Marco Block

Sign language recognition using kinect

An open source framework for general gesture recognition is presented and tested with isolated signs of sign language. Other than common systems for sign language recognition, this framework makes use of Kinect, a depth camera which makes real-time 3D-reconstruction easily applicable. Recognition is done using hidden Markov models with a continuous observation density. The framework also offers an easy way of initializing and training new gestures or signs by performing them several times in front of the camera. First results with a recognition rate of 97% show that depth cameras are well-suited for sign language recognition.

Local Contrast Segmentation to Binarize Images

In this paper, a new binarization algorithm for degraded document images is proposed. The method is based on positive and negative pixel energies using the Laplacian ofan image. After a filtering step and morphological perationsour local contrast segmentation method is able to detectingconnected components. The given approach is applied to casesof sophisticated, challenging documents and other application scenarios like whiteboard and chalk images.

Quantile Linear Algorithm for Robust Binarization of Digitalized Letters

We describe a threshold-based local algorithm for image binarization. The main idea is to compute a transition energy using pixel value differences taken from a neighborhood around the pixel of interest. By filtering the pixels with low positive and negative energy, we keep two subsets in the neighborhood, corresponding to higher positive and negative energy values. The binarization threshold is calculated using a statistical model of the high energy pixels. Experiments show that this new approach is faster and better than current state-of-the-art algorithms.

Robust Document Warping with Interpolated Vector Fields

This paper describes a new versatile algorithm for correcting nonlinear distortions, such as curvature of book pages, in camera based document processing. We introduce the idea of using local orientation features to interpolate a vector field from which a warping mesh is derived. Ultimately, the image is corrected by approximating the nonlinear distortion with multiple linear projections. Since the algorithm does not derive the mesh directly from text baselines it is robust over arbitrarily complex text layouts. We describe a baseline detector for extracting the required local orientation features. We also sketch a method for correcting nonlinear distortions of a documents vertical axis with our algorithm.

Multi-Exposure Document Fusion Based on Edge-Intensities

This paper presents a new algorithm for fusioning images of text-documents taken with different exposures.It is compared to several standard block oriented exposure- and focus-blending-algorithms.The recognition rate of a publicly available OCR-engine is used as a benchmark to quantify the results.Experiments show in average an improvement in the recognition rate from 0.46 to 0.64 by employing exposure blending as preprocessing step to an OCR. The presented algorithm of blending high-pass filtered images instead of original images further increases the recognition rate to 0.95.

Object recognition using summed features classifier

A common task in the field of document digitization for information retrieval is separating text and non-text elements. In this paper an innovative approach of recognizing patterns is presented. Statistical and structural features in arbitrary number are combined into a rating tree, which is an adapted decision tree. Such a tree is trained for character patterns to distinguish text elements from non-text elements. First experiments in a binarization application have shown promising results in significant reduction of false-positives without producing false-negatives.