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Chapter and Conference Paper
Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods
We have developed a new stochastic image rendering method for the compression, description and segmentation of images. This paintbrush-like image transformation is based on a random searching to insert brush-s...
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Chapter and Conference Paper
Adaptive Stabilization of Vibration on Archive Films
Image vibration is a typical type of degradation that is difficult to restore in an automatic film restoration system. It is usually caused by improper film transportation during the copying or the digitizatio...
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Chapter and Conference Paper
Comparing Objective and Subjective Quality Results for Compression Pre-processing with Non-linear Diffusion
Compression systems like JPEG include optional pre-processing with filtering to avoid compression artefacts. At higher compression ratios a stronger filtering is needed that impacts the large scale image conte...
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Chapter and Conference Paper
Hand Gesture Recognition in Camera-Projector System*
Our paper proposes a vision-based hand gesture recognition system. It is implemented in a camera-projector system to achieve an augmented reality tool. In this configuration the main problem is that the hand s...
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Chapter and Conference Paper
Image Indexing by Focus Map
Content-based indexing and retrieval (CBIR) of still and motion picture databases is an area of ever increasing attention. In this paper we present a method for still image information extraction, which in its...
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Chapter and Conference Paper
Use of Human Motion Biometrics for Multiple-View Registration
A novel image-registration method is presented which is applicable to multi-camera systems viewing human subjects in motion. The method is suitable for use with indoor or outdoor surveillance scenes. The paper...
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Chapter and Conference Paper
Markovian Framework for Foreground-Background-Shadow Separation of Real World Video Scenes
In this paper we give a new model for foreground-back-ground-shadow separation. Our method extracts the faithful silhouettes of foreground objects even if they have partly background like colors and shadows ar...
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Chapter and Conference Paper
Geometrical Scene Analysis Using Co-motion Statistics
Deriving the geometrical features of an observed scene is pivotal for better understanding and detection of events in recorded videos. In the paper methods are presented for the estimation of various geometric...
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Chapter and Conference Paper
VISRET – A Content Based Annotation, Retrieval and Visualization Toolchain
This paper presents a system for content-based video retrieval, with a complete toolchain for annotation, indexing, retrieval and visualization of imported data. The system contains around 20 feature descripto...
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Chapter and Conference Paper
Orthogonality Based Stop** Condition for Iterative Image Deconvolution Methods
Deconvolution techniques are widely used for image enhancement from microscopy to astronomy. The most effective methods are based on some iteration techniques, including Bayesian blind methods or Greedy algori...
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Chapter and Conference Paper
Geometrical and Textural Component Separation with Adaptive Scale Selection
The present paper addresses the cartoon/texture decomposition task, offering theoretically clear solutions for the main issues of adaptivity, structure enhancement and the quality criterion of the goal functio...
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Chapter and Conference Paper
Tracking the Saliency Features in Images Based on Human Observation Statistics
We address the statistical inference of saliency features in the images based on human eye-tracking measurements. Training videos were recorded by a head-mounted wearable eye-tracker device, where the position...
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Chapter and Conference Paper
A Dynamic MRF Model for Foreground Detection on Range Data Sequences of Rotating Multi-beam Lidar
In this paper, we propose a probabilistic approach for foreground segmentation in 360°-view-angle range data sequences, recorded by a rotating multi-beam Lidar sensor, which monitors the scene from a fixed pos...
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Chapter and Conference Paper
Calibrationless Sensor Fusion Using Linear Optimization for Depth Matching
Recently the observation of surveillanced areas scanned by multi-camera systems is getting more and more popular. The newly developed sensors give new opportunities for exploiting novel features.