Catálogo de publicaciones - libros

In this paper, we propose an so as to endow the users with the ability to control various home appliances instead of individual remote controller for each appliance and to command naturally at various places without being conscious of the view direction of the cameras. Through the developed system, the user first selects the device that he/her wants to control by pointing it with his/her hand. Then, the user can command operation of the desired functions via 10 predefined basic hand motion commands. By the experiment, we can get 97.1% recognition rate during offline test and 96.5% recognition rate during online test. The developed system complements some inconveniences of conventional remote controllers specially by giving additional freedom to persons with movement deficits and people without disabilities.

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking error for maneuvering target. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After an acceleration input is detected, the state estimate for each sub-model is modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). Finally, the tracking performance of the proposed method is compared with those of the input estimation(IE) method and AIMM method through computer simulations.

This paper describes a complete FPGA implemented intrinsic evolvable system which is employed as a novel approach to automatic design of spatial image filters for two given types of noise. The genotype-phenotype representation of the proposed evolvable system is inspired by the Cartesian Genetic Programming and the function level evolution. The innovative feature of the proposed system is that the whole evolvable system which consists of evolutionary algorithm unit, fitness value calculation unit and reconfigurable function elements array is realized in a same FPGA. A commercial and current FPGA card: Celoxica RC1000 PCI board with a Xilinx Virtex xcv2000E FPGA is employed as our hardware platform. The main motive of our research is to design a general, simple and fast virtual reconfigurable hardware platform with powerful computation ability to achieve intrinsic evolution. The experiment results show that a spatial image filter can be evolved in less than 71 seconds.

We present an algorithm for automatic selection of features that best segment an image in texture homogeneous regions. The set of “best extractors” are automatically selected among the Gabor filters, Co-occurrence matrix, Law’s energies and intensity response. Noise-features elimination is performed by taking into account the magnitude and the granularity of each feature image, i.e. the compute image when a specific feature extractor is applied. Redundant features are merged by means of probabilistic rules that measure the similarity between a pair of image feature. Then, cascade applications of general purpose image segmentation algorithms (K-Means, Graph-Cut and EC-GMMF) are used for computing the final segmented image. Additionally, we propose an evolutive gradient descent scheme for training the method parameters for a benchmark image set. We demonstrate by experimental comparisons, with stat of the art methods, a superior performance of our technique.

This paper presents a hybrid segmentation algorithm, which provides a synthetic image description in terms of regions. This method has been used to segment images of outdoor scenes. We have applied our segmentation algorithm to color images and images encoding 3D information. 5 different color spaces were tested. The segmentation results obtained with each color space are compared.

This paper presents a novel method that uses incremental self-organizing map (ISOM) network and wavelet transform together for the segmentation of magnetic resonance (MR), computer tomography (CT) and ultrasound (US) images. In order to show the validity of the proposed scheme, ISOM has been compared with Kohonen’s SOM. Two-dimensional continuous wavelet transform (2D-CWT) is used to form the feature vectors of medical images. According to the selected two feature extraction methods, features are formed by the intensity of the pixel of interest or mean value of intensities at one neighborhood of the pixel at each sub-band. The first feature extraction method is used for MR and CT head images. The second method is used for US prostate image.

Iris recognition is a method used to identify people based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: (1) image acquisition and preprocessing, (2) iris localization and extraction, (3) iris features characterization, and (4) comparison and matching. A novel contribution in the step of characterization of iris features is introduced by using a Hammersley’s sampling algorithm and accumulated histograms. Histograms are computed with data coming from sampled sub-images of iris. The optimal number and dimensions of samples is obtained by the simulated annealing algorithm. For the last step, couples of accumulated histograms iris samples are compared and a decision of acceptance is taken based on an experimental threshold. We tested our ideas with UBIRIS database; for clean eye iris databases we got excellent results.

We tackle the problem of retrieving images from a database. In particular we are concerned with the problem of retrieving images of airplanes belonging to one of the following six categories: 1) commercial planes on land, 2) commercial planes in the air, 3) war planes on land, 4) war planes in the air, 5) small aircrafts on land, and 6) small aircrafts in the air. During training, a wavelet-based description of each image is first obtained using Daubechies 4-wavelet transformation. The resulting coefficients are then used to train a neural network. During classification, test images are presented to the trained system. The coefficients are obtained from the Daubechies transform from histograms of a decomposition of the image into square sub-images of each channel of the original image. 120 images were used for training and 240 for independent testing. An 88% correct identification rate was obtained.

From the engineering aspect, the research on Kansei information is a field aimed at processing and understanding how human intelligence processes subjective information or ambiguous sensibility and how such information can be executed by a computer. Our study presents a method of image processing aimed at accurate image retrieval based on human Kansei. We created the Kansei-Vocabulary Scale by associating Kansei of high-level information with shapes among low-level features of an image and constructed the object retrieval system using Kansei-Vocabulary Scale. In the experimental process, we put forward an adaptive method of measuring similarity that is appropriate for Kansei-based image retrieval. We call it “adaptive-Tangent Space Representation (adaptive-TSR)”. The method is based on the improvement of the TSR in 2-dimensional space for Kansei-based retrieval. We then it define an adaptive similarity algorithm and apply to the Kansei-based image retrieval. As a result, we could get more promising results than the existing method in terms of human Kansei.

We consider statistical properties of prepositions—the most numerous and important functional words in European languages. Usually, they syntactically link verbs and nouns to nouns. It is shown that their rank distributions in Russian differ radically from those of content words, being much more compact. The Zipf law distribution commonly used for content words fails for them, and thus approximations flatter at first ranks and steeper at higher ranks are applicable. For these purposes, the Mandelbrot family and an expo-logarithmic family of distributions are tested, and an insignificant difference between the two least-square approximations is revealed. It is proved that the first dozen of ranks cover more than 80% of all preposition occurrences in the DB of Russian collocations of Verb-Preposition-Noun and Noun-Preposition-Noun types, thus hardly leaving room for the rest two hundreds of available Russian prepositions.