The obstacle detection field is a very broad one and a lot of obstacle detection systems have been developed in the last years in this domain. We tried to identify the main character of an obstacle detection system from the ruttier scene. Thus, we classified the main types of sensors from this field in passive (visible and infrared spectrum camera) and active (radar, laser-scanner, sonar) sensors and we made a survey in this domain. After a short presentation of every type of sensor, we presented another current and fancy solution for an obstacle detection system: the fusion of different sensor together. Almost all obstacle detection systems use a combination of passive-active technology, and in general the best solution is obtained using a vision system combined with a distance sensor like radar or laser. Maybe the most low-priced system is one combining only vision systems, but the inconvenient in this case is the lack of distance information.

After a review of the circle fitting issue, we recall a relatively unknown method derived from a classical geometric result. We propose an improvement of this technique by reweighting the data, iterating the procedure, and choosing at every step as the new inversion point the one diametrically opposite to the previous inversion point. © 2003 SPIE and IS&T.

In this paper we evaluate the effect of the emotional state of a speaker when text-independent speaker identification is per formed. The spectral features used for speaker recognition are the Mel-frequency cepstral coefficients, while for the training of the speaker models and testing the system the Gaussian Mixture Models are employed. The tests are performed on the Berlin emotional speech database which contains 10 different speakers recorded in different emotional situations: happy, angry, fear, bored, sad and neutral. The results show an important influence of the emotional state upon text-independent speaker identification. In the end we try to give a possible solution to this problem.

The goal of this work is to present an audio signal classification system based on Linear Predictive Coding and Random Forests. We consider the problem of multiclass classification with imbalanced datasets. The signals under classification belong to the class of sounds from wildlife intruder detection applications: birds, gunshots, chainsaws, human voice and tractors. The proposed system achieves an overall correct classification rate of 99.25%. There is no probability of false alarms in the case of birds or human voices. For the other three classes the probability is low, around 0.3%. The false omission rate is also low: around 0.2% for birds and tractors, a little bit higher for chainsaws (0.4%), lower for gunshots (0.14%) and zero for human voices.