varunr14/newsummary huggingface dataset:
This paper deals with model based pose estimation . We propose a direct approach that takes into account the image as a whole . For this we consider a similarity measure the mutual information . Mutual information is a measure of the quantity of information shared by two signals . Exploiting this measure allows our method to deal with different image modalities . Furthermore it handles occlusions and illumination changes . Results with synthetic and real image sequences with static or mobile camera demonstrate the robustness of the method and its ability to produce stable and precise pose estimations . 


Summary: We tackle the 3D model based tracking using the entire image and a textured 3D model. We propose a nonlinear optimization of the problem based on mutual information. Mutual information deals with the different modalities of real and virtual images. Our proposed method withdraws feature detection and matching issues. Results show the success of the approach and its precision.
To extend the use of wearable sensor networks for stroke patients training and assessment in non clinical settings this paper proposes a novel remote quantitative Fugl Meyer assessment framework in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb wrist and fingers . The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset . Considering the FMA scale is time consuming and complicated seven training exercises were designed to replace the upper limb related 33 items in FMA scale . 24 stroke inpatients participated in the experiments in clinical settings and 5 of them were involved in the experiments in home settings after they left the hospital . Both the experimental results in clinical and home settings showed that the proposed quantitative FMA model can precisely predict the FMA scores based on wearable sensor data the coefficient of determination can reach as high as 0.917 . It also indicated that the proposed framework can provide a potential approach to the remote quantitative rehabilitation training and evaluation . 


Summary: A novel remote quantitative Fugl Meyer assessment FMA framework was proposed for stroke patients. Seven training exercises were designed to represent the upper limb related 33 items in FMA scale. Ensemble machine learning and RRelief algorithm were applied to establish the quantitative assessment model. The proposed framework has been implemented in both clinical and home settings.
This paper addresses the general problem of robust parametric model estimation from data that has both an unknown and possibly majority fraction of outliers as well as an unknown scale of measurement noise. We focus on computer vision applications from image correspondences such as camera resectioning estimation of the fundamental matrix or relative pose for 3D reconstruction and estimation of 2D homographies for image registration and motion segmentation although there are many other applications. In practice these methods typically rely on a predefined inlier thresholds because automatic scale detection is usually too unreliable or too slow. We propose a new method for robust estimation with automatic scale detection that is faster more precise and more robust than previous alternatives and show that it can be practically applied to these problems. 


Summary: robust scale estimation without a breakdown point insensitive to inlier noise distribution efficient and practical application to many computer vision problems
Psoriasis is an autoimmune skin disease with red and scaly plaques on skin and affecting about 125 million people worldwide . Currently dermatologist use visual and haptic methods for diagnosis the disease severity . This does not help them in stratification and risk assessment of the lesion stage and grade . Further current methods add complexity during monitoring and follow up phase . The current diagnostic tools lead to subjectivity in decision making and are unreliable and laborious . This paper presents a first comparative performance study of its kind using principal component analysis based CADx system for psoriasis risk stratification and image classification utilizing 11 higher order spectra features 60 texture features and 86 color feature sets and their seven combinations . Aggregate 540 image samples from 30 psoriasis patients of Indian ethnic origin are used in our database . Machine learning using PCA is used for dominant feature selection which is then fed to support vector machine classifier to obtain optimized performance . Three different protocols are implemented using three kinds of feature sets . Reliability index of the CADx is computed . Among all feature combinations the CADx system shows optimal performance of 100 accuracy 100 sensitivity and specificity when all three sets of feature are combined . Further our experimental result with increasing data size shows that all feature combinations yield high reliability index throughout the PCA cutoffs except color feature set and combination of color and texture feature sets . HOS features are powerful in psoriasis disease classification and stratification . Even though independently all three set of features HOS texture and color perform competitively but when combined the machine learning system performs the best . The system is fully automated reliable and accurate . 


Summary: Comparative analysis of systems with different feature sets. HOS features extraction for psoriasis images. Largest set of mathematical features ever computed for psoriasis images. Understanding the reliability analysis of the CADx system. Accurate system with 100 accuracy and 100 sensitivity and specificity.
With the exponential growth in the world population and the constant increase in human mobility the possible impact of outbreaks of epidemics on cities is increasing especially in high density urban areas such as public transportation and transfer points . The volume and proximity of people in these areas can lead to an observed dramatic increase in the transmission of airborne viruses and related pathogens . Due to the critical role these areas play in transmission it is vital that we have a comprehensive understanding of the transmission highways in these areas to predict or prevent the spreading of infectious diseases in general . The principled approach of this paper is to combine and utilize as much information as possible from relevant sources and to integrate these data in a simulated environment that allows for scenario testing and decision support . In this paper we describe a novel approach to study the spread of airborne diseases in cities by combining traffic information with geo spatial data infection dynamics and spreading characteristics . The system is currently being used in an attempt to understand the outbreak of influenza in densely populated cities in China . 


