Berrydejesus7397

A key takeaway is that optimizing the display layout does indeed produce significant improvements.The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc.The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art.Vision and language techniques have achieved remarkable progress, but it is still difficult to well handle problems involving fine-grained details. For example, when the robot is told to bring me the book in the girls left hand, existing methods would fail if the girl holds one book respectively in her left and right hand. In this work, we introduce a new task named human-centric relation segmentation (HRS) as a fine-grained case of HOI-det. It aims to predict the relations between the human and surrounding entities and identify the interacted human parts, which are represented as pixel-level masks. Correspondingly, we collect a new Person In Context (PIC) dataset and propose a Simultaneously Matching and Segmentation (SMS) framework to solve the task. It contains three parallel branches. Specifically, the entity segmentation branch obtains entity masks by dynamically-generated conditional convolutions; the subject object matching branch links the corresponding subjects and objects by displacement estimation and classifies the interacted human parts; and the human parsing branch generates the pixelwise human part labels. Outputs of the three branches are fused to produce the final HRS results. Extensive experiments on two datasets show that SMS outperforms baselines with the 36 FPS inference speed.Contextual information plays an important role in solving various image and scene understanding tasks. Prior works have focused on the extraction of contextual information from an image and use it to infer the properties of some object(s) in the image or understand the scene behind the image, e.g., context-based object detection, recognition and semantic segmentation. In this paper, we consider an inverse problem, i.e., how to hallucinate the missing contextual information from the properties of standalone objects. We refer to it as object-level scene context prediction. This problem is difficult, as it requires extensive knowledge of the complex and diverse relationships among objects in the scene. We propose a deep neural network, which takes as input the properties (i.e., category, shape, and position) of a few standalone objects to predict an object-level scene layout that compactly encodes the semantics and structure of the scene context where the given objects are. Quantitative experiments and user studies demonstrate that our model can generate more plausible scene contexts than the baselines. Our model also enables the synthesis of realistic scene images from partial scene layouts. Selleckchem Ipatasertib Finally, we validate that our model internally learns useful features for scene recognition and fake scene detection.Adding haptic feedback has been reported to improve the outcome of minimally invasive robotic surgery. In this study, we seek to determine whether an algorithm based on simulating responses of a cutaneous afferent population can be implemented to improve the performance of presenting haptic feedback for robot-assisted surgery. We propose a bio-inspired controlling model to present vibration and force feedback to help surgeons localize underlying structures in phantom tissue. A single pair of actuators was controlled by outputs of a model of a population of cutaneous afferents based on the pressure signal from a single sensor embedded in surgical forceps. We recruited 25 subjects including 10 expert surgeons to evaluate the performance of the bio-inspired controlling model in an artificial palpation task using the da Vinci surgical robot. Among the control methods tested, the bio-inspired system was unique in allowing both novices and experts to easily identify the locations of all classes of tumors and did so with reduced contact force and tumor contact time. This work demonstrates the utility of our bio-inspired multi-modal feedback system, which resulted in superior performance for both novice and professional users, in comparison to a traditional linear and the existing piecewise discrete algorithms of haptic feedback.

To determine the electric field threshold in our numerical model that best fits the local response to irreversible electroporation (IRE) ablation of hepatic tumors as seen in 6 week follow-up MRI. To numerically evaluate the heat generating effect of IRE and demonstrate the potential of treatment planning to avoid thermal damage and shorten procedures in the future.

In a retrospective study 18 cases of hepatic tumors treated with IRE ablation were numerically reconstructed and treatment outcome was computed with a numerical treatment planning framework. Simulated ablation volumes were compared to ablation volumes segmented from follow-up MRI. Two cases with a high thermal damage component were selected for numerical optimization.

The best fit between segmented and simulated ablation zones was obtained at 900 V/cm threshold with the average absolute error of 5.6 1.5 mm. Considerable heating was observed in the dataset. In 7/18 cases >50 % of tumor volume experienced heating likely to cause thermal damage.