7 research outputs found
Badanie stabilno艣ci optymalnych parametr贸w klasyfikatora bazuj膮cego na prostych granulach wiedzy
Searching for optimal parameters of a classifier based on simple granules of knowledge investigated recently by the author (ARTIEMJEW 2010) raises a question about stability of optimal parameters. In this article, we will check dependence of stability of the optimal radius of granulation on random damage of decision system. The results of experiments show the dependence of stability on size of damage and strategies of treating missing values. This kind of research aims at finding methods of protecting decision systems which are vulnerable to damage against decreasing their classification effectiveness, which means preserving classifying possibilities similar to undamaged decision systems.Przeprowadzone w ostatnim czasie badania (ARTIEMJEW 2010) zmierzaj膮ce do wyszukiwania optymalnych parametr贸w klasyfikacji modu艂贸w decyzyjnych opartych na prostych granulach wiedzy zrodzi艂y pytanie o stabilno艣膰 optymalnych parametr贸w klasyfikacji. W pracy sprawdzono zale偶no艣膰 stabilno艣ci optymalnych promieni granulacji od losowego uszkadzania systemu decyzyjnego. Wyniki bada艅 wskaza艂y jednoznacznie, 偶e istnieje zale偶no艣膰 mi臋dzy stabilno艣ci膮 a wielko艣ci膮 uszkodzenia i strategiami traktowania warto艣ci uszkodzonych. Tego typu badania maj膮 na celu szukanie metod zabezpieczania system贸w decyzyjnych, kt贸re s膮 podatne na uszkodzenia, przed zmniejszaniem ich efektywno艣ci klasyfikacyjnej. Celem by艂o zachowanie mo偶liwo艣ci klasyfikacyjnych zbli偶onych do efektywno艣ci nieuszkodzonych system贸w decyzyjnych
Control of mindstorms NXT robot using Xtion Pro camera skeletal tracking
This paper focuses on the topic of creating a gesture-oriented user control system for robotic
agents. For testing purposes we have used a Lego Mindstorm NXT self-designed robot and a Xtion
Pro camera. Our construction consists of a NXT brick and three motors (two used for movement and
one for the clutch mechanism). Our software is developed using C++ with NXT++ and
OpenNi/NiTe libraries. System tests were performed in a real environment
Categorization of Similar Objects Using Bag of Visual Words and k - Nearest Neighbour Classifier
Image categorization is one of the fundamental tasks in computer vision, it has wide application in methods of artificial intelligence, robotic vision and many others. There are a lot of difficulties in computer vision to overcome, one of them appears during image recognition and classification. The difficulty arises from an image variance, which may be caused by scaling, rotation, changes in a perspective, illumination levels, or partial occlusions. Due to these reasons, the main task is to represent represent images in such way that would allow recognizing them even if they have been modified. Bag of Visual Words (BoVW) approach, which allows for describing local characteristic features of images, has recently gained much attention in the computer vision community. In this article we have presented the results of image classification with the use of BoVW and k - Nearest Neighbor classifier with different kinds of metrics and similarity measures. Additionally, the results of k - NN classification are compared with the ones obtained from a Support Vector Machine classifier