Louis Vuurpijl

The brain–computer interface cycle

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

An overview and comparison of voting methods for pattern recognition

In pattern recognition, there is a growing use of multiple classifier combinations with the goal to increase recognition performance. In many cases, plurality voting is a part of the combination process. In this article, we discuss and test several well known voting methods from politics and economics on classifier combination in order to see if an alternative to the simple plurality vote exists. We found that, assuming a number of prerequisites, better methods are available, that are comparatively simple and fast.

Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries

A method for image-based queries and search is proposed which is based on the generation of object outlines in images by using the pen, e.g., on color pen computers. The rationale of the approach is based on a survey on user needs, as well as on considerations from the point of view of pattern recognition and machine learning. By exploiting the actual presence of the human users with their perceptual-motor abilities and by storing textually annotated queries, an incrementally learning image retrieval system can be developed. As an initial test domain, sets of photographs of motor bicycles were used. Classification performances are given for outline and bitmap-derived feature sets, based on nearest-neighbour matching, with promising results. The benefit of the approach will be a user-based multimodal annotation of an image database, yielding a gradual improvement in precision and recall over time.

Finding structure in diversity: a hierarchical clustering method for the categorization of allographs in handwriting

The paper introduces a variant of agglomerative hierarchical clustering techniques. The new technique is used for categorizing character shapes (allographs) in large data sets of handwriting into a hierarchical structure. Such a technique may be used as the basis for a systematic naming scheme of character shapes. Problems with existing methods are described and the proposed method is explained. After application of the method to a very large set of characters, separately for all the letters of the alphabet, relevant clusters are identified and given a unique name. Each cluster represents an allograph prototype.

Iconic and multi-stroke gesture recognition

Many handwritten gestures, characters, and symbols comprise multiple pendown strokes separated by penup strokes. In this paper, a large number of features known from the literature are explored for the recognition of such multi-stroke gestures. Features are computed from a global gesture shape. From its constituent strokes, the mean and standard deviation of each feature are computed. We show that using these new stroke-based features, significant improvements in classification accuracy can be obtained between 10% and 50% compared to global feature representations. These results are consistent over four different databases, containing iconic pen gestures, handwritten symbols, and upper-case characters. Compared to two other multi-stroke recognition techniques, improvements between 25% and 39% are achieved, averaged over all four databases.

ICDAR 2009 Signature Verification Competition

Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily casework is performed through visual examination by trained forensic handwriting experts, without reliance on computer-assisted methods. With this competition on on- and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85\% Equal Error Rate (EER) on the online data and 9.15\% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.

Architectures for detecting and solving conflicts: two-stage classification and support vector classifiers

Abstract.In the majority of cases, a properly trained classifier or ensemble of classifiers may yield acceptable recognition results. However, in some cases, recognition will fail due to typical conflicts that are encountered, like the confusion between [A] and [H] or [U] and [V]. In this paper, two architectures for the recognition of handwritten text are described. The key issue for each of these systems is to detect the event of a possible conflict and subsequently attempt to solve that particular problem. Both systems exploit a two-stage classification method. In the event that the first-stage classifiers are not certain about the result, the second-stage system engages a set of support vector classifiers for refining the output hypothesis.

Dynamic time warping applied to Tamil character recognition

This paper describes the use of dynamic time warping (DTW) for classifying handwritten Tamil characters. Since DTW can match characters of arbitrary length, it is particularly suited for this domain. We built a prototype based classifier that uses DTW both for generating prototypes and for calculating a list of nearest prototypes. Prototypes were automatically generated and selected. Two tests were performed to measure the performance of our classifier in a writer dependent, and in a writer independent setting. Furthermore, several strategies were developed for rejecting uncertain cases. Two different rejection variables were implemented and using a Monte Carlo simulation, the performance of the system was tested in various configurations. The results are promising and show that the classifier can be of use in both writer dependent and writer independent automatic recognition of handwritten Tamil characters.

Mode detection in on-line pen drawing and handwriting recognition

On-line pen input benefits greatly from mode detection when the user is in a free writing situation, where he is allowed to write, to draw, and to generate gestures. Mode detection is performed before recognition to restrict the classes that a classifier has to consider, thereby increasing the performance of the overall recognition. In this paper we present a hybrid system which is able to achieve a mode detection performance of 95.6% on seven classes; handwriting, lines, arrows, ellipses, rectangles, triangles, and diamonds. The system consists of three kNN classifiers which use global and structural features of the pen trajectory and a fitting algorithm for verifying the different geometrical objects. Results are presented on a significant amount of data, acquired in different contexts like scribble matching and design applications.

Vind(x): using the user through cooperative annotation

In this paper, the image retrieval system Vind(x) is described. The architecture of the system and first user-experiences are reported. Using Vind(x), users on the Internet may cooperatively annotate objects in paintings by use of the pen or mouse. The collected data can be searched through query-by-drawing techniques, but can also serve as an (ever-growing) training and benchmark set for the development of automated image retrieval systems of the future. Several other examples of cooperative annotation are presented in order to underline the importance of this concept for the design of pattern recognition systems and the labeling of large quantities of scanned documents or online data.