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Andrea Colombari

Bio: Andrea Colombari is an academic researcher from University of Verona. The author has contributed to research in topics: Initialization & Epipolar geometry. The author has an hindex of 8, co-authored 15 publications receiving 242 citations.

Papers
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Journal ArticleDOI
TL;DR: A content-based representation of a video shot composed by a background (still) mosaic and one or more foreground moving objects that segmentation of moving objects is based on ego-motion compensation and on background modelling using tools from robust statistics.

62 citations

Journal ArticleDOI
TL;DR: The proposed patch-based technique for robust background initialization that exploits both spatial and temporal consistency of the static background is able to cope with heavy clutter and compares favorably with existing techniques.
Abstract: In this paper, we propose a patch-based technique for robust background initialization that exploits both spatial and temporal consistency of the static background. The proposed technique is able to cope with heavy clutter, i.e, foreground objects that stand still for a considerable portion of time. First, the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then, a tessellation is grown incrementally by selecting at each step the best continuation of the current background. The method rests on sound principles in all its stages and only few, intelligible parameters are needed. Experimental results show that the proposed algorithm is effective and compares favorably with existing techniques.

40 citations

Journal ArticleDOI
TL;DR: The main contributions of this work lie in the proper selection, customization and integration of the main functions for road extraction and tracking to cope with the addressed application, and the subsequent FPGA hardware implementation as a modular architecture of specialized blocks.

39 citations

Proceedings ArticleDOI
17 Jun 2006
TL;DR: This paper proposes a technique to robustly estimate the background in a cluttered sequence, i.e., a sequence where occluding objects persist in the same position for a considerable portion of time, and introduces spatial support.
Abstract: In this paper we propose a technique to robustly estimate the background in a cluttered sequence, i.e., a sequence where occluding objects persist in the same position for a considerable portion of time. As pixel-level heuristic are not sufficient in this case, we introduce spatial support. First the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then the background is grown incrementally by selecting at each step the best continuation of the current background, according to the principles of visual grouping. The method rests on sound principles in all its stages, and only few, intelligible parameters are needed. Experiments with real sequences illustrate the approach.

32 citations

Proceedings ArticleDOI
11 Nov 2005
TL;DR: A novel method for BG initialization and recovery is proposed, that merges interesting ideas coming from the video inpainting and the generative modelling subfields and is able to exploit two hypotheses in a principled and effective way.
Abstract: Most of the automated video-surveillance applications are based on background (BG) subtraction techniques, that aim at distinguishing moving objects in a static scene. These strategies strongly depend on the BG model, that has to be initialized and updated. A good initialization is crucial for the successive processing. In this paper, we propose a novel method for BG initialization and recovery, that merges interesting ideas coming from the video inpainting and the generative modelling subfields. The method takes as input a video sequence, in which several objects move in front of a stationary BG. Then, a statistical representation of the BG is iteratively built, discarding automatically the moving objects. The method is based on the following hypotheses: (i) a portion of the BG, called sure BG, can be identified with high certainty by using only per-pixel reasoning and (ii) the remaining scene BG can be generated utilizing exemplars of the sure BG. The proposed algorithm is able to exploit these hypotheses in a principled and effective way.

23 citations


Cited by
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Reference EntryDOI
15 Oct 2004

2,118 citations

Book
14 Dec 2016
TL;DR: Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.
Abstract: Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.The second edition is updated to cover new features and changes in OpenCV 2.0, especially the C++ interface.Computer vision is everywherein security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.This book includes:A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms

1,222 citations

Journal ArticleDOI
TL;DR: The technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and is demonstrated through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Abstract: We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

432 citations

Journal ArticleDOI
TL;DR: The proposed novel RGB-D data-based motion removal approach acted as a pre-processing stage to filter out data that were associated with moving objects in the traversed environments during the SLAM process.

290 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the background subtraction methods, that considers also channels other than the sole visible optical one (such as the audio and the infrared channels), organized in a novel taxonomy that encapsulates all the brand-new approaches in a seamless way.
Abstract: Background subtraction is a widely used operation in the video surveillance, aimed at separating the expected scene (the background) from the unexpected entities (the foreground). There are several problems related to this task, mainly due to the blurred boundaries between background and foreground definitions. Therefore, background subtraction is an open issue worth to be addressed under different points of view. In this paper, we propose a comprehensive review of the background subtraction methods, that considers also channels other than the sole visible optical one (such as the audio and the infrared channels). In addition to the definition of novel kinds of background, the perspectives that these approaches open up are very appealing: in particular, the multisensor direction seems to be well-suited to solve or simplify several hoary background subtraction problems. All the reviewed methods are organized in a novel taxonomy that encapsulates all the brand-new approaches in a seamless way.

189 citations