Efficient hardware architectures for MPEG-4 core profile

Efficient hardware acceleration architectures are proposed for the most demandingMPEG-4 core profile algorithms, namely; texture motion estimation (TME), binary motion estimation (BME)and the shape adaptive discrete cosine transform (SA-DCT). The proposed ME designs may also be used for H.264, since both architectures can handle variable block sizes. Both ME architectures employ early termination techniques that reduce latency and save needless memory accesses and power consumption. They also use a pixel subsampling technique to facilitate parallelism, while balancing the computational load. The BME datapath also saves operations by using Run Length Coded (RLC) pixel addressing. The SA-DCT module has a re-configuring multiplier-less serial datapath using adders and multiplexers only to improve area and power. The SA-DCT packing steps are done using a minimal switching addressing scheme with guarded evaluation. All three modules have been synthesised targeting the WildCard-II FPGA benchmarking platform adopted by the MPEG-4 Part9 reference hardware group

Stellar Open Clusters' Membership Probabilities: an N-Dimensional Geometrical Approach

We present a new geometrical method aimed at determining the members of open clusters. The methodology estimates, in an N-dimensional space, the membership probabilities by means of the distances between every star and the cluster central overdensity. It can handle different sets of variables, which have to satisfy the simple condition of being more densely distributed for the cluster members than for the field stars (as positions, proper motions, radial velocities and/or parallaxes are). Unlike other existing techniques, this fact makes the method more flexible and so can be easily applied to different datasets. To quantify how the method identifies the clus- ter members, we design series of realistic simulations recreating sky regions in both position and proper motion subspaces populated by clusters and field stars. The re- sults, using different simulated datasets (N = 1, 2 and 4 variables), show that the method properly recovers a very high fraction of simulated cluster members, with a low number of misclassified stars. To compare the goodness of our methodology, we also run other existing algorithms on the same simulated data. The results show that our method has a similar or even better performance than the other techniques. We study the robustness of the new methodology from different subsamplings of the ini- tial sample, showing a progressive deterioration of the capability of our method as the fraction of missing objects increases. Finally, we apply all the methodologies to the real cluster NGC 2682, indicating that our methodology is again in good agreement with preceding studies.Comment: 15 pages, 9 figures, 6 tables, accepted for publication in MNRA

Confidence sets for split points in decision trees

We investigate the problem of finding confidence sets for split points in decision trees (CART). Our main results establish the asymptotic distribution of the least squares estimators and some associated residual sum of squares statistics in a binary decision tree approximation to a smooth regression curve. Cube-root asymptotics with nonnormal limit distributions are involved. We study various confidence sets for the split point, one calibrated using the subsampling bootstrap, and others calibrated using plug-in estimates of some nuisance parameters. The performance of the confidence sets is assessed in a simulation study. A motivation for developing such confidence sets comes from the problem of phosphorus pollution in the Everglades. Ecologists have suggested that split points provide a phosphorus threshold at which biological imbalance occurs, and the lower endpoint of the confidence set may be interpreted as a level that is protective of the ecosystem. This is illustrated using data from a Duke University Wetlands Center phosphorus dosing study in the Everglades.Comment: Published at http://dx.doi.org/10.1214/009053606000001415 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Segmentation-Aware Convolutional Networks Using Local Attention Masks

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can match the performance of DenseCRFs while being faster and simpler, and in optical flow we obtain clearly sharper responses than networks that do not use local attention masks. In both cases, segmentation-aware convolution yields systematic improvements over strong baselines. Source code for this work is available online at http://cs.cmu.edu/~aharley/segaware

Complex diffusion-weighted image estimation via matrix recovery under general noise models

We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.Comment: 26 pages, 9 figure