Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

The reconstruction of social meaning in the space of flows

Artikkeli on alunperin julkaistu teoksessa: The informational city (1989) / Manuel Castells

Effects of group size and space allocation on physiological, behavioural and production-related welfare parameters in farmed silver fox cubs

Selostus: Ryhmäkoon ja käytössä olevan tilan vaikutus tarhattujen hopeakettupentujen hyvinvointiin

Performance Characteristics of an Axial-Flux Solid-Rotor-Core Induction Motor

The integration of electric motors and industrial appliances such as pumps, fans, and compressors is rapidly increasing. For instance, the integration of an electric motor and a centrifugal pump provides cost savings and improved performance characteristics. Material cost savings are achieved when an electric motor is integrated into the shaft of a centrifugal pump, and the motor utilizes the bearings of the pump. This arrangement leads to a smaller configuration that occupies less floor space. The performance characteristics of a pump drive can be improved by using the variable-speed technology. This enables the full speed control of the drive and the absence of a mechanical gearbox and couplers. When using rotational speeds higher than those that can be directly achieved by the network frequency the structure of the rotor has to be mechanically durable. In this thesis the performance characteristics of an axial-flux solid-rotor-core induction motor are determined. The motor studied is a one-rotor-one-stator axial-flux induction motor, and thus, there is only one air-gap between the rotor and the stator. The motor was designed for higher rotational speeds, and therefore a good mechanical strength of the solid-rotor-core rotor is required to withstand the mechanical stresses. The construction of the rotor and the high rotational speeds together produce a feature, which is not typical of traditional induction motors: the dominating loss component of the motor is the rotor eddy current loss. In the case of a typical industrial induction motor instead the dominating loss component is the stator copper loss. In this thesis, several methods to decrease the rotor eddy current losses in the case of axial-flux induction motors are presented. A prototype motor with 45 kW output power at 6000 min-1 was designed and constructed for ascertaining the results obtained from the numerical FEM calculations. In general, this thesis concentrates on the methods for improving the electromagnetic properties of an axial-flux solid-rotor-core induction motor and examines the methods for decreasing the harmonic eddy currents of the rotor. The target is to improve the efficiency of the motor and to reach the efficiency standard of the present-day industrial induction motors equipped with laminated rotors.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Local and Global Feature Extraction for Invariant Object Recognition

Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.

Modelling additional costs caused by a new product feature

Työn tavoitteena oli mallintaa uuden tuoteominaisuuden aiheuttamat lisäkustannukset ja suunnitella päätöksenteon työkalu Timberjack Oy:n kuormatraktorivalmistuksen johtoryhmälle. Tarkoituksena oli luoda karkean tason malli, joka sopisi eri tyyppisten tuoteominaisuuksien kustannuksien selvittämiseen. Uuden tuoteominaisuuden vaikutusta yrityksen eri toimintoihin selvitettiin haastatteluin. Haastattelukierroksen tukena käytettiin kysymyslomaketta. Haastattelujen tavoitteena oli selvittää prosessit, toiminnot ja resurssit, jotka ovat välttämättömiä uuden tuoteominaisuuden tuotantoon saattamisessa ja tuotannossa. Malli suunniteltiin haastattelujen ja tietojärjestelmästä hankitun tiedon pohjalta. Mallin rungon muodostivat ne prosessit ja toiminnot, joihin uudella tuoteominaisuudella on vaikutusta. Huomioon otettiin sellaiset resurssit, joita uusi tuoteominaisuus kuluttaa joko välittömästi, tai välillisesti. Tarkasteluun sisällytettiin ainoastaan lisäkustannukset. Uuden tuoteominaisuuden toteuttamisesta riippumattomat, joka tapauksessa toteutuvat yleiskustannukset jätettiin huomioimatta. Malli on yleistys uuden tuoteominaisuuden aiheuttamista lisäkustannuksista, koska tarkoituksena on, että se sopii eri tyyppisten tuoteominaisuuksien aiheuttamien kustannusten selvittämiseen. Lisäksi malli soveltuu muiden pienehköjen tuotemuutosten kustannusten kartoittamiseen.

Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

Musikaliska möten man minns : om musikundervisningen i årskurserna sju till nio som en arena för starka musikpplevelser

Earlier research has shown that strong experiences related to music (SEM) can occur in very different contexts and take on many different forms. Experiences like these seem significant and have among other things reportedly had an affect on the individual's continuing relationship towards music, which makes them interesting from a pedagogical point of view. Formal teaching situations, though, are under-represented in studies where people have been asked to describe strong experiences that they have had in connection with music. The purpose of my thesis is to investigate what SEM may mean to pupils and teachers in lower secondary school (grades 7-9), and to inquire more deeply into the potential "space" for such experiences within school music education. On a comprehensive level my ambition is to deepen the understanding for SEM as a possible element in pedagogical situations. Three empirical perspectives are employed: pupil-, teacher- and curriculum perspectives. The pupil perspective involved an analysis of written accounts of 166 fifteen-year-olds, describing own strong experiences. The teacher perspective involved studying 28 music teachers' conceptions of the purpose of teaching music in school as well as their understanding about strong music experiences in school context. Further, the teachers' descriptions of SEM that they have had themselves were analysed. The curriculum perspective is reflected through a study of how music experience was represented in 24 local and 2 national curriculum texts for music. Grounded in a phenomenological-hermeneutical perspective the material have been analyzed both qualitatively and quantitatively. The result points towards the fact that the music education in school has the potential to become an arena for SEM and that this can happen in relation to a multitude of activities and genres and take on many different expressions. Only one pupil referred to a musical encounter in the classroom environment; all other experiences that occurred inside the frame of school activity had taken place in other arenas (the school hall, public concert halls, and so on). However, more than 98 % of the descriptions concerned musical encounters in leisure time contexts. The significance of SEM is further clarified by narrative constructions. SEM as a conception does not occur on the curriculum level; however the analyze revealed a number of interesting "openings" which are illustrated. Even though all teachers displayed a fundamentally positive attitude towards the idea of regarding SEM as a feature of formal musical learning, it became clear that many teachers never had approached this theme from a pedagogical point of view before. Still, they proved to have an evident "familiarity" towards the phenomenon based on their own experiences of receptive and performative musical encounter. The possible space for strong musical experiences within school music education is specified through a detailed illustration of six specific themes derived from the reasoning of the teachers. Furthermore, this is described through a mapping of the potential experiencing zone, constructed from the teachers descriptions of educational aims.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task