Unsupervised Camera Motion Estimation and Moving Object Detection in Videos

In this article, we consider the robust estimation of a location parameter using M-estimators. We propose here to couple this estimation with the robust scale estimate proposed in [Dahyot and Wilson, 2006]. The resulting procedure is then completely unsupervised. It is applied to camera motion estimation and moving object detection in videos. Experimental results on different video materials show the adaptability and the accuracy of this new robust approach

Entropic Regularisation of Robust Optimal Transport

Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target). It was shown to be very competitive to alternative solutions based on Optimal Transport for colour transfer. We show that in fact Grogan et al’s formulation can also be understood as a new robust Optimal Transport based framework with entropy regularisation over marginals

Robust ellipse detection with Gaussian mixture models

The Euclidian distance between Gaussian Mixtures has been shown to be robust to perform point set registration (Jian and Vemuri, 2011). We propose to extend this idea for robustly matching a family of shapes (ellipses). Optimisation is performed with an annealing strategy, and the search for occurrences is repeated several times to detect multiple instances of the shape of interest. We compare experimentally our approach to other state-of-the-art techniques on a benchmark database for ellipses, and demonstrate the good performance of our approach

Mean shift algorithm for robust rigid registration between Gaussian Mixture Models

We present a Mean shift (MS) algorithm for solving the rigid point set transformation estimation [1]. Our registration algorithm minimises exactly the Euclidean distance between Gaussian Mixture Models (GMMs). We show experimentally that our algorithm is more robust than previous implementations [1], thanks to both using an annealing framework (to avoid local extrema) and using variable bandwidths in our density estimates. Our approach is applied to 3D real data sets captured with a Lidar scanner and Kinect sensor

Denoising RENOIR Image Dataset with DBSR

Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset