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

TokenFlow: Consistent Diffusion Features for Consistent Video Editing

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io

Challenges in Future Mathematical Modelling of Hierarchical Functional Safety Control Structures within STAMP Safety Model

In the STAMP model, based on control theory, the control relationships between various system elements enforced by the closed Control Loops (CLs) are logical and functional. A literature survey emphasized the fact that for the moment STAMP and its main tools STPA and CAST are not associated with any numerical tools. The main rationale of our work is to understand whether STAMP matches to be a quantitative model. Furthermore, in a case that we find that numerical tools can be used in STAMP, we intend to bridge the gap between the logical-functional approach in STAMP and any of the suitable quantitative approaches applied in Engineering Control Theory (ECT). As a first step, a literature comparison was performed between the basic control parameters existing explicitly at the moment in the STAMP model, and those well known in the literature of ECT. The results reveal that there are many similar terms, especially related to conceptual and general definitions. However, we have observed that there are also basic quantitative parameters from ECT which are not yet referred to in STAMP as quantitative safety evaluation parameters. Another main finding is an inherent difference in various ECT related parameters and the CLs at the various hierarchical levels. ECT was originally developed to deal with physical systems. Thus, any machine related internal control loops within the lower-physical level of a Sociotechnical System (STS) can be directly addressed with quantitative methods from ECT. However, most of the human-machine interactions in the lower levels and the human and societal controls in the higher levels are at the moment not suitable for those methods. We assume these ECT parameters may have an important role in designing and examining systems safety and hence we suggest, should be integrated into STAMP model, in purpose to be able to enhance systems safety

Neuroplasticity in Young Age: Computer-Based Early Neurodevelopment Classifier

Neurodevelopmental syndromes, a continuously growing issue, are impairments in the growth and development of the brain and CNS which are pronounced in a variety of emotional, cognitive, motor and social skills. Early assessment and detection of typical, clinically correlated early signs of developmental abnormalities is crucial for early and effective intervention, supporting initiation of early treatment and minimizing neurological and functional deficits. Successful early interventions would then direct to early time windows of higher neural plasticity. Various syndromes are reflected in early vocal and motor characteristics, making them suitable indicators of an infant’s neural development. Performance of the computerized classifiers we developed shows approximately 90% accuracy on a database of diagnosed babies. The results demonstrate the potential of vocal and motor analysis for computer-assisted early detection of neurodevelopmental insults

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

Priority diffusion model in lattices and complex networks

We introduce a model for diffusion of two classes of particles ($A$ and $B$) with priority: where both species are present in the same site the motion of $A$'s takes precedence over that of $B$'s. This describes realistic situations in wireless and communication networks. In regular lattices the diffusion of the two species is normal but the $B$ particles are significantly slower, due to the presence of the $A$ particles. From the fraction of sites where the $B$ particles can move freely, which we compute analytically, we derive the diffusion coefficients of the two species. In heterogeneous networks the fraction of sites where $B$ is free decreases exponentially with the degree of the sites. This, coupled with accumulation of particles in high-degree nodes leads to trapping of the low priority particles in scale-free networks.Comment: 5 pages, 3 figure

In the Hunt for Therapeutic Targets: Mimicking the Growth, Metastasis, and Stromal Associations of Early-Stage Lung Cancer Using a Novel Orthotopic Animal Model

BackgroundThe existing shortage of animal models that properly mimic the progression of early-stage human lung cancer from a solitary confined tumor to an invasive metastatic disease hinders accurate characterization of key interactions between lung cancer cells and their stroma. We herein describe a novel orthotopic animal model that addresses these concerns and consequently serves as an attractive platform to study tumor–stromal cell interactions under conditions that reflect early-stage lung cancer.MethodsUnlike previous methodologies, we directly injected small numbers of human or murine lung cancer cells into murine's left lung and longitudinally monitored disease progression. Next, we used green fluorescent protein-tagged tumor cells and immuno-fluorescent staining to determine the tumor's microanatomic distribution and to look for tumor-infiltrating immune cells and stromal cells. Finally, we compared chemokine gene expression patterns in the tumor and lung microenvironment.ResultsWe successfully generated a solitary pulmonary nodule surrounded by normal lung parenchyma that grew locally and spread distally over time. Notably, we found that both fibroblasts and leukocytes are recruited to the tumor's margins and that distinct myeloid cell attracting and CCR2-binding chemokines are specifically induced in the tumor microenvironment.ConclusionOur orthotopic lung cancer model closely mimics the pathologic sequence of events that characterizes early-stage human lung cancer propagation. It further introduces new means to monitor tumor–stromal cell interactions and offers unique opportunities to test therapeutic targets under conditions that reflect early-stage lung cancer. We argue that for such purposes our model is superior to lung cancer models that are based either on genetic induction of epithelial transformation or on ectopic transplantation of malignant cells