14448568
Sampling Representative Examples for Dimensionality Reduction and Recognition - Bootstrap Bumping LDA
We present a novel method for dimensionality reduction and recognition based on Linear Discriminant Analysis (LDA), which specifically deals with the Small Sample Size (SSS) problem in Computer Vision applications. Unlike the traditional methods, which impose specific assumptions to address the SSS problem, our approach introduces a variant of bootstrap bumping technique, which is a general framework in statistics for model search and inference. An intermediate linear representation is first hypothesized from each bootstrap sample. Then LDA is performed in the reduced subspace. Lastly, the final model is selected among all hypotheses for the best classification. Experiments on synthetic and real datasets demonstrate the advantages of our Bootstrap Bumping LDA (BB-LDA) approach over the traditional LDA based methods.
18888326
Analysis and recognition of walking movements
We present an approach for recognizing human walking movements using low-level motion regularities and constraints. Biomechanical features for classification are automatically extracted from video sequences of walkers. A multiplicative classification rule using statistical distances is then used to determine whether an unknown motion is consistent with normal walking patterns. Recognition results are shown distinguishing walking examples across multiple speeds from other nonwalking locomotions.
18551439
An adaptive focus-of-attention model for video surveillance and monitoring
In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.
11066432
Perceptual user interfaces: the KidsRoom
T he KidsRoom is a fully automated and interactive narrative playspace for children developed at the MIT Media Laboratory. Built to explore the design of perceptually based interactive interfaces, the Kids-Room uses computer vision action recognition simultaneously with computerized control of images, video, light, music, sound, and narration to guide children through a storybook adventure. Unlike most previous work in interactive environments, the Kids-Room does not require people in the space to wear any special clothing or hardware, and the KidsRoom can accommodate up to four people simultaneously. The system was designed to use computational perception to keep most interaction in the real, physical space even as participants interacted with virtual characters and scenes. The KidsRoom, designed in the spirit of several popular children's books, is an interactive child's bedroom that stimulates imagination by responding to actions with images and sound to transform itself into a storybook world. Two of the bedroom walls resemble the real walls in a child's room, complete with real furniture, posters, and windows. The other two walls are large, back-projected video screens used to transform the appearance of the room environment. Four speakers and one amplifier project steerable sound effects, music, and narration into the space. Three video cameras overlooking the space provide input to computer vision people-tracking and action recognition algorithms. Computer-controlled theatrical lighting illuminates the space, and a microphone detects the volume of enthusiastic screams. The room is fully automated. During the story, children interact with objects in the room, with one another, and with virtual creatures projected onto the walls. Perceptual recognition makes it possible for the room to respond to the physical actions of the children by appropriately moving the story forward thereby creating a compelling interactive narrative experience. Conversely, the narrative context of the story makes it easier to develop context-dependent (and therefore more robust) action recognition algorithms. The story developed for the KidsRoom begins with a normal-looking bedroom. Children enter after being told to find out the magic word by asking the talking furniture that speaks when approached. When the children scream the magic word loudly, sounds and images transform the room into a mystical forest. The story narration prods the children to stay in a group and follow a path to a river (see the stone path (a) in the figure). Along the way, they encounter roaring monsters and must hide behind the bed to make the roars …
16142452
Differential video coding of face and gesture events in presentation videos
Currently, bandwidth limitations pose a major challenge for delivering high-quality multimedia information over the Internet to users. In this research, we aim to provide a better compression of presentation videos (e.g., lectures). The approach is based on the idea that people tend to pay more attention to the face and gesturing hands, and therefore these regions are given more resolution than the remaining image. Our method first detects and tracks the face and hand regions using color-based segmentation and Kalman filtering. Next, different classes of natural hand gesture are recognized from the hand trajectories by identifying gesture holds, position/velocity changes, and repetitive movements. The detected face/ hand regions and gesture events in the video are then encoded at higher resolution than the remaining lower-resolution background. We present results of the tracking and gesture recognition approach, and evaluate and compare videos compressed with the proposed method to uniform compression.
