Recently, adversarial examples become one of the most dangerous risks in deep learning, which affects applications of real world such as robotics, cyber-security and computer vision. In image classification, adversarial attacks showed the ability to fool classifiers with small imperceptible perturbations added to the input. In this paper, we present an efficient defense mechanism, we call DVAE-SR that combine variational autoencoder and super-resolution to eliminate adversarial perturbation from image input before feeding it to the CNN classifier. The DVAE-SR can successfully defend against both white-box and black-box attacks without retraining CNN classifier and it recovers better accuracy than Defense-GAN and Defense-VAE.
Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose.
The 3D coronary vessels can be reconstructed by means of different cardiac imaging modalities. Two of the most widely used modalities for the purpose of coronary tree reconstruction are intravascular ultrasounds (IVUS) and biplane angiography. Current 3D vessel reconstruction based on IVUS pullback imaging is limited by the lack of information about the real vessel curvature, because the path of the catheter is assumed to be a straight line. This limitation can be overcome if information from an IVUS sequence is fused with a biplane X-ray image of the catheter acquired at the start of the pullback procedure. This work focuses on the reconstruction of the catheter path from biplane angiograms. This reconstruction represents the 3D path followed by the catheter inside the vessel of interest. While other approaches reconstruct the vessel after it has been segmented in both images independently, our approach, based on the snakes technique, allows us to segment and reconstruct the catheter trajectory merging information from both images simultaneously. The result is a more robust reconstruction since 3D constraints can be used and no correspondence of points between the projections is required. This reconstruction will allow a posterior more exact combination of IVUS and biplane angiography image modalities.
In this paper, we introduce a new segmentation technique (called Kohonen snake) based on the neural simulation of deformable models designed to reconstruct 3D objects. Kohonen snake possesses all properties of Kohonen networks (lateral interaction during the learning process, topologically preserving mapping) and of deformable models (namely, elastic properties). Elastic properties of the physics-based Kohonen ring improves the shortcomings of the Kohonen network related to twisting, `dead' neurons, accumulation and rounding the network, whereas the data- driven approach of Kohonen snake improves the problem of initialization and local minima of the snakes. When integrating both models, the first question is how to combine their parameters. We simulate the Kohonen snake behavior with different parameter values using sequential and parallel weight updating, study the need of decreasing the parameters and of reordering image features. As a result, we conclude that Kohonen snake has better control on its shape that makes it less dependent on the values of its parameters and initial conditions. Our tests on segmentation of synthetic and real images illustrate the usefulness of the Kohonen snake technique.
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