Michel Grimaldi

Writer identification based on the fractal construction of a reference base

Our aim is to achieve writer identification processthanks to a fractal analysis of handwriting style. For eachwriter, a set of characteristics is extracted. They arespecific to the writer. Advantage is taken from theautosimilarity properties that are present in oneshandwriting. In order to do that, some invariant patternscharacterizing the writing are extracted. During thetraining step these invariant patterns appear along afractal compression process, then they are organized in areference base that can be associated with the writer.This base allows to analyze an unknown writing thewriter of which has to be identified. A Pattern Matchingprocess is performed using all the reference basessuccessively. The results of this analyze are estimatedthrough the signal to noise ratio. Thus, the signal to noiseratio according to a set of bases identifies the unknowntexts writer.

An ultrasonic contactless sensor for breathing monitoring.

The monitoring of human breathing activity during a long period has multiple fundamental applications in medicine. In breathing sleep disorders such as apnea, the diagnosis is based on events during which the person stops breathing for several periods during sleep. In polysomnography, the standard for sleep disordered breathing analysis, chest movement and airflow are used to monitor the respiratory activity. However, this method has serious drawbacks. Indeed, as the subject should sleep overnight in a laboratory and because of sensors being in direct contact with him, artifacts modifying sleep quality are often observed. This work investigates an analysis of the viability of an ultrasonic device to quantify the breathing activity, without contact and without any perception by the subject. Based on a low power ultrasonic active source and transducer, the device measures the frequency shift produced by the velocity difference between the exhaled air flow and the ambient environment, i.e., the Doppler effect. After acquisition and digitization, a specific signal processing is applied to separate the effects of breath from those due to subject movements from the Doppler signal. The distance between the source and the sensor, about 50 cm, and the use of ultrasound frequency well above audible frequencies, 40 kHz, allow monitoring the breathing activity without any perception by the subject, and therefore without any modification of the sleep quality which is very important for sleep disorders diagnostic applications. This work is patented (patent pending 2013-7-31 number FR.13/57569).

A New Method for Detailed Electric Consumption of Domestic Appliances

Electric consumption constitutes an important part of the operational expenses of housekeeping. Through the meter, the distributors of electricity supply a global information of this consumption but, this day, there are no simple means to obtain the detail of its consumption, device by device.

Object segmentation from a dynamic background using a pixelwise rigidity criterion and application to maritime target recognition

This paper describes a novel approach for rigid object segmentation from a dynamic background using a pre-recorded video with a moving camera, and we apply it to the problem of vessel segmentation in a maritime video scene. The difficulty of modeling background appearance or/and dynamic, modeling object appearance, and compensating camera motion renders this task very challenging. Therefore, the proposed method only uses a geometric constraint regarding target motion rigidity in order to achieve object segmentation in a full video segment. Such an idea is not new in object segmentation literature, but the novelty in this paper resides in that the target rigidity assumption is implemented at the pixel level, but not at the object scale. This is firstly achieved by deriving a theoretical optic flow model in the neighborhood of each pixel under the 3D rigid motion assumption of object, which is later compared against the observed 2D optic flow model in the neighborhood of a pixel in order to derive a pixelwise rigidity criterion. The latter is further reenforced along individual (pixel) trajectories in a video segment. Finally, a mere thresholding operation allows to quickly extract target from background in a full video segment. Our experiments using real maritime video sequences captured with an airborne camera have shown that the method detects maritime targets accurately.

An unsupervised approach to automatic object extraction from a maritime video scene

In this paper, an efficient unsupervised approach for extracting objects from maritime background using solely still video images is proposed. Its main idea is that maritime background (sea) has the main particularity of absorbing only hot light frequencies (red and green), while an object has not this property. Therefore if a timely vector of class features is considered, then two distinct statistical classes can be easily obtained in one maritime image, using an appropriate unsupervised two-class classification algorithm, and thus one can efficiently extract object from background. This is achieved by firstly partitioning a maritime image into equally-sized blocks, and constructing a 13-dimensional block-wise feature vector (regardless of block size) based on some observed statistics of light absorption in a block. The latter capture at the same time the characteristics (higher-order geometric moments) of the Fourier spectrum of RGB intensities in an image block, and its Shannon entropies. Both types of RGB information allow together generally to characterize well background versus object regions. Furthermore, a mere statistical test makes it possible to detect situations where there is no object in a maritime video scene but only sea. We have tested the proposed approach using several maritime scene videos and it has shown good performances in terms of detection accuracy, robustness and computational time.