scholarly journals CRF-Based Model for Instrument Detection and Pose Estimation in Retinal Microsurgery

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mohamed Alsheakhali ◽  
Abouzar Eslami ◽  
Hessam Roodaki ◽  
Nassir Navab
Keyword(s):  
Random Field ◽  
Pose Estimation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Recovery Process ◽  
New Method ◽  
Ophthalmic Surgery ◽  
Illumination Changes ◽  
Cluttered Background

Detection of instrument tip in retinal microsurgery videos is extremely challenging due to rapid motion, illumination changes, the cluttered background, and the deformable shape of the instrument. For the same reason, frequent failures in tracking add the overhead of reinitialization of the tracking. In this work, a new method is proposed to localize not only the instrument center point but also its tips and orientation without the need of manual reinitialization. Our approach models the instrument as a Conditional Random Field (CRF) where each part of the instrument is detected separately. The relations between these parts are modeled to capture the translation, rotation, and the scale changes of the instrument. The tracking is done via separate detection of instrument parts and evaluation of confidence via the modeled dependence functions. In case of low confidence feedback an automatic recovery process is performed. The algorithm is evaluated on in vivo ophthalmic surgery datasets and its performance is comparable to the state-of-the-art methods with the advantage that no manual reinitialization is needed.

scholarly journals EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD

2016 ◽  
Vol XLI-B3 ◽  
pp. 633-640
Author(s):  
Weihao Li ◽  
Michael Ying Yang
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Mean Field ◽  
Semantic Segmentation ◽  
Gaussian Kernels ◽  
Previous State ◽  
Fully Connected ◽  
Better Than

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

scholarly journals Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach

2019 ◽  
Author(s):  
Bin Wang ◽  
Guojun Qi ◽  
Sheng Tang ◽  
Tianzhu Zhang ◽  
Yunchao Wei ◽  
...  
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Semantic Segmentation ◽  
Semantic Feature ◽  
Feature Maps ◽  
Feature Enhancement ◽  
Pascal Voc ◽  
Segmentation Approach ◽  
Fully Connected

Semantic segmentation suffers from the fact that densely annotated masks are expensive to obtain. To tackle this problem, we aim at learning to segment by only leveraging scribbles that are much easier to collect for supervision. To fully explore the limited pixel-level annotations from scribbles, we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic components, i.e., prediction refinement and boundary regression. Specifically, the prediction refinement progressively makes a better segmentation by adopting an iterative upsampling and a semantic feature  enhancement strategy. In the boundary regression, we employ class-agnostic edge maps for supervision to effectively guide the segmentation network in localizing the boundaries between different semantic regions, leading to producing finer-grained representation of feature maps for semantic segmentation. The experiment results on the PASCAL VOC 2012 demonstrate the proposed BPG achieves mIoU of 73.2% without fully connected Conditional Random Field (CRF) and 76.0% with CRF, setting up the new state-of-the-art in literature.

scholarly journals Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8090
Author(s):  
Joel Vidal ◽  
Chyi-Yeu Lin ◽  
Robert Martí
Keyword(s):  
Visual Attention ◽  
Pose Estimation ◽  
Feature Matching ◽  
State Of The Art ◽  
Estimation Methods ◽  
Color Spaces ◽  
Color Information ◽  
Robust Performance ◽  
Illumination Changes ◽  
Color Cues

Recently, 6D pose estimation methods have shown robust performance on highly cluttered scenes and different illumination conditions. However, occlusions are still challenging, with recognition rates decreasing to less than 10% for half-visible objects in some datasets. In this paper, we propose to use top-down visual attention and color cues to boost performance of a state-of-the-art method on occluded scenarios. More specifically, color information is employed to detect potential points in the scene, improve feature-matching, and compute more precise fitting scores. The proposed method is evaluated on the Linemod occluded (LM-O), TUD light (TUD-L), Tejani (IC-MI) and Doumanoglou (IC-BIN) datasets, as part of the SiSo BOP benchmark, which includes challenging highly occluded cases, illumination changing scenarios, and multiple instances. The method is analyzed and discussed for different parameters, color spaces and metrics. The presented results show the validity of the proposed approach and their robustness against illumination changes and multiple instance scenarios, specially boosting the performance on relatively high occluded cases. The proposed solution provides an absolute improvement of up to 30% for levels of occlusion between 40% to 50%, outperforming other approaches with a best overall recall of 71% for the LM-O, 92% for TUD-L, 99.3% for IC-MI and 97.5% for IC-BIN.

scholarly journals EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD

2016 ◽  
Vol XLI-B3 ◽  
pp. 633-640 ◽  
Author(s):  
Weihao Li ◽  
Michael Ying Yang
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Mean Field ◽  
Semantic Segmentation ◽  
Gaussian Kernels ◽  
Previous State ◽  
Fully Connected ◽  
Better Than

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

Aspect opinion expression and rating prediction via LDA–CRF hybrid

2018 ◽  
Vol 24 (4) ◽  
pp. 611-639 ◽  
Author(s):  
ABHISHEK LADDHA ◽  
ARJUN MUKHERJEE
Keyword(s):  
Random Field ◽  
Field Component ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Joint Modeling ◽  
Phrase Extraction ◽  
Opinion Expression ◽  
Rating Prediction ◽  
Restaurant Reviews ◽  
Modelling Approach

