Direction Selective Contour Detection for Salient Objects

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy even in case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points are extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods

DBpedia Mashups

If you see Wikipedia as a main place where the knowledge of mankind is concentrated, then DBpedia – which is extracted from Wikipedia – is the best place to find machine representation of that knowledge. DBpedia constitutes a major part of the semantic data on the web. Its sheer size and wide coverage enables you to use it in many kind of mashups: it contains biographical, geographical, bibliographical data; as well as discographies, movie meta-data, technical specifications, and links to social media profiles and much more. Just like Wikipedia, DBpedia is a truly cross-language effort, e.g., it provides descriptions and other information in various languages. In this chapter we introduce its structure, contents, its connections to outside resources. We describe how the structured information in DBpedia is gathered, what you can expect from it and what are its characteristics and limitations. We analyze how other mashups exploit DBpedia and present best practices of its usage. In particular, we describe how Sztakipedia – an intelligent writing aid based on DBpedia – can help Wikipedia contributors to improve the quality and integrity of articles. DBpedia offers a myriad of ways to accessing the information it contains, ranging from SPARQL to bulk download. We compare the pros and cons of these methods. We conclude that DBpedia is an un-avoidable resource for pplications dealing with commonly known entities like notable persons, places; and for others looking for a rich hub connecting other semantic resources

Feature-Based Target Detection and Classification in Passive ISAR Range-Crossrange Images

We present a method for passive ISAR image analysis for target detection, feature extraction and shape-based classification without a priori target shape information. Results show that classification is possible with limited target samples

Jelnyelvi tolmácskesztyű - Hallod amit mutatok?

Felhúzol egy kesztyűt, berakod a zsebedbe a telefont. Elmutatod a mondandódat, a telefon pedig hangosan felolvassa: így beszélgetsz az emberekkel. A siketek és siketnémák számára óriási gondot jelent kapcsolatba lépni az épekkel, akik nem beszélik az ő nyelvüket, a jelnyelvet. A SZTAKI fejlesztésének, a Tolmácskesztyűnek köszönhetően egy olyan segédeszközhöz jutnak a beszédre képtelen fogyatékosok, ami folyamatosan és automatikusan fordítja le a jelnyelvet a beszélt nyelvre. Ez nem más, mint egy automatikus jeltolmácsgép, ami a nap 24 órájában a siketek rendelkezésére áll. Ebben az előadásban felvázoljuk a siket fogyatékosok problémáit, megnézzük, hogy jelenleg milyen segédeszközök állnak a rendelkezésükre, és bemutatjuk új kutatás-fejlesztésünket, a Tolmácskesztyűt. Az érdeklődők bepillanthatnak a mérnök-informatikus kutatók mindennapjaiba és kipróbálhatják a fejlesztés alatt álló prototípust, amit nem csak jelnyelv fordítására fogunk használni - meglepetéssel is készülünk

Assessment of Hypertensive Patients’ Complex Metabolic Status Using Data Mining Methods

Cardiovascular diseases are among the leading causes of mortality worldwide. Hypertension is a preventable risk factor leading to major cardiovascular events. We have not found a comprehensive study investigating Central and Eastern European hypertensive patients’ complex metabolic status. Therefore, our goal was to calculate the prevalence of hypertension and associated metabolic abnormalities using data-mining methods in our region. We assessed the data of adults who visited the University of Debrecen Clinical Center’s hospital (n = 937,249). The study encompassed data from a period of 20 years (2001–2021). We detected 292,561 hypertensive patients. The calculated prevalence of hypertension was altogether 32.2%. Markedly higher body mass index values were found in hypertensive patients as compared to non-hypertensives. Significantly higher triglyceride and lower HDL-C levels were found in adults from 18 to 80 years old. Furthermore, significantly higher serum glucose and uric acid levels were measured in hypertensive subjects. Our study confirms that the calculated prevalence of hypertension is akin to international findings and highlights the extensive association of metabolic alterations. These findings emphasize the role of early recognition and immediate treatment of cardiometabolic abnormalities to improve the quality of life and life expectancy of hypertensive patients