We introduce a complete pipeline for recognizing and classifying people's clothing in natural scenes. This has several interesting applications, including e-commerce, event and activity recognition, online advertising, etc. The stages of the pipeline combine a number of state-of-the-art building blocks such as upper body detectors, various feature channels and visual attributes. The core of our method consists of a multi-class learner based on a Random Forest that uses strong discriminative learners as decision nodes. To make the pipeline as automatic as possible we also integrate automatically crawled training data from the web in the learning process. Typically, multi-class learning benefits from more labeled data. Because the crawled data may be noisy and contain images unrelated to our task, we extend Random Forests to be capable of transfer learning from different domains. For evaluation, we define 15 clothing classes and introduce a benchmark data set for the clothing classification task consisting of over 80, 000 images, which we make publicly available. We report experimental results, where our classifier outperforms an SVM baseline with 41.38 % vs 35.07 % average accuracy on challenging benchmark data.

International audienceThis paper investigates the use of color information when used within a state-of-the-art large scale image search system. We introduce a simple yet effective and efficient color signature generation procedure. It is used either to produce global or local descriptors. As a global descriptor, it outperforms several state-of-the-art color description methods, in particular the bag-of-words method based on color SIFT. As a local descriptor, our signature is used jointly with SIFT descriptors (no color) to provide complementary information. This significantly improves the recognition rate, outperforming the state of the art on two image search benchmarks. We will provide an open source package of our signature

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Abstract. As of today endoscopes have been only used as a keyhole to look inside the human body. Our goal is to enhance the endoscope to a full imaging device providing better quantitative and qualitative data. Possible applications for such an enhanced endoscope are referencing, navigation and 3D visualization during endoscopic surgery. To obtain accurate results, a reliable and fully automatic calibration method for the endoscopic camera has been developed which can be used within the operating room (OR). Special care has been taken to ensure robustness against inevitable distortions and inhomogeneous illumination.

Accurate patient registration and referencing is a key element in navigated surgery. Unfortunately all existing methods are either invasive or very time consuming. We propose a fully non-invasive optical approach using a tracked monocular endoscope to reconstruct the surgical scene in 3D using photogrammetric methods. The 3D reconstruction can then be used for matching the pre-operative data to the intra-operative scene. In order to cope with the near real-time requirements for referencing, we use a novel, efficient 3D point management method during 3D model reconstruction. The presented prototype system provides a reconstruction accuracy of 0.1 mm and a tracking accuracy of 0.5 mm on phantom data. The ability to cope with real data is demonstrated by cadaver experiments.