William J. Christmas

Structural matching in computer vision using probabilistic relaxation

In this paper, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply, We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations. >

Fast robust correlation

A new, fast, statistically robust, exhaustive, translational image-matching technique is presented: fast robust correlation. Existing methods are either slow or non-robust, or rely on optimization. Fast robust correlation works by expressing a robust matching surface as a series of correlations. Speed is obtained by computing correlations in the frequency domain. Computational cost is analyzed and the method is shown to be fast. Speed is comparable to conventional correlation and, for large images, thousands of times faster than direct robust matching. Three experiments demonstrate the advantage of the technique over standard correlation.

Video Shot Cut Detection Using Adaptive Thresholding

The performance of shot detection methods in video sequences can be improved by the use of a threshold that adapts itself to the sequence statistics. In this paper we present some new techniques for adapting the threshold. We then compare the new techniques with an existing one, leading to an improved shot detection method.

A Tennis Ball Tracking Algorithm for Automatic Annotation of Tennis Match

Several tennis ball tracking algorithms have been reported in the literature. However, most of them use high quality video and multiple cameras, and the emphasis has been on coordinating the cameras, or visualising the tracking results. In this paper, we propose a tennis ball tracking algorithm for low quality off-air video recorded with a single camera. Multiple visual cues are exploited for tennis candidate detection. A particle filter with improved sampling efficiency is used to track the tennis candidates. Experimental results show that our algorithm is robust and has a tracking accuracy that is sufficiently high for automatic annotation of tennis matches.

Random Cascaded-Regression Copse for Robust Facial Landmark Detection

In this letter, we present a random cascaded-regression copse (R-CR-C) for robust facial landmark detection. Its key innovations include a new parallel cascade structure design, and an adaptive scheme for scale-invariant shape update and local feature extraction. Evaluation on two challenging benchmarks shows the superiority of the proposed algorithm to state-of-the-art methods.

Local Ordinal Contrast Pattern Histograms for Spatiotemporal, Lip-Based Speaker Authentication

Lip region deformation during speech contains biometric information and is termed visual speech. This biometric information can be interpreted as being genetic or behavioral depending on whether static or dynamic features are extracted. In this paper, we use a texture descriptor called local ordinal contrast pattern (LOCP) with a dynamic texture representation called three orthogonal planes to represent both the appearance and dynamics features observed in visual speech. This feature representation, when used in standard speaker verification engines, is shown to improve the performance of the lip-biometric trait compared to the state-of-the-art. The best baseline state-of-the-art performance was a half total error rate (HTER) of 13.35% for the XM2VTS database. We obtained HTER of less than 1%. The resilience of the LOCP texture descriptor to random image noise is also investigated. Finally, the effect of the amount of video information on speaker verification performance suggests that with the proposed approach, speaker identity can be verified with a much shorter biometric trait record than the length normally required for voice-based biometrics. In summary, the performance obtained is remarkable and suggests that there is enough discriminative information in the mouth-region to enable its use as a primary biometric trait.

An evaluation of bags-of-words and spatio-temporal shapes for action recognition

Bags-of-visual-Words (BoW) and Spatio-Temporal Shapes (STS) are two very popular approaches for action recognition from video. The former (BoW) is an un-structured global representation of videos which is built using a large set of local features. The latter (STS) uses a single feature located on a region of interest (where the actor is) in the video. Despite the popularity of these methods, no comparison between them has been done. Also, given that BoW and STS differ intrinsically in terms of context inclusion and globality/locality of operation, an appropriate evaluation framework has to be designed carefully. This paper compares these two approaches using four different datasets with varied degree of space-time specificity of the actions and varied relevance of the contextual background. We use the same local feature extraction method and the same classifier for both approaches. Further to BoW and STS, we also evaluated novel variations of BoW constrained in time or space. We observe that the STS approach leads to better results in all datasets whose background is of little relevance to action classification.

Probabilistic relaxation for matching problems in computer vision

The authors present the theory of probabilistic relaxation for matching symbolic structures, derive as limiting cases the various heuristic formulas used by researchers in matching problems, and state the conditions under which they apply. They successfully apply the theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations.<<ETX>>

Periodic human motion description for sports video databases

Many different visual features can be used for analysis and annotation of sports video material. Here, we present a periodic motion feature descriptor that can discriminate between different sports types that contain periodic motion. The experimental results, using video material from the 1992 Barcelona Olympic Games, show that the proposed periodic motion descriptor can successfully classify four sports types: sprint, long-distance running, hurdling and canoeing.

A Study on Automatic Shot Change Detection

We present a study of various automatic shot change detection methods for video segmentation which have been proposed in the literature. We identify representatives of the main approaches to the problem of shot cut detection and compare them experimentally on a large number of sequences. We discuss the relative merits of various algorithms and their uses.