AbstractIn this paper, we study the problem of aspect-based sentiment analysis. Our model simultaneously extracts aspect-specific opinion expressions and determines the rating for each aspect in reviews. Previous works have mainly focused on the problem of opinion phrase extraction and aspect rating prediction in a pipelined manner and are not able to capture the dependencies of aspect opinion expression on aspect rating and vice-versa. They are also unable to discover aspect-specific opinion expressions and their associated rating scores. We present a joint modelling approach to extract aspect-specific sentiment expression and aspect rating prediction simultaneously. This paper proposes a novel LDA–CRF hybrid model which employs discriminative conditional random field component for phrase extraction, a regression component for rating prediction and a generative component for grouping aspect–sentiment expressions (aspect-specific opinion expressions) into coherent topics. To show the effectiveness of our approach, we evaluate the performance of the model on both task: (i) aspect-specific opinion expressions and (ii) rating prediction on the dataset of hotel and restaurant reviews from TripAdvisor.com. Experimental results show that both task potentially reinforce each other and joint modeling outperformed state-of-the-art baselines for each individual tasks.

Biological NMR Spectroscopy

1997 ◽  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Nmr Spectroscopy ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
State Of The Art ◽  
Critical Assessment ◽  
Resonance Spectroscopy ◽  
History Of ◽  
Structure Of Proteins

This book presents a critical assessment of progress on the use of nuclear magnetic resonance spectroscopy to determine the structure of proteins, including brief reviews of the history of the field along with coverage of current clinical and in vivo applications. The book, in honor of Oleg Jardetsky, one of the pioneers of the field, is edited by two of the most highly respected investigators using NMR, and features contributions by most of the leading workers in the field. It will be valued as a landmark publication that presents the state-of-the-art perspectives regarding one of today's most important technologies.

scholarly journals State-of-the-art methods and devices for generation, exposure, and collection of aerosols from e-vapor products

2020 ◽  
Vol 4 ◽  
pp. 239784732097975
Author(s):  
Stéphanie Boué ◽  
Didier Goedertier ◽  
Julia Hoeng ◽  
Anita Iskandar ◽  
Arkadiusz K Kuczaj ◽  
...  
Keyword(s):  
Ad Hoc ◽  
State Of The Art ◽  
Physicochemical Characterization ◽  
Objective Evaluation ◽  
Vapor Generation ◽  
Characterization Methods ◽  
Clinical Exposure ◽  
Aerosol Generation

E-vapor products (EVP) have become popular alternatives for cigarette smokers who would otherwise continue to smoke. EVP research is challenging and complex, mostly because of the numerous and rapidly evolving technologies and designs as well as the multiplicity of e-liquid flavors and solvents available on the market. There is an urgent need to standardize all stages of EVP assessment, from the production of a reference product to e-vapor generation methods and from physicochemical characterization methods to nonclinical and clinical exposure studies. The objective of this review is to provide a detailed description of selected experimental setups and methods for EVP aerosol generation and collection and exposure systems for their in vitro and in vivo assessment. The focus is on the specificities of the product that constitute challenges and require development of ad hoc assessment frameworks, equipment, and methods. In so doing, this review aims to support further studies, objective evaluation, comparison, and verification of existing evidence, and, ultimately, formulation of standardized methods for testing EVPs.

scholarly journals Bioengineered Islet Cell Transplantation

2021 ◽  
Author(s):  
Kevin Bellofatto ◽  
Beat Moeckli ◽  
Charles-Henri Wassmer ◽  
Margaux Laurent ◽  
Graziano Oldani ◽  
...  
Keyword(s):  
State Of The Art ◽  
Diabetes Research ◽  
Islet Cell Transplantation ◽  
Β Cell ◽  
Cell Compartment ◽  
Factors Affecting ◽  
Insulin Activity ◽  
On Chip

Abstract Purpose of Review β cell replacement via whole pancreas or islet transplantation has greatly evolved for the cure of type 1 diabetes. Both these strategies are however still affected by several limitations. Pancreas bioengineering holds the potential to overcome these hurdles aiming to repair and regenerate β cell compartment. In this review, we detail the state-of-the-art and recent progress in the bioengineering field applied to diabetes research. Recent Findings The primary target of pancreatic bioengineering is to manufacture a construct supporting insulin activity in vivo. Scaffold-base technique, 3D bioprinting, macro-devices, insulin-secreting organoids, and pancreas-on-chip represent the most promising technologies for pancreatic bioengineering. Summary There are several factors affecting the clinical application of these technologies, and studies reported so far are encouraging but need to be optimized. Nevertheless pancreas bioengineering is evolving very quickly and its combination with stem cell research developments can only accelerate this trend.

scholarly journals In vivo interactome profiling by enzyme‐catalyzed proximity labeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yangfan Xu ◽  
Xianqun Fan ◽  
Yang Hu
Keyword(s):  
State Of The Art ◽  
Catalytic Efficiency ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Current State ◽  
Protein Interactome ◽  
Potential Applications ◽  
Protein Protein Interaction Networks ◽  
Temporal And Spatial

AbstractEnzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

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