14802063
Minimal-latency human action recognition using reliable-inference
We present a probabilistic reliable-inference framework to address the issue of rapid detection of human actions with low error rates. The approach determines the shortest video exposures needed for low-latency recognition by sequentially evaluating a series of posterior ratios for different action classes. If a subsequence is deemed unreliable or confusing, additional video frames are incorporated until a reliable classification to a particular action can be made. Results are presented for multiple action classes and subsequence durations, and are compared to alternative probabilistic approaches. The framework provides a means to accurately classify human actions using the least amount of temporal information.
9694396
Assigning Intonational Features in Synthesized Spoken Directions
Speakers convey much of the information hearers use to interpret discourse by varying prosodic features such as PHRASING, PITCH ACCENT placement, TUNE, and PITCH RANGE. The ability to emulate such variation is crucial to effective (synthetic) speech generation. While text-to-speech synthesis must rely primarily upon structural information to determine appropriate intonational features, speech synthesized from an abstract representation of the message to be conveyed may employ much richer sources. The implementation of an intonation assignment component for Direction Assistance, a program which generates spoken directions, provides a first approximation of how recent models of discourse structure can be used to control intonational variation in ways that build upon recent research in intonational meaning. The implementation further suggests ways in which these discourse models might be augmented to permit the assignment of appropriate intonational features.
37350008
On Edge-Disjoint Spanning Trees in Hypercubes
In this note we give a construction for obtaining the maximum number of edge-disjoint spanning trees in a hypercube. The result has applications to multicast communication in wormhole-routed parallel computers.
16145112
Mosaics of scenes with moving objects
Image mosaics are useful for a variety of tasks in vision and computer graphics. A particularly convenient way to generate mosaics is by 'stitching' together many ordinary photographs. Existing algorithms focus on capturing static scenes. This paper presents a complete system for creating visually pleasing mosaics in the presence of moving objects. There are three primary contributions. The first component of our system is a registration method that remains unbiased by movement-the Mellin transform is extended to register images related by a projective transform. Second an efficient method for finding a globally consistent registration of all images is developed. By solving a linear system of equations, derived from many pairwise registration matrices, we find an optimal global registration. Lastly, a new method of compositing images is presented. Blurred areas due to moving objects are avoided by segmenting the mosaic into disjoint regions and sampling pixels in each region from a single source image.
14449649
The RASE (Rapid, Accurate Simulation Environment) for chip multiprocessors
We present RASE, a full system high performance simulation methodology for simulating complex server applications and server class chip multiprocessors enabled with fine-grain multithreading (CMTs). RASE combines application knowledge, operating system information, and data access patterns with an instruction stream from a highly-tuned, scalable steady-state benchmark [5] [22] to generate multiple representative instruction streams that can be mapped to a variety of CMT configurations. We use execution-driven simulation to generate instruction streams for M processors and store them as instruction trace files (several billion instructions per processor) that can be post-processed and augmented for larger than M processor system simulation. We use SPEC JBB2000, TPC-C, and an XML server benchmark to compare the performance estimates of RASE to a reference prototype CMT system. By varying M, we find that our trace-driven simulation methodology predicts within 5% of the instructions per cycle (IPC) of the reference hardware for the applications. Without post-processing the traces, in the best cases, the performance prediction accuracy degrades to 20-40% of the real IPC for instruction traces that require a high replication factor.
7180811
Calibrating pan-tilt cameras in wide-area surveillance networks
Pan-tilt cameras are often used as components of wide-area surveillance systems. It is necessary to calibrate these cameras in relation to one another in order to obtain a consistent representation of the entire space. Existing methods for calibrating pan-tilt cameras have assumed an idealized model of camera mechanics. In addition, most methods have been calibrated using only a small range of camera motion. We present a method for calibrating pan-tilt cameras that introduces a more complete model of camera motion. Pan and tilt rotations are modeled as occurring around arbitrary axes in space. In addition, the wide area surveillance system itself is used to build a large virtual calibration object, resulting in better calibration than would be possible with a single small calibration target. Finally, the proposed enhancements are validated experimentally, with comparisons showing the improvement provided over more traditional methods.