Abstract — We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a generalpurpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations— partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels. I.

The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words ” and treat them using text search approaches following the “bag of features ” paradigm. In this paper, we explore analogous approaches in the 3D world applied to the problem of non-rigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words”, we construct compact and informative shape descriptors by means of the “bag of features ” approach. We also show that considering pairs of “geometric words ” (“geometric expressions”) allows creating spatially-sensitive bags of features with better discriminativity. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark.

We present an efficient algorithm for continuous image recognition and feature descriptor tracking in video which operates by reducing the search space of possible interest points inside of the scale space image pyramid. Instead of performing tracking in 2D images, we search and match candidate features in local neighborhoods inside the 3D image pyramid without computing their feature descriptors. The candidates are further validated by fitting to a motion model. The resulting tracked interest points are more repeatable and resilient to noise, and descriptor computation becomes much more efficient because only those areas of the image pyramid that contain features are searched. We demonstrate our method on real-time object recognition and label augmentation running on a mobile device. 1.

In this paper we propose a novel computational method to infer visual saliency in images. The method is based on the idea that salient objects should have local characteristics that are different than the rest of the scene, being edges, color or shape. By using a novel operator, these characteristics are combined to infer global information. The obtained information is used as a weighting for the output of a segmentation algorithm so that the salient object in the scene can easily be distinguished from the background. The proposed approach is fast and it does not require any learning. The experimentation shows that the system can enhance interesting objects in images and it is able to correctly locate the same object annotated by humans with an F-measure of 85.61 % when the object size is known, and 79.19 % when the object size is unknown, improving the state of the art performance on a public dataset. 1.

We present and evaluate various methods for purely automated attacks against click-based graphical passwords. Our purely automated methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention. Our method results in a significantly better automated attack than previous work, guessing 8-15 % of passwords for two representative images using dictionaries of less than 2 24.6 entries, and about 16 % of passwords on each of these images using dictionaries of less than 2 31.4 entries (where the full password space is 2 43). Relaxing our click-order pattern substantially increased the efficacy of our attack albeit with larger dictionaries of 2 34.7 entries, allowing attacks that guessed 48-54 % of passwords (compared to previous results of 0.9 % and 9.1 % on the same two images with 2 35 guesses). These latter automated attacks are independent of focus-of-attention models, and are based on imageindependent guessing patterns. Our results show that automated attacks, which are easier to arrange than humanseeded attacks and are more scalable to systems that use multiple images, pose a significant threat. 1

In this paper, we address the problem of detecting partial symmetries in 3D objects. In contrast to previous work, our algorithm is able to match deformed symmetric parts: We first develop an algorithm for the case of approximately isometric deformations, based on matching graphs of surface feature lines that are annotated with intrinsic geometric properties. The sensitivity to non-isometry is controlled by tolerance parameters for each such annotation. Using large tolerance values for some of these annotations and a robust matching of the graph topology yields a more general symmetry detection algorithm that can detect similarities in structures that have undergone strong deformations. This approach for the first time allows for detecting partial intrinsic as well as more general, non-isometric symmetries. We evaluate the recognition performance of our technique for a number

This paper introduces a novel interaction technique for handheld mobile devices which enables the user interface to be controlled by the motion of the user’s hand. A feature-based approach is proposed for global motion estimation that exploits gradient measures for both feature selection and feature motion uncertainty analysis. A voting-based scheme is presented for outlier removal. A Kalman filter is applied for smoothing motion trajectories. A fixed-point implementation of the method was developed due to the lack of floating-point hardware. Experiments testify the effectiveness of the approach on a camera-enabled mobile phone. Key words: user interfaces, handheld devices, global motion estimation 1

Abstract. We describe the object retrieval task of ImageCLEF 2007, give an overview of the methods of the participating groups, and present and discuss the results. The task was based on the widely used PASCAL object recognition data to train object recognition methods and on the IAPR TC-12 benchmark dataset from which images of objects of the ten different classes bicycles, buses, cars, motorbikes, cats, cows, dogs, horses, sheep, and persons had to be retrieved. Seven international groups participated using a wide variety of methods. The results of the evaluation show that the task was very challenging and that different methods for relevance assessment can have a strong influence on the results of an evaluation. 1

Abstract – This paper explores the possibilities to use robust object tracking algorithms based on visual model features as generator of visual references for UAV control. A Scale Invariant Feature Transform (SIFT) algorithm is used for detecting the salient points at every processed image, then a projective transformation for evaluating the visual references is obtained using a version of the RANSAC algorithm, in which a series of matched key-points pairs that fulfill the transformation equations are selected, rejecting otherwise the corrupted data. The system has been tested using diverse image sequences showing its capability to track objects significantly changed in scale, position, rotation, generating at the same time velocity references to the UAV flight controller. The robustness our approach has also been validated using images taken from real flights showing noise and lighting distortions. The results presented are promising in order to be used as reference generator for the control system.

In this paper we address the problem of removing unwanted image content in a single orthographic facade image. We exploit the regular structure present in building facades and introduce propagation process that is guided by the symmetry prevalent in the image. It removes larger unwanted image objects such as traffic lights, street signs, or cables as well as smaller noise, such as reflections in the windows. The output is intended as source for textures in urban reconstruction projects.