Abdessalam Benzinou

Variational shape matching for shape classification and retrieval

In this paper we define a multi-scale distance between shapes based on geodesics in the shape space. The proposed distance, robust to outliers, uses shape matching to compare shapes locally. The multi-scale analysis is introduced in order to address local and global variabilities. The resulting similarity measure is invariant to translation, rotation and scaling independently of constraints or landmarks, but constraints can be added to the approach formulation when needed. An evaluation of the proposed approach is reported for shape classification and shape retrieval on the part B of the MPEG-7 shape database. The proposed approach is shown to significantly outperform previous works and reaches 89.05% of retrieval accuracy and 98.86% of correct classification rate.

Autonomous agents for edge detection and continuity perception on otolith images

Abstract An automatic method for edge detection on biological images (otolith images) using a multi-agent system is presented. One of the major problems encountered during an automatic contour detection is the lack of structure continuity perception. In this paper we present a new approach to perceive continuity based on a 2D reconstruction of closed contours using a multi-agent system. Each agent is provided with sensors on the image, which allow it to follow local intensity extremes. The purpose is to detect alternative light and dark concentric structures in an image. To improve the detection of these reactive agents, we have added high-level information about the shape of the contour. An application to fish otolith growth ring detection is presented in this paper.

2D Image-based reconstruction of shape deformation of biological structures using a level-set representation

This paper copes with the reconstruction of accretionary growth sequence from images of biological structures depicting concentric ring patterns. Accretionary growth shapes are modeled as the level-sets of a potential function. Given an image of a biological structure, the reconstruction of the sequence of growth shapes is stated as a variational issue derived from geometric criteria. This variational setting exploits image-based information, in terms of the orientation field of relevant image structures, which leads to an original advection term. The resolution of this variational issue is discussed. Experiments on synthetic and real data are reported to validate the proposed approach.

An optimal elastic partial shape matching via shape geodesics

This paper presents a planar curve matching framework based on computing similarities between shape parts. We propose an elastic similarity measure issued from shape geodesics in the shape space. As the transition from global matching to partial matching leads to additional difficulties, we bypass them with a shape decomposition process based on the discrete curve evolution (DCE). This decomposition aims to obtain significant parts to match and it leads to a robust and efficient 2D shape matching algorithm. The comparison of the proposed method with the state of the art demonstrates its ability to handle elastic deformations leading to an overall optimal partial correspondence between shapes.

A multiagent system for edge detection and continuity perception on fish otolith images

We present an algorithm for fish otolith growth ring detection using a multiagent system. Up to now, the identification of growth rings, for age estimation, is routinely achieved by human readers, but this task is tedious and depends on the reader subjectivity. One of the major problems encountered during an automatic contour detection is the lack of ring continuity perception. We present an approach to improve this continuity perception based on a 2D reconstruction of rings using a multiagent system. The originality of the approach is to use local edge detection achieved by agents and combine it with continuity perception that active contours allow.

Parameterization of a multiagent system for roof edge detection: an application to growth ring detection on fish otoliths

In this paper we present a method of segmentation using a multiagent system, and an application to fish otolith growth ring detection. The otoliths images are composed of alternative concentric dark and light rings, the number of which increases with the age of the fish. Up to now, the identification of growth rings, for age estimation, is routinely achieved by human readers, but this task is tedious and depends on the readers subjectivity. The system proposed here is composed of several agents whose individual task is to detect local extremes on a grayscale image. For this aim the agents are provided with sensors on the gray levels of the image. By computing the mean gray level of two sensors placed in front of it, the agent, if it searches for light rings (respectively dark) will decide to turn in the direction of the lighter (respectively darker) sensor. The path of the agents has been tested as a roof edge detector, using the Canny criteria: good detection, good localization, and low multiple response, in order to choose the best parameters ruling the agents behavior, according to the image structures. Tests have been first achieved on synthetic images, and then on otoliths images.

Shape geodesics for boundary-based object recognition and retrieval

In this paper we define a distance between shapes based on geodesics in shape space. The proposed distance, robust to outliers, uses shape matching to compare shapes locally. Multiscale analysis is introduced in order to avoid problems of local and global variabilities. The resulting similarity measure is invariant to translation, rotation and scaling independently of constraints or landmarks, but constraints can be added to the approach formulation when needed. An evaluation of the proposed approach is reported for shape classification and retrieval on a complex benchmark shape database. It demonstrates in both cases that previous work is outperformed.

Robust time-frequency model estimation in otolith images for fish age and growth analysis

We present a robust method for time-frequency model estimation. It involves a robust Leclercs estimator to ensure robustness w.r.t. noise and interferences present in time- frequency representations. This scheme is applied to fish age and growth analysis from otolith images. This application involves the estimation of the parameters of a priori fish growth models using this robust time-frequency analysis. We present a quantitative experimental validation over a large set of real images of Plaice otoliths.

Geodesics-based statistical shape analysis

In this paper, we describe a statistical shape analysis founded on a robust elastic metric. The proposed metric is based on geodesics in the shape space. Using this distance, we formulate a variational setting to estimate intrinsic mean shape viewed as the perfect pattern to represent a set of given shapes. By applying a geodesic-based shape warping, we generate a principal component analysis (PCA) able to capture nonlinear shape variability. Indeed, the proposed approach better reflects the main modes of variability of the data. Therefore, characterizing dominant modes of individual shape variations is conducted well through the reconstruction process. We demonstrate the efficiency of our approach with an application on a GESTURES database.

Variational Level-Set Reconstruction of Accretionary Morphogenesis from Images

This paper copes with the reconstruction of accretionary morphogenesis within a given observation plane from an image depicting successive (typically seasonal or daily) growth structures. Modeling accretionary growth shapes as the level-sets of a potential function, a variational framework is derived from geometric criteria. It resorts to minimizing an energy functional involving two terms: a regularization term and a data-driven term which constrain the evolution of the shapes with respect to a growth orientation field. Experiments carried out on real data (e.g., fish otoliths) validate the proposed approach, which opens new research directions for information extraction and decoding from biological archives.