Abstract: Computationally efficient searching, browsing and retrieval of one or more objects in a video sequence are accomplished using learned generative models. The generative model is trained on an automatically or manually selected query sequence from a sequence of image frames. The resulting generative model is then used in searching, browsing or retrieval of one or more similar or dissimilar image frames or sequences within the image sequence by determining the likelihood of each frame under the learned generative model. Further, this method allows for automatic separation and balancing of various causes of variability while analyzing the image sequence. The generative models are based on appearances of multiple, possibly occluding objects in an image sequence. Further, the search strategies used include clustering and intelligent fast forward through the image sequence. Additionally, in one embodiment, a fast forward speed is relative to the current frame likelihood under the learned generative model.

Abstract: A technique for estimating the optical flow between images of a scene and a segmentation of the images is presented. This involves first establishing an initial segmentation of the images and an initial optical flow estimate for each segment of each images and its neighboring image or images. A refined optical flow estimate is computed for each segment of each image from the initial segmentation of that image and the initial optical flow of the segments of that image. Next, the segmentation of each image is refined from the last-computed optical flow estimates for each segment of the image. This process can continue in an iterative manner by further refining the optical flow estimates for the images using their respective last-computed segmentation, followed by further refining the segmentation of each image using their respective last-computed optical flow estimates, until a prescribed number of iterations have been completed.

Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.

Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.

Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.

Abstract: The subject invention leverages spectral “palettes” or representations of an input sequence to provide recognition and/or synthesizing of a class of data. The class can include, but is not limited to, individual events, distributions of events, and/or environments relating to the input sequence. The representations are compressed versions of the data that utilize a substantially smaller amount of system resources to store and/or manipulate. Segments of the palettes are employed to facilitate in reconstruction of an event occurring in the input sequence. This provides an efficient means to recognize events, even when they occur in complex environments. The palettes themselves are constructed or “trained” utilizing any number of data compression techniques such as, for example, epitomes, vector quantization, and/or Huffman codes and the like.

Abstract: Systems that facilitate immunogen design are described herein. An optimization component is provided to determine an immunogen according to at least one criterion. The immunogen comprises a set of overlapping sequences comprising sequences that are known to be and/or are likely to be immunogenic. At least one of the sequences that are likely to be immunogenic can be determined by analyzing associations between a host and a pathogen at a population level. Methods of determining an epitome are described herein. A plurality of sequences are received. At least one of the sequences is predicted to be an epitope based on a relationship between a diverse trait of a population and a mutation of a pathogen. A collection of the plurality of sequences is optimized according to one or more criteria to determine the epitome. Epitomes and immunogens determined by the systems and methods described herein are also contemplated.

Abstract: A system comprising a machine learning classifier trained on a plurality of associations between a host and a pathogen to predict a pathogen characteristic is described herein. The pathogen characteristic can relate to a disease state of the host. Computer-executable instructions for performing a method of forecasting a portion of a target molecule anticipated to influence an organism's condition also are described herein. The method comprises employing population data to automatically analyze one or more areas of the target molecule to determine the portion of the target molecule anticipated to influence the organism's condition. The population data can pertain to at least one relationship between at least one diverse organism trait and the target molecule. One or more epitopes forecast by employing the method also are contemplated.

Abstract: A “Video Browser” provides an intuitive user interface for indexing, and interactive visual browsing, of particular elements within a video recording. In general, the Video Browser operates by first generating a set of one or more mosaic images from the video recording. In one embodiment, these mosaics are further clustered using an adjustable similarity threshold. User selection of a particular video mosaic then initiates a playback of corresponding video frames. However, in contrast to conventional mosaicing schemes which simply play back the set of frames used to construct the mosaic, the Video Browser provides a playback of only those individual frames within which a particular point selected within the image mosaic was observed. Consequently, user selection of a point in one of the image mosaics serves to provide a targeted playback of only those frames of interest, rather than playing back the entire image sequence used to generate the mosaic.

