23 research outputs found
Camera motion estimation through planar deformation determination
In this paper, we propose a global method for estimating the motion of a
camera which films a static scene. Our approach is direct, fast and robust, and
deals with adjacent frames of a sequence. It is based on a quadratic
approximation of the deformation between two images, in the case of a scene
with constant depth in the camera coordinate system. This condition is very
restrictive but we show that provided translation and depth inverse variations
are small enough, the error on optical flow involved by the approximation of
depths by a constant is small. In this context, we propose a new model of
camera motion, that allows to separate the image deformation in a similarity
and a ``purely'' projective application, due to change of optical axis
direction. This model leads to a quadratic approximation of image deformation
that we estimate with an M-estimator; we can immediatly deduce camera motion
parameters.Comment: 21 pages, version modifi\'ee accept\'e le 20 mars 200
Data fusion by segmentation. Application to texture discrimination
Nous présentons un algorithme de segmentation multi-canaux qui est déduit de la fonctionnelle de Mumford et Shah. La méthode est un algorithme de croissance sur une image "vectorielle". Les applications présentées sont la discrimination de textures et la détection d'objets sur fond naturel
Data fusion by segmentation. Application to texture discrimination.
Introduction. Image processors today are often presented with multiple data for the same scene, obtained using various sensors. Typical examples include satellite pictures using multispectral data or medical data such as MR images. It thus seems quite natural to include this information in image processing algorithms. This not only should improve analysis of the data but should also help to obtain more stable algorithms since there is more information available. Another way to obtain multichannel input is by preprocessing a textured picture. Indeed in order to characterize textures the common method is to get information about orientation, terminators, corners and so on. We present a multidimensional segmentation algorithm which has been presented in [KMS] for the gray-level case. This note completes the description of the possibilities of the simple segmentation model given by the Mumford and Shah functional (see [MS]) and the corresponding region growing algorithm. The outl
Combinatorial pyramids and discrete geometry for energy-minimizing segmentation
International audienceThis paper defines the basis of a new hierarchical framework for segmentation algorithms based on energy minimization schemes. This new framework is based on two formal tools. First, a combinatorial pyramid encode efficiently a hierarchy of partitions. Secondly, discrete geometric estimators measure precisely some important geometric parameters of the regions. These measures combined with photometrical and topological features of the partition allows to design energy terms based on discrete measures. Our segmentation framework exploits these energies to build a pyramid of image partitions with a minimization scheme. Some experiments illustrating our framework are shown and discussed
Multi-Scale Improves Boundary Detection in Natural Images
Abstract. In this work we empirically study the multi-scale boundary detection problem in natural images. We utilize local boundary cues including contrast, localization and relative contrast, and train a classifier to integrate them across scales. Our approach successfully combines strengths from both large-scale detection (robust but poor localization) and small-scale detection (detail-preserving but sensitive to clutter). We carry out quantitative evaluations on a variety of boundary and object datasets with human-marked groundtruth. We show that multi-scale boundary detection offers large improvements, ranging from 20% to 50%, over single-scale approaches. This is the first time that multi-scale is demonstrated to improve boundary detection on large datasets of natural images.