Summary: A novel approach to study Urban Airborne Disease spreading. Integrate as much information as possible in a simulation environment. GIS enabled city modeling and travel routing calculation. Synthesizing the population in a city and modeling people s daily behavior in the presence of epidemics.
Background and objective In oral and maxillofacial surgery conventional radiographic cephalometry is one of the standard auxiliary tools for diagnosis and surgical planning . While contemporary computer assisted cephalometric systems and methodologies support cephalometric analysis they tend neither to be practical nor intuitive for practitioners . This is particularly the case for 3D methods since the associated landmarking process is difficult and time consuming . In addition to this there are no 3D cephalometry norms or standards defined therefore new landmark selection methods are required which will help facilitate their establishment . This paper presents and evaluates a novel haptic enabled landmarking approach to overcome some of the difficulties and disadvantages of the current landmarking processes used in 2D and 3D cephalometry . Method In order to evaluate this new system s feasibility and performance 21 dental surgeons performed a range of case studies using a haptic enabled 2D 2 D and 3D digital cephalometric analyses . Results The results compared the 2D 2 D and 3D cephalometric values errors and standard deviations for each case study and associated group of participants and revealed that 3D cephalometry significantly reduced landmarking errors and variability compared to 2D methods . Conclusions Through enhancing the process by providing a sense of touch the haptic enabled 3D digital cephalometric approach was found to be feasible and more intuitive than its counterparts as well effective at reducing errors the variability of the measurements taken and associated task completion times . 


Summary: Computer aided cephalometric systems tend not to be practical and intuitive. A new haptic enabled landmarking approach for 3D cephalometry is proposed. Several experimental tests were conducted to evaluate the proposed approach. Haptic technologies facilities the landmark selection process in 3D cephalometry. The haptic user interface allows the user to feel and touch the virtual patient s skull.
In this paper how to calibrate a fixed multi camera system and simultaneously achieve a Euclidean reconstruction from a set of segments is addressed . It is well known that only a projective reconstruction could be achieved without any prior information . Here the known segment lengths are exploited to upgrade the projective reconstruction to a Euclidean reconstruction and simultaneously calibrate the intrinsic and extrinsic camera parameters . At first a DLT like algorithm for the Euclidean upgrading from segment lengths is derived in a very simple way . Although the intermediate results in the DLT like algorithm are essentially equivalent to the quadric of segments the DLT like algorithm is of higher accuracy than the existing linear algorithms derived from the QoS because of a more accurate way to extract the plane at infinity from the intermediate results . Then to further improve the accuracy of Euclidean upgrading two weighted DLT like algorithms are presented by weighting the linear constraint equations in the original DLT like algorithm . Finally using the results of these linear algorithms as the initial values a new weighted nonlinear algorithm for Euclidean upgrading is explored to recover the Euclidean structure more accurately . Extensive experimental results on both the synthetic data and the real image data demonstrate the effectiveness of our proposed algorithms in Euclidean upgrading and multi camera calibration . 


Summary: A DLT like algorithm for Euclidean upgrading from segment lengths is proposed. The constraint equation is parameterized in a simple way. The plane at infinity is directly extracted from the intermediate results. This algorithm has higher accuracy than the existing linear algorithms. Weighting and nonlinear refinement further improve the accuracy.
Recent research emphasizes more on analyzing multiple features to improve face recognition FR performance. One popular scheme is to extend the sparse representation based classification framework with various sparse constraints. Although these methods jointly study multiple features through the constraints they just process each feature individually such that they overlook the possible high level relationship among different features. It is reasonable to assume that the low level features of facial images such as edge information and smoothed low frequency image can be fused into a more compact and more discriminative representation based on the latent high level relationship. FR on the fused features is anticipated to produce better performance than that on the original features since they provide more favorable properties. Focusing on this we propose two different strategies which start from fusing multiple features and then exploit the dictionary learning DL framework for better FR performance. The first strategy is a simple and efficient two step model which learns a fusion matrix from training face images to fuse multiple features and then learns class specific dictionaries based on the fused features. The second one is a more effective model requiring more computational time that learns the fusion matrix and the class specific dictionaries simultaneously within an iterative optimization procedure. Besides the second model considers to separate the shared common components from class specified dictionaries to enhance the discrimination power of the dictionaries. The proposed strategies which integrate multi feature fusion process and dictionary learning framework for FR realize the following goals 1 exploiting multiple features of face images for better FR performances 2 learning a fusion matrix to merge the features into a more compact and more discriminative representation 3 learning class specific dictionaries with consideration of the common patterns for better classification performance. We perform a series of experiments on public available databases to evaluate our methods and the experimental results demonstrate the effectiveness of the proposed models. 


Summary: We propose two strategies for face recognition through multiple features. Our methods integrate multi feature fusion and dictionary learning. The fusion process and dictionary learning are learned simultaneously. Extensive experiments validate the merits of our methods.
In this article a novel technique for fixation prediction and saccade generation will be introduced . The proposed model simulates saccadic eye movement to incorporate the underlying eye movement mechanism into saliency estimation . To this end a simple salience measure is introduced . Afterwards we derive a system model for saccade generation and apply it in a stochastic filtering framework . The proposed model will dynamically make a saccade toward the next predicted fixation and produces saliency maps . Evaluation of the proposed model is carried out in terms of saccade generation performance and saliency estimation . Saccade generation evaluation reveals that the proposed model outperforms inhibition of return . Also experiments signify integration of eye movement mechanism into saliency estimation boosts the results . Finally comparison with several saliency models shows the proposed model performs aptly . 


