Figure Text Extraction in Biomedical Literature

Background: Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engin

On-line learning of unknown hand held objects via tracking

For many computer vision applications labeled/segmented data is needed. Manually assigning labels or segmenting images is a time consuming and tedious task and becomes infeasible for a huge amount of data (e.g., when analyzing a video stream). Thus, this paper proposes a new approach to minimize the manual labeling/segmentation effort for learning an object detector by automatically extracting training data directly from a video sequence. Therefore, a robust background model, a tracker and an on-line learning method are combined. The main idea is to track an object through a video sequence and to directly use the obtained image patches, showing the object from different views, to incrementally update an existing model which in turn can be used for detection. As the tracker is initialized automatically by change detection, no user interaction is needed! Thus, an unknown object can be learned without having any prior information. To show the benefit of the proposed approach the framework is demonstrated on several typical objects that can be found on a desktop.

Using Partial Edge Contour Matches for Efficient Object Category Localization

Abstract. We propose a method for object category localization by partially matching edge contours to a single shape prototype of the category. Previous work in this area either relies on piecewise contour approximations, requires meaningful supervised decompositions, or matches coarse shape-based descriptions at local interest points. Our method avoids error-prone pre-processing steps by using all obtained edges in a partial contour matching setting. The matched fragments are efficiently summarized and aggregated to form location hypotheses. The efficiency and accuracy of our edge fragment based voting step yields high quality hypotheses in low computation time. The experimental evaluation achieves excellent performance in the hypotheses voting stage and yields competitive results on challenging datasets like ETHZ and INRIA horses.

Optimizing 1-Nearest Prototype Classifiers

The development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision. However, the trend towards large scale datasets revived the interest in simpler classifiers to reduce runtime. Simple nearest neighbor classifiers have several beneficial properties, such as low complexity and inherent multi-class handling, however, they have a runtime linear in the size of the database. Recent related work represents data samples by assigning them to a set of prototypes that partition the input feature space and afterwards applies linear classifiers on top of this representation to approximate decision boundaries locally linear. In this paper, we go a step beyond these approaches and purely focus on 1-nearest prototype classification, where we propose a novel algorithm for deriving optimal prototypes in a discriminative manner from the training samples. Our method is implicitly multi-class capable, parameter free, avoids noise overfitting and, since during testing only comparisons to the derived prototypes are required, highly efficient. Experiments demonstrate that we are able to outperform related locally linear methods, while even getting close to the results of more complex classifiers. 1

New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure

This paper presents a method for detection of salient objects from images. The proposed algorithms for image segmentation and objects detection use a hexagonal representation of the image pixels and a hypergraph structure to process this hierarchal structure. The main goal of the method is to obtain salient regions, which may be associated with semantic labels. The designed algorithms use color characteristic and syntactic features for image segmentation. The object-oriented model used for storing the results of the segmentation and detection allows directly annotation of regions without a processing of these. The experiments showed that the presented method is robust and accurate comparing with others public methods used for salient objects detection