Robust Facial Features Tracking Using Geometric Constraints and Relaxation

International audienceThis work presents a robust technique for tracking a set of detected points on a human face. Facial features can be manually selected or automatically detected. We present a simple and efficient method for detecting facial features such as eyes and nose in a color face image. We then introduce a tracking method which, by employing geometric constraints based on knowledge about the configuration of facial features, avoid the loss of points caused by error accumulation and tracking drift. Experiments with different sequences and comparison with other tracking algorithms, show that the proposed method gives better results with a comparable processing time

Matching Local Invariant Features with Contextual Information : an Experimental Evaluation

The main advantage of using local invariant features is their local character which yields robustness to occlusion and varying background. Therefore, local features have proved to be a powerful tool for finding correspondences between images, and have been employed in many applications. However, the local character limits the descriptive capability of features descriptors, and local features fail to resolve ambiguities that can occur when an image shows multiple similar regions. Considering some global information will clearly help to achieve better performances. The question is which information to use and how to use it. Context can be used to enrich the description of the features, or used in the matching step to filter out mismatches. In this paper, we compare different recent methods which use context for matching and show that better results are obtained if contextual information is used during the matching process. We evaluate the methods in two applications: wide baseline matching and object recognition, and it appears that a relaxation based approach gives the best results

Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

In this paper, we present a new color image regularization method using a rotating smoothing filter. This approach combines a pixel classification method, which roughly determines if a pixel belongs to a homogenous region or an edge with an anisotropic perceptual edge detector capable of computing two precise diffusion directions. Using a now classical formulation, image regularization is here treated as a variational model, where successive iterations of associated PDE (Partial Differential Equation) are equivalent to a diffusion process. Our model uses two kinds of diffusion: isotropic and anisotropic diffusion. Anisotropic diffusion is accurately controlled near edges and corners, while isotropic diffusion is applied to smooth regions either homogeneous or corrupted by noise. A comparison of our approach with other regularization methods applied on real images demonstrate that our model is able to efficiently restore images as well as handle diffusion, and at the same time preserve edges and corners well

Matching Local Invariant Features: How Can Contextual Information Help?

International audienceLocal invariant features are a powerful tool for finding correspondences between images since they are robust to cluttered background, occlusion and viewpoint changes. However, they suffer the lack of global information and fail to resolve ambiguities that can occur when an image has multiple similar regions. Considering some global information will clearly help to achieve better performances. The question is which information to use and how to use it. While previous approaches use context for description, this paper shows that better results are obtained if contextual information is included in the matching process. We compare two different methods which use context for matching and experiments show that a relaxation based approach gives better results

A simple and efficient eye detection method in color images

International audienceIn this paper we propose a simple and efficient eye detection method for face detection tasks in color images. The algorithm first detects face regions in the image using a skin color model in the normalized RGB color space. Then, eye candidates are extracted within these regions. Finally, using the anthrophological characteristics of human eyes, the pairs of eye regions are selected. The proposed method is simple and fast, since it needs no template matching step for face verification. It is robust because it can deals with face rotation. Experimental results show the validity of our approach, a correct eye detection rate of 98.4% is achieved using a subset of the AR face database

RSD-DOG : A New Image Descriptor based on Second Order Derivatives

International audienceThis paper introduces the new and powerful image patch de-scriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order fea-tures/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC

A Novel Image Descriptor Based on Anisotropic Filtering

International audienceIn this paper, we present a new image patch descriptor for object detection and image matching. The descriptor is based on the standard HoG pipeline. The descriptor is generated in a novel way, by embedding the response of an oriented anisotropic derivative half Gaussian kernel in the Histogram of Orientation Gradient (HoG) framework. By doing so, we are able to bin more curvature information. As a result, our descriptor performs better than the state of art descriptors such as SIFT, GLOH and DAISY. In addition to this, we repeat the same procedure by replacing the anisotropic derivative half Gaussian kernel with a compu-tationally less complex anisotropic derivative half exponential kernel and achieve similar results. The proposed image descriptors using both the kernels are very robust and shows promising results for variations in brightness, scale, rotation, view point, blur and compression. We have extensively evaluated the effectiveness of the devised method with various challenging image pairs acquired under varying circumstances

MESSIE : un systeme multi specialistes en vision. Application a l'interpretation en imagerie aerienne

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Thin nets and Crest lines : Application to Satellite Data and Medical Images

Projet SYNTIMIn this paper, we describe a new approach for extracting {\em thin nets} in grey level images. The key point of our approach is to model thin nets as the crest lines of the image surface. Crest lines are the lines where the magnitude of the maximum curvature is locally maximum in the corresponding principal direction. We define these lines using first, second and third derivatives of the image. We compute the image derivatives using recursive filters approximating the Gaussian filter and its derivatives. Using an adapted scale factor, we apply this approach to the extraction of roads in satellite data and blood vessels in medical images. We also apply this method to the extraction of the crest lines in depth maps of human faces

Morphology of the gas-rich debris disk around HD 121617 with SPHERE observations in polarized light

Debris disks are the signposts of collisionally eroding planetesimal circumstellar belts, whose study can put important constraints on the structure of extrasolar planetary systems. The best constraints on the morphology of disks are often obtained from spatially resolved observations in scattered light. Here, we investigate the young (~16 Myr) bright gas-rich debris disk around HD121617. We use new scattered-light observations with VLT/SPHERE to characterize the morphology and the dust properties of this disk. From these properties we can then derive constraints on the physical and dynamical environment of this system, for which significant amounts of gas have been detected. The disk morphology is constrained by linear-polarimetric observations in the J band. Based on our modeling results and archival photometry, we also model the SED to put constraints on the total dust mass and the dust size distribution. We explore different scenarios that could explain these new constraints. We present the first resolved image in scattered light of the debris disk HD121617. We fit the morphology of the disk, finding a semi-major axis of 78.3$\pm$0.2 au, an inclination of 43.1$\pm$0.2{\deg} and a position angle of the major axis with respect to north, of 239.8$\pm$0.3{\deg}, compatible with the previous continuum and CO detection with ALMA. Our analysis shows that the disk has a very sharp inner edge, possibly sculpted by a yet-undetected planet or gas drag. While less sharp, its outer edge is steeper than expected for unperturbed disks, which could also be due to a planet or gas drag, but future observations probing the system farther from the main belt would help explore this further. The SED analysis leads to a dust mass of 0.21$\pm$0.02 M$_{\oplus}$ and a minimum grain size of 0.87$\pm$0.12 $\mu$m, smaller than the blowout size by radiation pressure, which is not unexpected for very bright col...Comment: 12 pages, 7 figures. Accepted in A&A (06/02/2023