Summary: Introducing a stochastic model for simulating eye movements Implementing the proposed model Implementing an algorithm for predicting saccades and fixations
Intensity inhomogeneity often appears in medical images such as X ray tomography and magnetic resonance images due to technical limitations or artifacts introduced by the object being imaged . It is difficult to segment such images by traditional level set based segmentation models . In this paper we propose a new level set method integrating local and global intensity information adaptively to segment inhomogeneous images . The local image information is associated with the intensity difference between the average of local intensity distribution and the original image which can significantly increase the contrast between foreground and background . Thus the images with intensity inhomogeneity can be efficiently segmented . What is more to avoid the re initialization of the level set function and shorten the computational time a simple and fast level set evolution formulation is used in the numerical implementation . Experimental results on synthetic images as well as real medical images are shown in the paper to demonstrate the efficiency and robustness of the proposed method . 


Summary: We constructed a new region based pressure force RBPF function. We proposed a novel level set based model for inhomogeneous image segmentation. We combine the local and global intensity information adaptively. The local image information can significantly increase image contrast. We apply a fast and simple level set method to implement the curve evolution.
In this paper we present the dfcomb R package for the implementation of a single prospective clinical trial or simulation studies of phase I combination trials in oncology . The aim is to present the features of the package and to illustrate how to use it in practice though different examples . The use of combination clinical trials is growing but the implementation of existing model based methods is complex so this package should promote the use of innovative adaptive designs for early phases combination trials . 


Summary: R package dfcomb available for phase I combination clinical trials according to previous methodological paper. Implement a single prospective clinical trial or simulation studies of phase I combination trials in oncology. Features and illustration of the functions. Promote the use of innovative adaptive design for early phase combination trials.
Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention procedures . Intravascular ultrasound is most commonly adapted for screening but current tools lack the ability for risk stratification based on grayscale plaque morphology . Our hypothesis is based on the genetic makeup of the atherosclerosis disease that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up . This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk . This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B mode ultrasound carotid intima media thickness a marker of subclinical atherosclerosis . Support vector machine learning paradigm is adapted for risk stratification where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces which are then used by the SVM classifier with five different kernels sets . These six feature combinational spaces are designed using 56 novel feature sets . K fold cross validation protocol with 10 trials per fold is used for optimization of best SVM kernel and best feature combination set . IRB approved coronary IVUS and carotid B mode ultrasound were jointly collected on 15 patients via 40MHz catheter utilizing iMap with 2865 frames per patient and linear probe B mode carotid ultrasound . Using the above protocol the system shows the classification accuracy of 94.95 and AUC of 0.95 using optimized feature combination . This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients cardiovascular disease management while validating our two hypotheses . 


Summary: Coronary risk stratification. Machine learning to link two arteries. 56 grayscale features. Coronary tissue characterization. Classification accuracy 94.95 and AUC 0.95.
One of the key elements of e learning platforms is the content provided to the students . Content creation is a time demanding task that requires teachers to prepare material taking into account that it will be accessed on line . Moreover the teacher is restricted by the functionalities provided by the e learning platforms . In contexts such as radiology where images have a key role the required functionalities are still more specific and difficult to be provided by these platforms . Our purpose is to create a framework to make teacher s tasks easier specially when he has to deal with contents where images have a main role . In this paper we present RadEd a new web based teaching framework that integrates a smart editor to create case based exercises that support image interaction such as changing the window width and the grey scale used to render the image taking measurements on the image attaching labels to images and selecting parts of the images amongst others . It also provides functionalities to prepare courses with different topics exercises and theory material and also functionalities to control students work . Different experts have used RadEd and all of them have considered it a very useful and valuable tool to prepare courses where radiological images are the main component . RadEd provides teachers functionalities to prepare more realistic cases and students the ability to make a more specific diagnosis . 


Summary: Content creation is a very time demanding task. RadEd is a web based framework with a smart editor to create image based exercises. RadEd provides teachers functionalities to prepare more realistic cases. RadEd allows learners to make more specific diagnosis.
Background and objectives Because skin cancer affects millions of people worldwide computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis . This paper aims to present a review of the current methods and outline a comparative analysis with regards to several of the fundamental steps of image processing such as image acquisition pre processing and segmentation . Methods Techniques that have been proposed to achieve these tasks were identified and reviewed . As to the image segmentation task the techniques were classified according to their principle . Results The techniques employed in each step are explained and their strengths and weaknesses are identified . In addition several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results . Conclusions The image segmentation of skin lesions has been addressed successfully in many studies however there is a demand for new methodologies in order to improve the efficiency . 


Summary: The clinical requirement for the early diagnosis of malignant skin lesions from images is introduced and justified. An up to date review about the proposed techniques for the image segmentation of pigmented skin lesions is presented. Additionally the tasks related to image acquisition and pre processing are also taken into account. The techniques are introduced classified and some examples of their results are illustrated and discussed. This review is of interest both for researchers and for health professionals.