Boundary Fragment Matching and Articulated Pose Under Occlusion

Silhouette recognition can reconstruct the three-dimensional pose of a human subject in monocular video so long as the camera’s view remains unoccluded by other objects. This paper develops a shape representation that can describe and compare partial shapes, extending the silhouette recognition technique to apply to video with occlusions. The new method operates without human intervention, and experiments demonstrate that it can reconstruct accurate three-dimensional articulated pose tracks from single-camera walking video despite occlusion of one-third to one-half of the subject

Recognition-Based Motion Capture and the HumanEva II Test Data

Quantitative comparison of algorithms for human motion capture have been hindered by the lack of standard benchmarks. The development of the HumanEva I & II test sets provides an opportunity to assess the state of the art by evaluating existing methods on the new standardized test videos. This paper presents a comprehensive evaluation of a monocular recognition-based pose recovery algorithm on the HumanEva II clips. The results show that the method achieves a mean relative error of around 10-12 cm per joint

Data as Ensembles of Records: Representation and Comparison

Many collections of data do not come packaged in a form amenable to the ready application of machine learning techniques. Nevertheless, there has been only limited research on the problem of preparing raw data for learning, perhaps because widespread differences between domains make generalization difficult. This paper focuses on one common class of raw data, in which the entities of interest actually comprise collections of (smaller pieces of) homologous data. We present a technique for processing such collections into high-dimensional vectors, suitable for the application of many learning algorithms including clustering, nearestneighbors, and boosting. We demonstrate the abilities of the method by using it to implement similarity metrics on two different domains: natural images and measurements from ocean buoys in the Pacific

Boosted Image Classification: An Empirical Study

The rapid pace of research in the fields of machine learning and image comparison has produced powerful new techniques in both areas. At the same time, research has been sparse on applying the best ideas from both fields to image classification and other forms of pattern recognition. This paper combines boosting with stateof-the-art methods in image comparison to carry out a comparative evaluation of several top algorithms. The results suggest that a new method for applying boosting may be most effective on data with many dimensions. Effectively marrying the best ideas from the two fields takes effort, but the techniques and analyses developed herein make the task straightforward

Flow Lookup and Biological Motion Perception

Optical flow in monocular video can serve as a key for recognizing and tracking the three-dimensional pose of human subjects. In comparison with prior work using silhouettes as a key for pose lookup, flow data contains richer information and in experiments can successfully track more difficult sequences. Furthermore, flow recognition is powerful enough to model human abilities in perceiving biological motion from sparse input. The experiments described herein show that a tracker using flow moment lookup can reconstruct a common biological motion (walking) from images containing only point light sources attached to the joints of the moving subject

Inkball Models for Character Localization and Out-of-Vocabulary Word Spotting

Inkball models have previously been used for keyword spotting under the whole word query-by-image paradigm. This paper applies inkball methods to string-based queries for the first time, using synthetic models composed from individual characters. A hybrid system using both query-by-string for unknown words and query-by-example for known words outperforms either approach by itself on the George Washington and Parzival test sets. In addition, inkball character models offer an explanatory tool for understanding handwritten markings. In combination with a transcript they can help to to attribute each ink pixel of a word image to specific letters, resulting in highquality character segmentations

Style-Based Retrieval for Ancient Syriac Manuscripts

Thousands of documents written in Syriac script by early Christian theologians are of unknown provenance and uncertain date, partly due to a shortage of human expertise. This paper addresses the problem of attribution by developing a novel algorithm for offline handwriting style identification and document retrieval, demonstrated on a set of documents in the Estrangelo variant of Syriac writing. The method employs a feature vector based upon the estimated affine transformation of actual observed characters, character parts, and voids within characters as compared to a hypothetical average or ideal form. Experiments on seventy-six pages from nineteen Syriac manuscripts written by different scribes show that the method can identify pages written in the same hand with high precision, even with documents that exhibit various challenging forms of degradation

Chronological Profiling for Paleography

This paper approaches manuscript dating from a Bayesian perspective. Prior work on paleographic date recovery has generally sought to predict a single date for a manuscript. Bayesian analysis makes it possible to estimate a probability distribution that varies with respect to time. This in turn enables a number of alternative analyses that may be of more use to practitioners. For example, it may be useful to identify a range of years that will include a document’s creation date with a particular confidence level. The methods are demonstrated on a selection of Syriac documents created prior to 1300 CE