Abstract: A “Video Browser” provides interactive browsing of unique events occurring within an overall video recording. In particular, the Video Browser processes the video to generate a set of video sprites representing unique events occurring within the overall period of the video. These unique events include, for example, motion events, security events, or other predefined event types, occurring within all or part of the total period covered by the video. Once the video has been processed to identify the sprites, the sprites are then arranged over a background image extracted from the video to create an interactive static video montage. The interactive video montage illustrates all events occurring within the video in a single static frame. User selection of sprites within the montage causes either playback of a portion of the video in which the selected sprites were identified, or concurrent playback of the selected sprites within a dynamic video montage.

Abstract: A system and method to facilitate pattern recognition or matching between patterns are disclosed that is substantially invariant to small transformations. A substantially smooth deformation field is applied to a derivative of a first pattern and a resulting deformation component is added to the first pattern to derive a first deformed pattern. An indication of similarity between the first pattern and a second pattern may be determined by minimizing the distance between the first deformed pattern and the second pattern with respect to deformation coefficients associated with each deformed pattern. The foregoing minimization provides a system (e.g., linear) that may be solved with standard methods.

Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly which optimize an optimization criterion, via machine learning algorithms, e.g., a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection.

Abstract: A system and method to facilitate pattern recognition or matching between patterns are disclosed that is substantially invariant to small transformations. A substantially smooth deformation field is applied to a derivative of a first pattern and a resulting deformation component is added to the first pattern to derive a first deformed pattern. An indication of similarity between the first pattern and a second pattern may be determined by minimizing the distance between the first deformed pattern and the second pattern with respect to deformation coefficients associated with each deformed pattern. The foregoing minimization provides a system (e.g., linear) that may be solved with standard methods.

Abstract: A system and method facilitating object tracking is provided. The system includes an audio model that receives at least two audio input signals and a video model that receives a video input. The audio model and the video model employ probabilistic generative models which are combined to facilitate object tracking. Expectation maximization can be employed to modify trainable parameters of the audio model and the video model.

Abstract: A system and method facilitating object tracking is provided. The invention includes an audio model that receives at least two audio input signals and a video model that receives a video input. The audio model and the video model employ probabilistic generative models which are combined to facilitate object tracking. Expectation maximization can be employed to modify trainable parameters of the audio model and the video model.

Abstract: A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.

Abstract: A system and method facilitating speech detection and/or enhancement utilizing audio/video fusion is provided. The present invention fuses audio and video in a probabilistic generative model that implements cross-model, self-supervised learning, enabling rapid adaptation to audio visual data. The system can learn to detect and enhance speech in noise given only a short (e.g., 30 second) sequence of audio-visual data. In addition, it automatically learns to track the lips as they move around in the video.

Abstract: A system and method to facilitate pattern recognition or matching between patterns are disclosed that is substantially invariant to small transformations. A substantially smooth deformation field is applied to a derivative of a first pattern and a resulting deformation component is added to the first pattern to derive a first deformed pattern. An indication of similarity between the first pattern and a second pattern may be determined by minimizing the distance between the first deformed pattern and the second pattern with respect to deformation coefficients associated with each deformed pattern. The foregoing minimization provides a system (e.g., linear) that may be solved with standard methods.

Abstract: A user interface (UI) for adaptive video fast forward provides a novel fully adaptive content-based UI for allowing user interaction with an image sequence or video relative to a user identified query sample. This query sample is drawn either from an image sequence being searched or from another image sequence entirely. The user interaction offered by the UI includes providing a user with computationally efficient searching, browsing and retrieval of one or more objects, frames or sequences of interest in video or image sequences, as well as automatic content-based variable-speed playback based on a computed similarity to the query sample. In addition, the UI also provides the capability to search for image frames or sequences that are dissimilar to the query sample, thereby allowing the user to quickly locate unusual or different activity within an image sequence.

Abstract: Computationally efficient searching, browsing and retrieval of one or more objects in a video sequence are accomplished using learned generative models. The generative model is trained on an automatically or manually selected query sequence from a sequence of image frames. The resulting generative model is then used in searching, browsing or retrieval of one or more similar or dissimilar image frames or sequences within the image sequence by determining the likelihood of each frame under the learned generative model. Further, this method allows for automatic separation and balancing of various causes of variability while analyzing the image sequence. The generative models are based on appearances of multiple, possibly occluding objects in an image sequence. Further, the search strategies used include clustering and intelligent fast forward through the image sequence. Additionally, in one embodiment, a fast forward speed is relative to the current frame likelihood under the learned generative model.