48 research outputs found
Subspace segmentation with a minimal square frobenius norm representation
We introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly. In the noisy case, we show that on both synthetic and real-word datasets, MSFNR is much faster than most state-of-the-art methods while achieving comparable segmentation accuracy.published_or_final_versio
Discovering useful parts for pose estimation in sparsely annotated datasets
Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images. Discovered parts are used to generate more accurate appearance likelihoods for traditional part-based models like Pictorial Structures and its derivatives. Our experiments on images of a hawkmoth in flight show that our proposed approach significantly improves over existing work for this application, while also being more generally applicable. Our proposed approach localizes landmarks at least twice as accurately as a baseline based on a Mixture of Pictorial Structures (MPS) model. Our unique High-Resolution Moth Flight (HRMF) dataset is made publicly available with annotations.https://arxiv.org/abs/1605.00707Accepted manuscrip
Optical flow sensing and the inverse perception problem for flying bats
The movements of birds, bats, and other flying species are governed by complex sensorimotor systems that allow the animals to react to stationary environmental features as well as to wind disturbances, other animals in nearby airspace, and a wide variety of unexpected challenges. The paper and talk will describe research that analyzes the three-dimensional trajectories of bats flying in a habitat in Texas. The trajectories are computed with stereoscopic methods using data from synchronous thermal videos that were recorded with high temporal and spatial resolution from three viewpoints. Following our previously reported work, we examine the possibility that bat trajectories in this habitat are governed by optical flow sensing that interpolates periodic distance measurements from echolocation. Using an idealized geometry of bat eyes, we introduce the concept of time-to-transit, and recall some research that suggests that this quantity is computed by the animals' visual cortex. Several steering control laws based on time-to-transit are proposed for an idealized flight model, and it is shown that these can be used to replicate the observed flight of what we identify as typical bats. Although the vision-based motion control laws we propose and the protocols for switching between them are quite simple, some of the trajectories that have been synthesized are qualitatively bat-like. Examination of the control protocols that generate these trajectories suggests that bat motions are governed both by their reactions to a subset of key feature points as well by their memories of where these feature points are located
Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition
Despite the high biometric performance, finger-vein recognition systems are
vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we
present a new and robust approach for detecting presentation attacks on
finger-vein biometric systems exploiting the 3D Shape (normal-map) and material
properties (diffuse-map) of the finger. Observing the normal-map and
diffuse-map exhibiting enhanced textural differences in comparison with the
original finger-vein image, especially in the presence of varying illumination
intensity, we propose to employ textural feature-descriptors on both of them
independently. The features are subsequently used to compute a separating
hyper-plane using Support Vector Machine (SVM) classifiers for the features
computed from normal-maps and diffuse-maps independently. Given the scores from
each classifier for normal-map and diffuse-map, we propose sum-rule based score
level fusion to make detection of such presentation attack more robust. To this
end, we construct a new database of finger-vein images acquired using a custom
capture device with three inbuilt illuminations and validate the applicability
of the proposed approach. The newly collected database consists of 936 images,
which corresponds to 468 bona fide images and 468 artefact images. We establish
the superiority of the proposed approach by benchmarking it with classical
textural feature-descriptor applied directly on finger-vein images. The
proposed approach outperforms the classical approaches by providing the Attack
Presentation Classification Error Rate (APCER) & Bona fide Presentation
Classification Error Rate (BPCER) of 0% compared to comparable traditional
methods.Comment: This work was accepted in The 15th International Conference on SIGNAL
IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS, 201