The abstract, multidimensional varieties and their classification

We define abstract multidimensional variety without borders, using the investigation of the complex of multi-ary relations (H. Martini and P. Soltan, 2003 [3]) and the notion of compact, combinatorial, multidimensional variety without borders (V. G. Boltyanski and V. A. Efrimovici, 1982 [1]). We indicate the classification of this kind of varieties similarly to the results of classification of compact, two-dimensional surfaces without borders (V. G. Boltyanski and V. A. Efrimovici, 1982 [1]). We use varieties' genders (modulo Euler characteristic (V. G. Boltyanski, 1995 [2])) to classify them

Object detection via a multi-region & semantic segmentation-aware CNN model

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.Comment: Extended technical report -- short version to appear at ICCV 201

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research