Sebastian Zambanini

Markerless visual fingertip detection for natural mobile device interaction

The vision-based detection of hand gestures is one technological enabler for Natural User Interfaces which try to provide a natural and intuitive interaction with computers. In particular, mobile devices might benefit from such a less device-centric but more natural input possibility. In this paper, we introduce our ongoing work on the visual markerless detection of fingertips on mobile devices. Further, we shed light on the potential of mobile hand gesture detection and present several promising use cases and respective demo applications based on the presented engine.

Image based recognition of ancient coins

Illegal trade and theft of coins appears to be a major part of the illegal antiques market. Image based recognition of coins could substantially contribute to fight against it. Central component in the permanent identification and traceability of coins is the underlying classification and identification technology. However, currently available algorithms focus basically on the recognition of modern coins. To date, no optical recognition system for ancient coins has been researched successfully. In this paper, we give an overview on recent research for coin classification and we show if existing approaches can be extended from modern coins to ancient coins. Results of the algorithms implemented are presented for three different coins databases with more then 10.000 coins.

Introducing a statistical behavior model into camera-based fall detection

Camera based fall detection represents a solution to the problem of people falling down and being not able to stand up on their own again. For elderly people who live alone, such a fall is a major risk. In this paper we present an approach for fall detection based on multiple cameras supported by a statistical behavior model. The model describes the spatio-temporal unexpectedness of objects in a scene and is used to verify a fall detected by a semantic driven fall detection. In our work a fall is detected using multiple cameras where each of the camera inputs results in a separate fall confidence. These confidences are then combined into an overall decision and verified with the help of the statistical behavior model. This paper describes the fall detection approach as well as the verification step and shows results on 73 video sequences.

Identification of ancient coins based on fusion of shape and local features

We present a vision-based approach to ancient coins’ identification. The approach is a two-stage procedure. In the first stage an invariant shape description of the coin edge is computed and matching based on shape is performed. The second stage uses preselection by the first stage in order to refine the matching using local descriptors. Results for different descriptors and coin sides are combined using naive Bayesian fusion. Identification rates on a comprehensive data set of 2400 images of ancient coins are on the order of magnitude of 99%.

Automatic image-based assessment of lesion development during hemangioma follow-up examinations

OBJECTIVE This paper presents an automatic method for the quantification of the development of cutaneous hemangiomas in digital images. Two measurements on digital images acquired during follow-up examinations are performed: (1) the skin area affected by the lesion is measured and (2) the change of the hemangioma during follow-up examinations called regression is determined. Current manual measurements exhibit inter- and intra-reader variation, which impedes precision and comparisons across clinical studies. The proposed automatic method aims at a more accurate and objective evaluation of the course of disease than the current clinical practice of manual measurement. METHODS AND MATERIAL The proposed method classifies individual pixels and calculates the area based on a ruler attached to the skin. For the regression detection follow-up images are registered automatically based on local gradient histograms. The method was evaluated on 90 individual images and a set of 4 follow-up series consisting of 3-4 examinations. RESULTS The absolute average error of the individual area measurements lies at 0.0775cm(2) corresponding to a variation coefficient of 8.82%. The measurement of the regression area provides an absolute average error of 0.1134cm(2) and a variation coefficient of 7.40 %. CONCLUSIONS The results indicate that the proposed method provides an accurate and objective evaluation of the course of cutaneous hemangiomas. This is relevant for the monitoring of individual therapy and for clinical trials.

Coarse-to-Fine correspondence search for classifying ancient coins

In this paper, we build upon the idea of using robust dense correspondence estimation for exemplar-based image classification and adapt it to the problem of ancient coin classification. We thus account for the lack of available training data and demonstrate that the matching costs are a powerful dissimilarity metric to establish coin classification for training set sizes of one or two images per class. This is accomplished by using a flexible dense correspondence search which is highly insensitive to local spatial differences between coins of the same class and different coin rotations between images. Additionally, we introduce a coarse-to-fine classification scheme to decrease runtime which would be otherwise linear to the number of classes in the training set. For evaluation, a new dataset representing 60 coin classes of the Roman Republican period is used. The proposed system achieves a classification rate of 83.3 % and a runtime improvement of 93 % through the coarse-to-fine classification.

Detecting falls at homes using a network of low-resolution cameras

In a smart home system, a camera-based fall detector at elderly homes leads to immediate alarming and helping. In this paper we propose an approach for the detection of falls based on multiple cameras. Based on semantic driven features, fall detection is done in 3D and fuzzy logic is used to estimate confidence values for different human postures as well as for the incidence of a fall/no fall. Emphasis is given on simplicity, low computational effort and fast processing. Therefore, based on an evaluation on 73 test sequences, we show the applicability of the method for videos with low spatial resolution and frame rate.

Supporting Ancient Coin Classification by Image-Based Reverse Side Symbol Recognition

Coins and currency are studied in the field of Numismatics. Our aim in this article is to use the knowledge of Numismatics for the development of part of a framework for the visual classification of ancient coins. Symbols minted on the reverse side of these coins vary greatly in their shapes and visual structures. Due to this property of symbols, we propose to use them as a discriminative feature for the visual classification of ancient coins. We use dense sampling based bag of visual words (BoVWs) approach for our problem. Due to the fact that BoVWs lack the spatial information, we evaluate three types of schemes to incorporate spatial information. Other parameters of BoVWs such as the size of visual vocabulary, level of detail of the dense sampling grid and number of features per image to construct the visual vocabulary are also investigated.

A Local Image Descriptor Robust to Illumination Changes

In this paper we address the problem of building a local image descriptor that is insensitive to the complex appearance changes induced by illumination variations on non-flat objects. The presented descriptor is based on multi-scale and multi-oriented even Gabor filters and constructed in such a way that typical effects of illumination variations like changes of edge polarity or spatially varying brightness changes are taken into account for illumination insensitivity. For evaluation, a dataset of textured as well as textureless objects is used which introduces a greater challenge towards evaluating the robustness against illumination changes than conventional datasets used in the past. The experiments finally show the superiority of our descriptor compared to existing ones under illumination changes.

Word detection applied to images of ancient Roman coins

This paper presents a method for recognizing legends in images of ancient coins. It accounts for the special challenging conditions of ancient coins and thus does not rely on character segmentation contrary to traditional Optical Character Recognition (OCR) methods designed for text written on paper. Instead, characters are detected by means of individual character classifiers applied to a dense grid of local SIFT features. Final word recognition is accomplished using a lexicon of known legend words. For this purpose, the Pictorial Structures approach is adopted to find the most likely word occurrences based on the previously detected characters. Experiments are conducted on a set of 180 coin images from the Roman period with 35 different legend words. Depending on the lexicon size used, the achieved word detection rate varies from 29% to 53%.