Browsing by Author "Viitaniemi, Ville"
Now showing 1 - 8 of 8
- Results Per Page
- Sort Options
- Concept-based video search with the PicSOM multimedia retrieval system
Aalto-yliopiston teknillinen korkeakoulu | D4 Julkaistu kehittämis- tai tutkimusraportti tai -selvitys(2010) Viitaniemi, Ville; Sjöberg, Mats; Koskela, Markus; Laaksonen, Jorma - Evaluation of pointer click relevance feedback in PicSOM : deliverable D1.2 of FP7 project nº 216529 PinView
Faculty of Information and Natural Sciences | D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys(2008) Viitaniemi, Ville; Laaksonen, JormaThis report presents the results of a series of experiments where knowledge of the most relevant part of images is given as additional information to a content-based image retrieval system. The most relevant parts have been identified by search-task-dependent pointer clicks on the images. As such they provide a rudimentary form of explicit enriched relevance feedback and to some extent mimic genuine implicit eye movement measurements which are essential ingredients of the PinView project. - Eye and mouth openness estimation in sign language and news broadcast videos
Perustieteiden korkeakoulu | Master's thesis(2014) Luzardo, MarcosCurrently there exists an increasing need of automatic video analysis tools to support sign language studies and the evaluation of the activity of the face in sign language and other videos. Henceforth, research focusing on automatic estimation and annotation of videos and facial gestures is continuously developing. In this work, techniques for the estimation of eye and mouth openness and eyebrow position are studied. Such estimation could prove beneficial for automatic annotation and quantitative evaluation of sign language videos as well as towards more prolific production of sign language material. The method proposed for the estimation of the eyebrow position, eye openness, and mouth state is based on the construction of a set of facial landmarks that employ different detection techniques designed for each facial element. Furthermore, we compare the presented landmark detection algorithm with a recently published third-party face alignment algorithm. The landmarks are used to compute features which describe the geometric information of the elements of the face. The features constitute the input for the classifiers that can produce quantized openness estimates for the studied facial elements. Finally, the estimation performance of the estimations is evaluated in quantitative and qualitative experiments with sign language and news broadcast videos. - Image segmentation in content-based image retrieval
Helsinki University of Technology | Master's thesis(2002) Viitaniemi, Ville - Kasvojen ilmeiden tunnistaminen videokuvasta
Perustieteiden korkeakoulu | Bachelor's thesis(2014-04-13) Väänänen, Pekka - Mallipohjaiset käsienseurantamenetelmät ja niiden soveltuvuus viittomakielten käden konfiguraatioiden estimointiin videosta
Perustieteiden korkeakoulu | Bachelor's thesis(2011-11-29) Karppa, Matti - Techniques for image classification, object detection and object segmentation
Faculty of Information and Natural Sciences | D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys(2008) Viitaniemi, Ville; Laaksonen, JormaIn this paper we document the techniques which we used to participate in the PASCAL NoE VOC Challenge 2007 image analysis performance evaluation campaign. We took part in three of the image analysis competitions: image classification, object detection and object segmentation. In the classification task of the evaluation our method produced comparatively good performance, the 4th best of 19 submissions. In contrast, our detection results were quite modest. Our method's segmentation accuracy was the best of all submissions. Our approach for the classification task is based on fused classifications by numerous global image features, including histograms of local features. The object detection combines similar classification of automatically extracted image segments and the previously obtained scene type classifications. The object segmentations are obtained in a straightforward fashion from the detection results. - Visual category detection: an experimental perspective
School of Science | Doctoral dissertation (article-based)(2012) Viitaniemi, VilleNowadays huge volumes of digital visual data are constantly being produced and archived. Automatically distilling useful information from such information masses requires one to somehow answer the grand long-standing question of computer vision: how to make computers understand images? In this thesis the visual content analysis problem is looked at as a category detection problem. In the category detection formulation, the general image content understanding task is partitioned into a number of small binary decision tasks. In each of the sub-tasks, one decides whether an image belongs to some pre-defined category. A category could be defined, for example, to consist of images taken indoors. By defining an appropriate set of categories, the visual content of an image can be described on a desired level of granularity by determining the image's membership in each one of the categories. This thesis discusses a framework for visual category detection that consists of three major components: feature extraction, feature-wise detection and fusion of the detection results. The point of view in the discussion is empirical: the framework is validated by the good levels of performance systems implementing it have demonstrated in various benchmark tasks of visual analysis. A body of experiments is described that compare various technological alternatives for implementing the three major components of the framework. In addition to comparing implementation techniques, the experiments demonstrate that the discussed generic category detection architecture is very versatile: a set of diverse visual analysis problems can be addressed using the same visual category detection system as a backbone component by equipping the system with a task-specific front-end. From the experiments and discussion in the thesis, one can conclude that the category detection formulation is a useful way of approaching the general image content understanding problem. In category detection, performances reaching the state-of-the-art can be realised using the presented fusion-based system architecture and implementation technologies of the system components.