Learning sparse representations of depth

This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page

Dictionary learning with large step gradient descent for sparse representations

This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238. The final publication is available at link.springer.com http://www.springerlink.com/content/l1k4514765283618

Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons

Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simulating self-motion in an underwater scene. Local retinal motion was estimated with a motion detector and encoded in four populations of directionally tuned retinal ganglion cells, represented as two signed input variables. This activity was then used as input into one of two learning networks: a sparse coding network (competitive learning) and backpropagation network (supervised learning). Both simulations develop specificities for optic flow which are comparable to those found in a neurophysiological study (Kubo et al. 2014), and relative frequencies of the various neuronal responses are best modeled by the sparse coding approach. We conclude that the optic flow neurons in the zebrafish pretectum do reflect the optic flow statistics. The predicted vectorial receptive fields show typical optic flow fields but also "Gabor" and dipole-shaped patterns that likely reflect difference fields needed for reconstruction by linear superposition.Comment: Published Conference Paper from ICANN 2018, Rhode

Pattern recognition, attention, and information bottlenecks in the primate visual system

In its evolution, the primate visual system has developed impressive capabilities for recognizing complex patterns in natural images. This process involves many stages of analysis and a variety of information processing strategies. This paper concentrates on the importance of 'information bottlenecks,' which restrict the amount of information that can be handled at different stages of analysis. These steps are crucial for reducing the overwhelming computational complexity associated with recognizing countless objects from arbitrary viewing angles, distances, and perspectives. The process of directed visual attention is an especially important information bottleneck because of its flexibility in determining how information is routed to high-level pattern recognition centers

A high-quality video denoising algorithm based on reliable motion estimation

11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IIIAlthough the recent advances in the sparse representations of images have achieved outstanding denosing results, removing real, structured noise in digital videos remains a challenging problem. We show the utility of reliable motion estimation to establish temporal correspondence across frames in order to achieve high-quality video denoising. In this paper, we propose an adaptive video denosing framework that integrates robust optical flow into a non-local means (NLM) framework with noise level estimation. The spatial regularization in optical flow is the key to ensure temporal coherence in removing structured noise. Furthermore, we introduce approximate K-nearest neighbor matching to significantly reduce the complexity of classical NLM methods. Experimental results show that our system is comparable with the state of the art in removing AWGN, and significantly outperforms the state of the art in removing real, structured noise

Greater Motor Evoked Torque in ACLR Patients during Force Reproduction Task Compared to Health Controls

Persistent quadriceps dysfunction following anterior cruciate ligament reconstruction (ACLR) may lead to further pathological complication. Quadriceps weakness has been linked to corticospinal excitability. However, it remains unclear how this altered corticospinal excitability contributes to ACLR patients during knee strength tasks when compared to healthy controls. PURPOSE: The purpose of this study was to examine force reproduction strategies during isometric knee extension between ACLR patients and healthy controls. METHODS: Five ACLR (20.40±1.67yrs, 72.12±12.87kg, 171.07±7.40cm) participants and five matched healthy controls (21.00±1.73yrs, 65.77±13.61kg, 166.62±11.99cm) performed an isometric force reproduction task. They were instructed to maintain 10% of maximal voluntary isometric contraction (MVIC) in response to unexpected Transcranial Magnetic Stimulation (TMS) over the primary motor cortex, targeting the quadriceps. The TMS stimulations were randomly delivered at two different intensities: 120% and 140% active motor threshold (AMT). Additionally, resting twitch torque (RTT) was measured by delivering TMS stimulations at 100% intensity over the quadriceps. Motor evoked torque (MET, %) was calculated by normalizing the 120% and 140% peak change relative to 10% MVIC by RTT values. Comparisons were made using 2-way ANOVAs with one within factor (intensity, 2 levels) and one between factor (group, 2 levels). RESULTS: A significant TMS intensity by group interaction was observed for MET (F[1,8] = 18.639, p = 0.003). The ACLR group had higher MET than the control group at AMT 140% (196.12±40.83 vs 106.69±34.01%, p = 0.006), while there was no difference at 120% (117.19±36.72 vs 69.06±44.18%, p = 0.098). CONCLUSION: The ACLR group produced similar torque changes to the CONT group at 120% of AMT, but more torque changes at the higher intensity. This may indicate protective neural adaptations responsible for force production, particularly at the corticospinal tract. However, this altered corticospinal excitability may also cause heightened quadriceps contraction during high-intensity tasks, potentially resulting in anterior ACL translation, which could put stress on the ACL and increase the risk of re-tear

Riemannian Sparse Coding for Positive Definite Matrices

International audienceInspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extended. Prior works have approached this problem by defining a sparse coding loss function using either extrinsic similarity measures (such as the log-Euclidean distance) or kernelized variants of statistical measures (such as the Stein divergence, Jeffrey's divergence, etc.). In contrast, we propose to use the intrinsic Riemannian distance on the manifold of SPD matrices. Our main contribution is a novel mathematical model for sparse coding of SPD matrices; we also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets showcase superior classification and retrieval performance compared with state-of-the-art approaches

Pattern recognition, attention, and information bottlenecks in the primate visual system

In its evolution, the primate visual system has developed impressive capabilities for recognizing complex patterns in natural images. This process involves many stages of analysis and a variety of information processing strategies. This paper concentrates on the importance of 'information bottlenecks,' which restrict the amount of information that can be handled at different stages of analysis. These steps are crucial for reducing the overwhelming computational complexity associated with recognizing countless objects from arbitrary viewing angles, distances, and perspectives. The process of directed visual attention is an especially important information bottleneck because of its flexibility in determining how information is routed to high-level pattern recognition centers