ISPRS Hannover workshop

Urban growth is an ongoing trend and one of its direct consequences is the development of buried utility networks. Locating these networks is becoming a challenging task. While the labeling of large objects in aerial images is extensively studied in Geosciences, the localization of small objects (smaller than a building) is in counter part less studied and very challenging due to the variance of object colors, cluttered neighborhood, non-uniform background, shadows and aspect ratios. In this paper, we put forward a method for the automatic detection and localization of manhole covers in Very High Resolution (VHR) aerial and remotely sensed images using a Convolutional Neural Network (CNN). Compared to other detection/localization methods for small objects, the proposed approach is more comprehensive as the entire image is processed without prior segmentation. The first experiments using the Prades-Le-Lez and Gigean datasets show that our method is indeed effective as more than 49% of the ground truth database is detected with a precision of 75 %. New improvement possibilities are being explored such as using information on the shape of the detected objects and increasing the types of objects to be detected, thus enabling the extraction of more object specific features

A keratin K5Cre transgenic line appropriate for tissue-specific or generalized Cre-mediated recombination

We describe here a mouse line bearing a bovine keratin K5Cre recombinase transgene. These mice showed a dual pattern of Cre-mediated recombination, depending on the parent transmitting the transgene. In paternal transmission, recombination occurred specifically in the skin and stratified epithelia-as expected according to the expression of endogenous keratin K5. However, constitutive recombination between loxP sites transmitted by the sperm took place when the mother possessed the K5Cre transgene, even when the transgene was absent in the progeny. Cre expression in late-stage oocytes, with the Cre protein persisting into the developing embryo, leads to the constitutive recombination observed. Thus, this transgenic line allows for both tissue-specific and generalized recombination, depending on the breeding scheme. (C) 2004 Wiley-Liss, Inc

Keratin K5Cre transgenic line appropriate for sissue-specific or generalized Cre-mediated recombination

6 páginas, 4 figuras, 1 tabla -- PAGS nros. 52-57We describe here a mouse line bearing a bovine keratin K5Cre recombinase transgene. These mice showed a dual pattern of Cre-mediated recombination, depending on the parent transmitting the transgene. In paternal transmission, recombination occurred specifically in the skin and stratified epithelia—as expected according to the expression of endogenous keratin K5. However, constitutive recombination between loxP sites transmitted by the sperm took place when the mother possessed the K5Cre transgene, even when the transgene was absent in the progeny. Cre expression in late-stage oocytes, with the Cre protein persisting into the developing embryo, leads to the constitutive recombination observed. Thus, this transgenic line allows for both tissue-specific and generalized recombination, depending on the breeding schemethe Medical Research Council (Ph.D. studentship to D.W.), the Spanish Ministry of Science and Technology (to J.L.J.) La Ligue National contre le Cancer and l’ARC (France) (to A.G.), Cancer Research UK (to D.W.M.)Peer reviewe

Evaluation of deep learning and conventional approaches for image steganalysis

Steganography is the technique that's used to embed secret messages into digital media without changing their appearances. As a countermeasure to steganography, steganalysis detects the presence of hidden data in a digital content. For the last decade, the majority of image steganalysis approaches can be formed by two stages. The first stage is to extract effective features from the image content and the second is to train a classifier in machine learning by using the features from stage one. Ultimately the image steganalysis becomes a binary classification problem. Since Deep Learning related architecture unify these two stages and save researchers lots of time designing hand-crafted features, the design of a CNN-based steganalyzer has therefore received increasing attention over the past few years. In this paper, we will examine the development in image steganalysis, both in spatial domain and in JPEG domain, and discuss the future directions