Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor

In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion. Our method for fusion combines images from different spectral channels into a single fused image by different approaches for low and high frequency signals. A prerequisite of fusion is a stage of geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel do not yield reasonable results on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment of a level that allows us to further fuse the images. As our experiments show, we produce a high quality of multi-spectral image registration and fusion under many challenging scenarios

Deep Multi-Spectral Registration Using Invariant Descriptor Learning

In this paper, we introduce a novel deep-learning method to align cross-spectral images. Our approach relies on a learned descriptor which is invariant to different spectra. Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. To that end, we developed a feature-based approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration problem. Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor. As our experiments demonstrate we achieve a high-quality alignment of cross-spectral images with a sub-pixel accuracy. Comparing to other existing methods, our approach is more accurate in the task of VIS to NIR registration

Fast Multiple-Part Based Object Detection Using KD-Ferns

In this work we present a new part-based object de-tection algorithm with hundreds of parts performing real-time detection. Part-based models are currently state-of-the-art for object detection due to their ability to represent large appearance variations. However, due to their high computational demands such methods are limited to sev-eral parts only and are too slow for practical real-time im-plementation. Our algorithm is an accelerated version of the “Feature Synthesis ” (FS) method [1], which uses mul-tiple object parts for detection and is among state-of-the-art methods on human detection benchmarks, but also suf-fers from a high computational cost. The proposed Accel-erated Feature Synthesis (AFS) uses several strategies for reducing the number of locations searched for each part. The first strategy uses a novel algorithm for approximate nearest neighbor search which we developed, termed “KD-Ferns”, to compare each image location to only a subset of the model parts. Candidate part locations for a specific part are further reduced using spatial inhibition, and using an object-level “coarse-to-fine ” strategy. In our empirical evaluation on pedestrian detection benchmarks, AFS main-tains almost fully the accuracy performance of the original FS, while running more than ×4 faster than existing part-based methods which use only several parts. AFS is to our best knowledge the first part-based object detection method achieving real-time running performance: nearly 10 frames per-second on 640 × 480 images on a regular CPU. 1

Reconsidering OS Memory Optimizations in the Presence of Disaggregated Memory

Tiered memory systems introduce an additional memory level with higher-than-local-DRAM access latency and require sophisticated memory management mechanisms to achieve cost-efficiency and high performance. Recent works focus on byte-addressable tiered memory architectures which offer better performance than pure swap-based systems. We observe that adding disaggregation to a byte-addressable tiered memory architecture requires important design changes that deviate from the common techniques that target lower-latency non-volatile memory systems. Our comprehensive analysis of real workloads shows that the high access latency to disaggregated memory undermines the utility of well-established memory management optimizations Based on these insights, we develop HotBox – a disaggregated memory management subsystem for Linux that strives to maximize the local memory hit rate with low memory management overhead. HotBox introduces only minor changes to the Linux kernel while outperforming state-of-the-art systems on memory-intensive benchmarks by up to 2.25×

Labor Augmentation with Oxytocin Decreases Glutathione Level

Objective. To compare oxidative stress following spontaneous vaginal delivery with that induced by Oxytocin augmented delivery. Methods. 98 women recruited prior to labor. 57 delivered spontaneously, while 41 received Oxytocin for augmentation of labor. Complicated deliveries and high-risk pregnancies were excluded. Informed consent was documented. Arterial cord blood gases, levels of Hematocrit, Hemoglobin, and Bilirubin were studied. Glutathione (GSH) concentration was measured by a spectroscopic method. Plasma and red blood cell (RBC) levels of Malondialdehyde indicated lipid peroxidation. RBC uptake of phenol red denoted cell penetrability. SPSS data analysis was used. Results. Cord blood GSH was significantly lower in the Oxytocin group (2.3±0.55 mM versus 2.55±0.55 mM, =.01). No differences were found in plasma or RBC levels of MDA or in uptake of Phenol red between the groups. Conclusion. Lower GSH levels following Oxytocin augmentation indicate an oxidative stress, though selected measures of oxidative stress demonstrate no cell damage