In this paper, a new approach to face description is proposed. The linguistic description of human faces in digital pictures is generated within a framework of fuzzy granulation. Fuzzy relations and fuzzy relational rules are applied in order to create the image description. By use of type-2 fuzzy sets, fuzzy relations, and fuzzy IF-THEN rules, an image recognition system can infer and explain its decision. Such a system can retrieve an image, recognize, and classify – especially a human face – based on the linguistic description. © 2021, Springer Nature Switzerland AG.

In this paper, a new approach to face recognition is proposed. The knowledge represented by fuzzy IF-THEN rules, with type-1 and type-2 fuzzy sets, are employed in order to generate the linguistic description of human faces in digital pictures. Then, an image recognition system can recognize and retrieve a picture (image of a face) or classify face images based on the linguistic description. Such a system is explainable – it can explain its decision based on the fuzzy rules. © 2020, Springer Nature Switzerland AG.

The classical fuzzy decision-making model is now tested for qualitative compound states-symptoms to select the most efficacious medicine, acting on all symptoms. Instead of terminating the decision procedure in the way comparing values of total utilities of decisions-treatments, we test the aggregated utility values in utility levels. This activity lets us assign a verbally verified utility to each medicine.

In this paper, a new method of image retrieval is proposed. This concerns retrieving color digital images from a database that contains a specific linguistic description considered within the theory of fuzzy granulation and computing with words. The linguistic description is generated by use of the CIE chromaticity color model. The image retrieval is performed in different way depending on users' knowledge about the color image. Specific database queries can be formulated for the image retrieval.

This paper presents different aspects of parallelization of a problem of processing color digital images in order to generate linguistic description of their content. A parallel architecture of an intelligent image recognition system is proposed. Fuzzy classifcation and inference is performed in parallel, based on the CIE chromaticity color model and granulation approach. In addition, the parallelization concerns, e.g., processing a large collection of images or parts of a single image.

The paper proposes a new method employed in an intelligent pattern recognition system that generates linguistic description of color digital images. The linguistic description is produced based on fuzzy rules and information granules concerning colors as most important among image attributes. With regard to the color, the CIE chromaticity color model is applied, with the concept of fuzzy color areas. The linguistic description uses information about location of color granules in input images. © Springer International Publishing AG 2017.

Dynamic signature verification is a very interesting biometric issue. It is difficult to realize because signatures of the user are characterized by relatively high intra-class and low inter-class variability. However, this method of an identity verification is commonly socially acceptable.

It is a big advantage of the dynamic signature biometric attribute. In this paper, we propose a new hybrid algorithm for the dynamic signature verification based on global and regional approach. We present the simulation results of the proposed method for BioSecure DS2 database, distributed by the BioSecure Association.

By proposing a new approach to fuzzy decision making, we try to support the medical decision, concerning recommendations for the treatment with hyperbaric oxygen (HBO). This treatment can be used for patients, suffering from necrotizing fasciitis. Due to the disease rarity, it sometimes is difficult for a physician to determine, if a single patient needs the treatment with HBO. We thus identify the decision with a linguistic variable, equipped with treatment recommendation levels. The choice of the appropriate level is based on values of clinical symptoms, found in the patient. To extract the optimal recommendation level for the treatment with HBO, we involve fuzzy set techniques in the decision model. In the paper, we mainly concentrate on designs of fuzzy sets, standing for clinical symptoms and recommendation levels. The levels act as the outcomes, dependent on the cumulative input of the patient’s clinical markers. Since the focus is laid on a parametric structure of the outcomes, then we can categorize the model as robust approach to algorithmic modeling of outcomes, being part of eHealth data records.

The paper describes new algorithms proposed for the granular pattern recognition system that retrieves an image from a collection of color digital pictures based on the knowledge contained in the object information granule (OIG). The algorithms use the granulation approach that employs fuzzy and rough granules. The information granules present knowledge concerning attributes of the object to be recognized. Different problems are considered depending on the full or partial knowledge where attributes are "color", "location", "size", and  "shape".

In the current research, we aim to define a new form of the similarity coefficient to compare the resemblance grade of two Gaussian density functions. We aim to assess the method utility on a theoretical model. The density functions are stated for a biological marker “survival length”, observed in three groups of patients, suffering from a hypothetical disease. The first group consists of patients who are not treated, whereas we recommend 2 possible treatment methods for the second and the third group, respectively. All the “survival length” assumptions of the model (mean values and standard deviations) are made to exclude the equivocal conclusion, regarding a selection of the better treatment. At the first stage, we apply the measure of similarity to populations: survival among untreated patients contra survival among patients after Treatment 1. Another similarity coefficient estimates a relation between populations: survival among untreated patients versus survival among patients after Treatment 2. The lower value of the coefficient points out the more effective treatment. In order to simplify calculations, proposed in the definition of a similarity coefficient, we approximate the Gaussian curve by a specially designed polynomial, known as the p-function.