposing each frame of the video into low-pass and high-pass frequency components. Then, for each frame of the video after the first frame, an estimate of a noise variance in the high pass component is obtained. The noise in the high pass component of each pixel of each frame is reduced using the noise variance estimate obtained for the frame under consideration, whenever there has been no substantial motion exhibited by the pixel since the last previous frame. Evidence of motion is determined by analyzing the high and low pass components. | 12-25-2008 |
| 20090018980 | MULTIPLE-INSTANCE PRUNING FOR LEARNING EFFICIENT CASCADE DETECTORS - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
| 20090018981 | LEARNING CLASSIFIERS USING COMBINED BOOSTING AND WEIGHT TRIMMING - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
| 20090018985 | HISTOGRAM-BASED CLASSIFIERS HAVING VARIABLE BIN SIZES - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
| 20090172779 | MANAGEMENT OF SPLIT AUDIO/VIDEO STREAMS - Described herein is a method that includes receiving multiple requests for access to an exposed media object, wherein the exposed media object represents a live media stream that is being generated by a media source. The method also includes receiving data associated with each entity that provided a request, and determining, for each entity, whether the entities that provided the request are authorized to access the media stream based at least in part upon the received data and splitting the media stream into multiple media streams, wherein a number of media streams corresponds to a number of authorized entities. The method also includes automatically applying at least one policy to at least one of the split media streams based at least in part upon the received data. | 07-02-2009 |
| 20090220165 | EFFICIENT IMAGE DISPLAYING - Efficient image display on a display screen (e.g., in terms of number, space, resolution, and/or distortion) is facilitated by implementing one or more specialized select and pack routines for images. That is, representative images are selected from an image database, based on desired resolution and distortion, then resized and packed into a display arrangement that enhances use of display screen space. This allows, for example, images to be sent to a user from an image database more quickly, with more desirable resolution, and less distortion than traditional display techniques. | 09-03-2009 |
| 20090263010 | ADAPTING A PARAMETERIZED CLASSIFIER TO AN ENVIRONMENT - A classifier is trained on a first set of examples, and the trained classifier is adapted to perform on a second set of examples. The classifier implements a parameterized labeling function. Initial training of the classifier optimizes the labeling function's parameters to minimize a cost function. The classifier and its parameters are provided to an environment in which it will operate, along with an approximation function that approximates the cost function using a compact representation of the first set of examples in place of the actual first set. A second set of examples is collected, and the parameters are modified to minimize a combined cost of labeling the first and second sets of examples. The part of the combined cost that represents the cost of the modified parameters applied to the first set is calculated using the approximation function. | 10-22-2009 |
| 20090327418 | PARTICIPANT POSITIONING IN MULTIMEDIA CONFERENCING - A multimedia conference technique is disclosed that allows physically remote users to participate in an immersive telecollaborative environment by synchronizing multiple data, images and sounds. The multimedia conference implementation provides users with the perception of being in the same room visually as well as acoustically according to an orientation plan which reflects each remote user's position within the multimedia conference environment. | 12-31-2009 |
| 20100085416 | Multi-Device Capture and Spatial Browsing of Conferences - Multi-device capture and spatial browsing of conferences is described. In one implementation, a system detects cameras and microphones, such as the webcams on participants' notebook computers, in a conference room, group meeting, or table game, and enlists an ad-hoc array of available devices to capture each participant and the spatial relationships between participants. A video stream composited from the array is browsable by a user to navigate a 3-dimensional representation of the meeting. Each participant may be represented by a video pane, a foreground object, or a 3-D geometric model of the participant's face or body displayed in spatial relation to the other participants in a 3-dimensional arrangement analogous to the spatial arrangement of the meeting. The system may automatically re-orient the 3-dimensional representation as needed to best show the currently interesting event such as current speaker or may extend navigation controls to a user for manually viewing selected participants or nuanced interactions between participants. | 04-08-2010 |
| 20100225743 | Three-Dimensional (3D) Imaging Based on MotionParallax - Techniques and technologies are described herein for motion parallax three-dimensional (3D) imaging. Such techniques and technologies do not require special glasses, virtual reality helmets, or other user-attachable devices. More particularly, some of the described motion parallax 3D imaging techniques and technologies generate sequential images, including motion parallax depictions of various scenes derived from clues in views obtained of or created for the displayed scene. | 09-09-2010 |
| 20100289904 | VIDEO CAPTURE DEVICE PROVIDING MULTIPLE RESOLUTION VIDEO FEEDS - Systems are disclosed that provide improved transfer speed of video data from a video capture device to a computing device using multiple video feeds respectively comprising different resolutions. A high-resolution image sensor is used to convert light images into a high-resolution video data stream. A down sampler converts the high-resolution video data stream to a low-resolution video data stream, so that both a low-resolution data stream and a high-resolution data stream are available. While the low resolution-data stream can be sent to the computing device, a digital signal processor (DSP) processes the high-resolution video data stream in accordance with an input control signal that is comprised of desired high-resolution video stream parameters derived from the low-resolution video data stream. | 11-18-2010 |
| 20100329358 | MULTI-VIEW VIDEO COMPRESSION AND STREAMING - Multi-view video that is being streamed to a remote device in real time may be encoded. Frames of a real-world scene captured by respective video cameras are received for compression. A virtual viewpoint, positioned relative to the video cameras, is used to determine expected contributions of individual portions of the frames to a synthesized image of the scene from the viewpoint position using the frames. For each frame, compression rates for individual blocks of a frame are computed based on the determined contributions of the individual portions of the frame. The frames are compressed by compressing the blocks of the frames according to their respective determined compression rates. The frames are transmitted in compressed form via a network to a remote device, which is configured to render the scene using the compressed frames. | 12-30-2010 |
| 20100329517 | BOOSTED FACE VERIFICATION - Techniques for face verification are described. Local binary pattern (LBP) features and boosting classifiers are used to verify faces in images. A boosted multi-task learning algorithm is used for face verification in images. Finally, boosted face verification is used to verify faces in videos. | 12-30-2010 |
| 20110119210 | Multiple Category Learning for Training Classifiers - Described is multiple category learning to jointly train a plurality of classifiers in an iterative manner. Each training iteration associates an adaptive label with each training example, in which during the iterations, the adaptive label of any example is able to be changed by the subsequent reclassification. In this manner, any mislabeled training example is corrected by the classifiers during training. The training may use a probabilistic multiple category boosting algorithm that maintains probability data provided by the classifiers, or a winner-take-all multiple category boosting algorithm selects the adaptive label based upon the highest probability classification. The multiple category boosting training system may be coupled to a multiple instance learning mechanism to obtain the training examples. The trained classifiers may be used as weak classifiers that provide a label used to select a deep classifier for further classification, e.g., to provide a multi-view object detector. | 05-19-2011 |
