Julien Mille

Narrow band region-based active contours and surfaces for 2D and 3D segmentation

We describe a narrow band region approach for deformable curves and surfaces in the perspective of 2D and 3D image segmentation. Basically, we develop a region energy involving a fixed-width band around the curve or surface. Classical region-based methods, like the Chan-Vese model, often make strong assumptions on the intensity distributions of the searched object and background. In order to be less restrictive, our energy achieves a trade-off between local features of gradient-like terms and global region features. Relying on the theory of parallel curves and surfaces, we perform a mathematical derivation to express the region energy in a curvature-based form allowing efficient computation on explicit models. We introduce two different region terms, each one being dedicated to a particular configuration of the target object. Evolution of deformable models is performed by means of energy minimization using gradient descent. We provide both explicit and implicit implementations. The explicit models are a parametric snake in 2D and a triangular mesh in 3D, whereas the implicit models are based on the level set framework, regardless of the dimension. Experiments are carried out on MRI and CT medical images, in 2D and 3D, as well as 2D color photographs.

Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading.

This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of the reference standard and the evaluation measures. This framework has been introduced at the MICCAI 2009 workshop 3D Segmentation in the Clinic: A Grand Challenge III, and we compare the results of eight teams that participated. These results show that automated segmentation of the vessel lumen is possible with a precision that is comparable to manual annotation. The framework is open for new submissions through the website http://cls2009.bigr.nl.

Understanding leaves in natural images - A model-based approach for tree species identification

With the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia.

Evaluation of video activity localizations integrating quality and quantity measurements

Evaluating the performance of computer vision algorithms is classically done by reporting classification error or accuracy, if the problem at hand is the classification of an object in an image, the recognition of an activity in a video or the categorization and labeling of the image or video. If in addition the detection of an item in an image or a video, and/or its localization are required, frequently used metrics are Recall and Precision, as well as ROC curves. These metrics give quantitative performance values which are easy to understand and to interpret even by non-experts. However, an inherent problem is the dependency of quantitative performance measures on the quality constraints that we need impose on the detection algorithm. In particular, an important quality parameter of these measures is the spatial or spatio-temporal overlap between a ground-truth item and a detected item, and this needs to be taken into account when interpreting the results. We propose a new performance metric addressing and unifying the qualitative and quantitative aspects of the performance measures. The performance of a detection and recognition algorithm is illustrated intuitively by performance graphs which present quantitative performance values, like Recall, Precision and F-Score, depending on quality constraints of the detection. In order to compare the performance of different computer vision algorithms, a representative single performance measure is computed from the graphs, by integrating out all quality parameters. The evaluation method can be applied to different types of activity detection and recognition algorithms. The performance metric has been tested on several activity recognition algorithms participating in the ICPR 2012 HARL competition.

Region-Based 2D Deformable Generalized Cylinder for Narrow Structures Segmentation

In this paper, we present a region-based deformable cylinder model, extending the work on classical region-based active contours and gradient-based ribbon snakes. Defined by a central curve playing the role of the medial axis and a variable thickness, the model is endowed with a region-dependent term.This energy follows the narrow band principle, in order to handle local region properties while overcoming limitations of classical edge-based models. The energy is subsequently transformed and derived in order to allow implementation on a polygonal line deformed with gradient descent. The model is used to extract path-like objects in medical and aerial images.

A model-based approach for compound leaves understanding and identification

In this paper, we propose a specific method for the identification of compound-leaved tree species, with the aim of integrating it in an educational smartphone application. Our work is based on dedicated shape models for compound leaves, designed to estimate the number and shape of leaflets. A deformable template approach is used to fit these models and produce a high-level interpretation of the image content. The resulting models are later used for the segmentation of leaves in both plain and natural background images, by the use of multiple region-based active contours. Combined with other botany-inspired descriptors accounting for the morphological properties of the leaves, we propose a classification method that makes a semantic interpretation possible. Results are presented over a set of more than 1000 images from 17 European tree species, and an integration in the existing mobile application Folia1 is considered.

Space-time spectral model for object detection in dynamic textured background

Background models are used for object detection in many computer vision algorithms. In this article, we propose a novel background modeling method based on frequency for spatially varying and time repetitive textured background. The local Fourier transform is applied to construct a pixel-wise representation of local frequency components. We apply our method for object detection in moving background conditions. Experimental results of our frequency-based background model are evaluated both qualitatively and quantitatively.

Greedy Algorithm and Physics-Based Method for Active Contours and Surfaces: A Comparative Study

Deformable models, such as the discrete active contour and surface, imply the use of iterative evolution methods to perform 2D and 3D image segmentation. Among the several existing evolution methods, we focus on the greedy algorithm, which minimizes an energy functional, and the physics-based method, which applies forces in order to solve a dynamic differential equation. In this paper, we compare the greedy and physics-based approaches applied on 2D and 3D models, as regards overall speed and segmentation quality, quantified with an evaluating function mainly based on the mean distance between the model and the desired shape.

A Local Normal-Based Region Term for Active Contours

Global region-based active contours, like the Chan-Vese model, often make strong assumptions on the intensity distributions of the searched object and background, preventing their use in natural images. We introduce a more flexible local region energy achieving a trade-off between local features of gradient-like terms and global region features. Relying on the theory of parallel curves, we define our region term using constant length lines normal to the contour. Mathematical derivations are performed on an explicit curve, leading to a form allowing efficient implementation on a parametric snake. However, we provide implementations on both explicit and implicit contours.

Region tracking with narrow perception of background

We address the problem of object tracking within image sequences through region-based energy minimization. A common underlying assumption in region tracking is that color statistics can be confidently estimated in a global manner over object and background regions. This can be a drawback for tracking in real scenes with cluttered backgrounds, where statistical color data is highly scattered, preventing the estimation of reliable color statistics for object/background discrimination. To overcome this limitation, we propose an approach based on a narrow perception of background, which concentrates on the vicinity of tracked objects and thus extract more consistent statistical data for region separation. The benefits of our approach are demonstrated using two different statistical color models.