Noncombinatorial detection of regular repetitions under perspective skew

We present a geometric framework for the efficient detection of regular repetitions of planar (but not necessarily coplanar) patterns. At the heart of our system, lie the fixed structures of the transformations that describe these regular configurations. The approach detects a number of symmetric configurations that have traditionally been dealt with separately, in that all configurations corresponding to planar homologies are detected. These include important cases such as periodicities, mirror symmetries, and reflections about a point. The approach can handle perspective distortions. It avoids to get trapped in combinatorics; through invariant-based hashing for pattern matching and through Hough transforms for the detection of fixed structures. Additional efficiency and robustness are obtained from the system's ability to "reason" about the consistency of multiple homologies. The performance of the system is demonstrated with several examples.     

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informativeness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because (i) it re-uses the same features extracted for detection, (ii) it aggregates features using integral images, and (iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that DeepProposals outperform most of the previous object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.     

The influence of curing conditions on the mechanical properties and leaching of inorganic polymers made of fayalitic slag

This study reports on the impact of the curing conditions on the mechanical properties and leaching of inorganic polymer (IP) mortars made from a water quenched fayalitic slag. Three similar IP mortars were produced by mixing together slag, aggregate and activating solution, and cured in three different environments for 28 d: a) at 20 °C and relative humidity (RH) ~ 50% (T20RH50), b) at 20 °C and RH≥90% (T20RH90) and c) at 60 °C and RH ~ 20% (T60RH20). Compressive strength (EN 196-1) varied between 19 MPa (T20RH50) and 31 MPa (T20RH90). This was found to be attributed to the cracks formed upon curing. Geochemical modelling and two leaching tests were performed, the EA NEN 7375 tank test, and the BS EN 12457-1 single batch test. Results show that Cu, Ni, Pb, Zn and As leaching occurred even at high pH, which varied between 10 and 11 in the tank test’s leachates and between 12 and 12.5 in the single batch’s leachates. Leaching values obtained were below the requirements for non-shaped materials of Flemish legislation for As, Cu and Ni in the single batch test.

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Shape-from-recognition: Recognition enables meta-data transfer

Low-level cues in an image not only allow to infer higher-level information like the presence of an object, but the inverse is also true. Category-level object recognition has now reached a level of maturity and accuracy that allows to successfully feed back its output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer different kinds of meta-data annotations for images of previously unseen object instances, in particular information on 3D shape. Meta-data can be discrete, real- or vector-valued. Our approach builds on the Implicit Shape Model of Leibe and Schiele [B. Leibe, A. Leonardis, B. Schiele, Robust object detection with interleaved categorization and segmentation, International Journal of Computer Vision 77 (1–3) (2008) 259–289], and extends it to transfer annotations from training images to test images. We focus on the inference of approximative 3D shape information about objects in a single 2D image. In experiments, we illustrate how our method can infer depth maps, surface normals and part labels for previously unseen object instances.     

Vision Based Intelligent Wheel Chair Control: The Role of Vision and Inertial Sensing in Topological Navigation

This paper describes ongoing research on vision based mobile robot navigation for wheel chairs. After a guided tour through a natural environment while taking images at regular time intervals, natural landmarks are extracted to automatically build a topological map. Later on this map can be used for place recognition and navigation. We use visual servoing on the landmarks to steer the robot. In this paper, we investigate ways to improve the performance by incorporating inertial sensors. © 2004 Wiley Periodicals, Inc.     

Unsupervised Object Discovery: A Comparison

The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.     

Cross-Media Alignment of Names and Faces

In this paper we report on our experiments on aligning names and faces as found in images and captions of online news Websites. Developing accurate technologies for linking names and faces is valuable when retrieving or mining information from multimedia collections. We perform exhaustive and systematic experiments exploiting the (a)symmetry between the visual and textual modalities. This leads to different schemes for assigning names to the faces, assigning faces to the names, and establishing name-face link pairs. On top of that, we investigate generic approaches to the use of textual and visual structural information to predict the presence of the corresponding entity in the other modality. The proposed methods are completely unsupervised and are inspired by methods for aligning phrases and words in texts of different languages developed for constructing dictionaries for machine translation. The results are competitive with state-of-the-art performance on the ?Labeled Faces in the Wild? dataset in terms of recall values, now reported on the complete dataset, include excellent precision values, and show the value of text and image analysis for identifying the probability of being pictured or named in the alignment process.     

Finding a needle in a haystack: an interactive video archive explorer for professional video searchers

Professional video searchers typically have to search for particular video fragments in a vast video archive that contains many hours of video data. Without having the right video archive exploration tools, this is a difficult and time consuming task that induces hours of video skimming. We propose the video archive explorer, a video exploration tool that provides visual representations of automatically detected concepts to facilitate individual and collaborative video search tasks. This video archive explorer is developed by employing a user-centred methodology, which ensures that the tool is more likely to fit to the end user needs. A qualitative evaluation with professional video searchers shows that the combination of automatic video indexing, interactive visualisations and user-centred design can result in an increased usability, user satisfaction and productivity.