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.
9994863
Motion segmentation and pose recognition with motion history gradients
Abstract. This paper presents a fast and simple method using a timed motion history image (tMHI) for representing motion from the gradients in successively layered silhouettes. This representation can be used to (a) determine the current pose of the object and (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead are motion regions that are naturally connected to parts of the moving object. This method may be used as a very general gesture recognition “toolbox”. We demonstrate the approach with recognition of waving and overhead clapping motions to control a music synthesis program.
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.
10341811
A Two-Stage Template Approach to Person Detection in Thermal Imagery
We present a two-stage template-based method to detect people in widely varying thermal imagery. The approach initially performs a fast screening procedure using a generalized template to locate potential person locations. Next an AdaBoosted ensemble classifier using automatically tuned filters is employed to test the hypothesized person locations. We demonstrate and evaluate the approach using a challenging dataset of thermal imagery
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 …
10174533
Cost-Sensitive Decision Tree Learning for Forensic Classification
In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the forensic classification problem and present a new algorithm for building decision trees that maximizes classification accuracy while minimizing total feature costs. By expressing the ID3 decision tree algorithm in an information theoretic context, we derive our algorithm from a well-formulated problem objective. We evaluate our algorithm across several datasets and show that, for a given level of accuracy, our algorithm builds cheaper trees than existing methods. Finally, we apply our algorithm to a real-world system, Clarify. Clarify classifies unknown or unexpected program errors by collecting statistics during program runtime which are then used for decision tree classification after an error has occurred. We demonstrate that if the classifier used by the Clarify system is trained with our algorithm, the computational overhead (equivalently, total feature costs) can decrease by many orders of magnitude with only a slight (<1%) reduction in classification accuracy.
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.
2294366
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces
We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not need markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. The algorithm registers two meshes by optimizing a joint probabilistic model over all point-to-point correspondences between them. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface deformation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models.
17316572
Aligning Ontologies through Formal Concept Analysis
Abstract.Ontologies have been developed for a number of knowledge domains as di-verseasclinicalterminology,photocamerapartsandmicro-arraygeneexpressiondata.However,aninnatecharacteristicofthedevelopmentofontologiesisthattheyareoftencreatedbyindependentgroupsofexpertise,whichgeneratedthenecessityofmergingandaligningontologiescoveringoverlappingdomains.Manyalgorithmsandtoolshavebeenproposedformergingofontologies,butmostofthemdisregardthestructuralpropertiesofthesourceontologies,focusingmostlyonsyntacticanalysis.This article focuses on an alignment method for ontologies based on Formal ConceptAnalysis,adataanalysistechniquefoundedonlatticetheory. 1 Introduction IntheheartofSemanticWebinitiative,proposedbyTimBerners-lee[2,3],liestheideaofmachineprocessableinformation,suchthatautomatedagents,orsoftbots,couldscanthewebspaceforinformationwithaspeciﬂcmeaning.Nevertheless,theunderstandingofmeaninginWebpagesdesignedforhumansisnotaneasytaskforthepresentgenerationofsoftbotsandontologiescangreatlyassistinthisendeavour.Manyinitiativesaroundtheworldhavecreatedontologies.Someoftheseontologieswerecreatedforhighleveldescriptionoftheworld,whereasothersrepresentveryspeciﬂcsub-jects,likebreastcancer,forinstance.Sometimes,ontologiesdevelopedindependentlymayhavesomeoverlaps.TheforementionedBreastCancerOntology,forexample,mayhavecommonconceptsandrelationswithaGeneOntology.Theseoverlapsareveryimpor-tantfromthepointofviewofinformationintegration,becausethecommonontologicalentitieshavetobeidentiﬂedbeforeinformationcanbesuccessfullyintegrated.Forthisreason,mergeandalignmentofontologieshavereceivedsigniﬂcantattentionfromtheresearchcommunityinthelastsixyears.Theﬂrstimportantissueinmerge/alignmentofontologiesistherelationsymbol-meaning.Ontologiesareconstitutedofsymbols(entities)andrelationsbetweensymbols.However,inSemiotics[18]symbolshaveassociatedmeaning,whichlinksthemtotheirreferent
14742091
On local pruning of association rules using directed hypergraphs
Here we propose an adaptive local pruning method for association rules. Our method exploits the exact mapping between a certain class of association rules, namely those whose consequents are singletons and backward directed hypergraphs (B-graphs). The hypergraph which represents the association rules is called an association rules network(ARN). Here we present a simple example of an ARN. We further prove several properties of the ARN and apply the results of our approach to two popular data sets.