Somervillesimon1567

Moreover, two novel constraints, sample affinity consistency and sample affinity regularization, are devised to refine the features and achieve the mutual promotion of these two branches. Extensive experiments of synthetic and real LR cases are conducted on wireless capsule endoscopy and histopathology images, verifying that our proposed method is significantly effective for medical image diagnosis.In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, GRAPHGEN, that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply starting from a set of rules, the algorithms introduced with the GRAPHGEN framework can generate decision trees with minimum average path-length, possibly considering image pattern frequencies, apply state prediction and code compression by the use of Directed Rooted Acyclic Graphs (DRAGs). Moreover, the proposed algorithmic solutions allow to combine different optimization techniques and significantly improve performance. Our proposal is showcased on three classical and widely employed algorithms (namely Connected Components Labeling, Thinning, and Contour Tracing). When compared to existing approaches -in 2D and 3D-, implementations using the generated optimal DRAGs perform significantly better than previous state-of-the-art algorithms, both on CPU and GPU.Human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We annotate a subset of the EPIC Kitchens dataset to include time-to-contact between hands and objects, as well as segmentations of hands and objects. Using these annotations we train the Anticipation Module, a module producing Contact Anticipation Maps and Next Active Object Segmentations - novel low-level representations providing temporal and spatial characteristics of anticipated near future action. On top of the Anticipation Module we apply Egocentric Object Manipulation Graphs (Ego-OMG), a framework for action anticipation and prediction. Ego-OMG models longer term temporal semantic relations through the use of a graph modeling transitions between contact delineated action states. Use of the Anticipation Module within Ego-OMG produces state-of-the-art results, achieving 1st and 2nd place on the unseen and seen test sets, respectively, of the EPIC Kitchens Action Anticipation Challenge, and achieving state-of-the-art results on the tasks of action anticipation and action prediction over EPIC Kitchens. We perform ablation studies over characteristics of the Anticipation Module to evaluate their utility.Dynamic artistic text style transfer aims to migrate the style in terms of both the appearance and motion patterns from a reference style video to the target text to create artistic text animation. Recent researches have improved the usability of transfer models by introducing texture control. However, it remains an important open challenge to investigate the control of the stylistic degree with respect to shape deformation. In this paper, we explore a new problem of dynamic artistic text style transfer with glyph stylistic degree control. The key idea is to build multi-scale glyph-style shape mappings through a novel bidirectional shape matching framework. Following this idea, we first introduce a scale-ware Shape-Matching GAN to learn such mappings to simultaneously model the style shape features at multiple scales and transfer them onto the target glyph. Furthermore, an advanced Shape-Matching GAN++ is proposed to animate a static text image based on the reference style video. Our Shape-Matching GAN++ characterizes the short-term consistency of motion patterns via shape matchings within consecutive frames, which are propagated to achieve effective long-term consistency. Experiments show that the proposed method outperforms previous state-of-the-arts both qualitatively and quantitatively, and generate high-quality and controllable artistic text.

To obtain definitive cancer diagnosis for suspicious lesions, accurate needle deployment and adequate tissue sampling in needle biopsy are essential. However, the single-bevel needles in current biopsy devices often induce deflection during insertion, potentially causing lesion missampling/undersampling and cancer misdiagnosis. Ro-3306 research buy This study aims to reveal the biopsy needle design criteria enabling both low deflection and adequate tissue sampling.

A novel model capable of predicting needle deflection and tissue deformation was first established to understand needle-tissue interaction with different needle tip geometries. Experiments of needle deflection and ex-vivo tissue biopsy were conducted for model validation.

The developed model showed a reasonably good prediction on the correlation of needle tip type vs. the resultant needle deflection and tissue sampling length. A new multi-bevel needle with the tissue separation point below the needle groove face has demonstrated to be an effective design with an 87% reduction in deflection magnitude and equivalently long tissue sampling length compared to the current single-bevel needle.

This study has revealed two critical design criteria for biopsy needles 1) multiple bevel faces at the needle tip can generate forces to balance bending moments during insertion to enable a low needle deflection and 2) the tissue separation point should be below the needle groove face to ensure long tissue sampling length.

The developed methodologies and findings in this study serve as proof-of-concept and can be utilized to investigate various biopsy procedures to improve cancer diagnostic accuracy as well as other procedures requiring accurate needle insertion.

The developed methodologies and findings in this study serve as proof-of-concept and can be utilized to investigate various biopsy procedures to improve cancer diagnostic accuracy as well as other procedures requiring accurate needle insertion.