Alessandro Mecocci

Comments on "A possibilistic approach to clustering"

In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy clustering (PCM) proposed by Keller and Krishnapuram (1993). In applying this algorithm we found that it has the undesirable tendency to produce coincidental clusters. Results illustrating this tendency are reported and a possible explanation for the PCM behavior is suggested.

Object tracking for retrieval applications in MPEG-2

The work presented in this article describes a tool for object tracking, notes insertion, and information retrieval, applicable to MPEG-2 sequences. Maximum compliance with the MPEG standard is sought, so the added information is transmitted as side information without affecting the actual video-audio stream as defined in the MPEG-2 standard. Additional processing is added to a standard sequence, allowing for automatic tracking of one object across different groups of pictures. Results show that the proposed algorithm is capable of tracking objects with a good degree of precision. Features are included to alert the human operator when objects disappear, or must be considered lost, due to an excessive change in their shape.

Fast vector median filter based on Euclidean norm approximation

The vector median filter has good filtering capabilities; nevertheless, its huge computational complexity significantly limits its practical usability. A vector median filter based on a fast approximation of the Euclidean norm is presented. The proposed algorithm couples computational and filtering effectiveness, and it is well suited for hardware implementation. Theoretical and experimental results regarding both approximation error and speed improvement prove the validity of the proposed algorithm.<<ETX>>

Theoretical and experimental assessment of noise effects on least-squares spectral unmixing of hyperspectral images

The problem of input noise affecting the subpixel classifica- tion is examined in order to assess its relationship with the output noise. The approach followed in this study was to investigate the output noise level obtained with a least-squares subpixel classification algorithm ap- plied to simulated spectra. The simulation of mixed pixel spectra took into account variable pixel composition and a selectable power of the superimposed noise. Noise was considered a zero-mean stochastic pro- cess over wavelength that was assumed to be jointly normal and uncor- related. The paper outlines the structure and the mathematical proper- ties of the performed unmixing simulations, and clearly shows the relationship between input and output noise. It is shown that a simple exponential law relates with substantial accuracy the standard deviation of input noise to that of the computed subpixel abundances for fully constrained unmixing. As expected, the cases of unconstrained and abundances sum to one partially constrained unmixing are controlled by a linear relationship between input and output noise amplitude. The paper also shows the dependence of unmixed abundances and output noise on the spectral similarity of end members involved in the unmixing. Three subpixel classification approaches unconstrained, partially con- strained, and fully constrained algorithms were investigated.

Counting people getting in and out of a bus by real-time image-sequence processing

Abstract The number of people getting in and out of a bus is an important parameter to allocate the proper number of buses for each connection-line of a public transport service. On the other hand, the correct distribution of the available buses over the different paths, is fundamental to obtain an optimization of the whole transport network, and to reduce costs. In this paper, an automatic system using dynamic image sequence processing to count people getting in and out of a bus is presented. Some fast algorithms are used to detect motion, estimate its direction, and validate the presence of moving people. The system can deal with vibrations, lighting fluctuations and environmental variations. The main advantages are the execution speed and the reliability of the counting process, is performed correctly even if people flow in a chaotic and very clustered way.

A suboptimal approach to channel equalization based on the nearest neighbor rule

Applications of clustering and neural network techniques to channel equalization have revealed the classification nature of this problem. This paper illustrates an implementation of a global system for mobile communications (GSM) receiver in which channel equalization and demodulation are realized by means of the nearest neighbor (NN) classifier algorithm. The most important advantage in using such techniques is the significant reduction in terms of the computational complexity compared with the maximum likelihood sequence estimation (MLSE) equalizer. The proposed approach involves symbol-by-symbol interpretation and the knowledge of the channel is embedded in the mapping process of the received symbols over the symbols of the training sequence. This means that no explicit channel estimation need be carried out, either with correlative blocks or using neural networks thus speeding up the entire process. The performance of the proposed receiver, evaluated through a channel simulator for mobile radio communications, is compared with the results obtained by means of a 16-state Viterbi algorithm and other suboptimal receivers. It is shown that the presented algorithm increases the bit error rate (BER) compared with the MLSE demodulator, but the performance degradation, despite the simplicity of the receiver, is kept within the limits imposed by the GSM specifications.

Colour-based detection of defects on chicken meat

Existing vision-based automatic inspection systems are mainly devoted to mechanic and electronic applications, their introduction into other fields being strongly limited by the need for operating in non-controlled environments and by the lack of an accurate definition of the inspection task. In this paper, an intelligent vision system aimed at the detection of defects on chicken meat before packing is presented. The detection of defects relies on the analysis of the chromatic content of chicken images. Possibly defective areas are first extracted by means of morphological image reconstruction, and then classified according to a predefined list of defects. Experimental results show the effectiveness of the proposed approach, thus proving the feasibility of automatic inspection of alimentary products.

Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation

Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.

Perceptual grouping for symbol chain tracking in digitized topographic maps

Abstract In this paper a new algorithm that applies perceptual grouping to detect and track discontinuous chains of symbols in digitized maps is proposed. The procedure is based on an artificial intelligence kernel that supervises three different auxiliary processes: the Search Strategy Generation module that is responsible for the strategy to scan pixels; the Symbol Detection (SD) module that extracts the recognized symbols; the Cost Function Evaluation (CFE) module that assigns a global quality index to each symbol by considering the whole course of the line. Selected Gestalt rules are used to optimize the grouping procedures. After the algorithm discussion, the problem of the extraction of dotted and dashed lines from digitized topographic maps is discussed. Experimental results on many maps of the Istituto Geografico Militare Italiano (IGMI) show a very good behavior: 92% of the discontinuous lines have been correctly chained, and the percentage of incorrectly classified symbols is also very small.

An intelligent system for automatic fire detection in forests

Fire detection is a very important problem today due to the economical, ecological and naturalistic value of forests for our world. In this paper it is presented a system that detects fire and operates in real-time by using some TV cameras, suitably placed in the external environment.