Miracle-dz/UniFAR-FTUs huggingface dataset:
ADVANTAGES OF USING SIFT FOR BRAIN TUMOR DETECTION

The brain is the anterior most part of the central nervous system. The cranium, a bony box in the skull protects it. Virtually every activity or thought of ours is controlled by our brain. So, it’s very dangerous when the proper functioning of the brain is hindered. Brain tumor is one such disease which if not detected early and treated accordingly, can prove fatal. Structure of the brain is quite complex and hence it is very difficult to detect the abnormalities in early stages. In our paper we will be giving an overview of the various techniques used for brain tumor detection and how SIFT overcomes their limitations. The techniques discussed include biopsy, manual segmentation, mathematical morphology & wavelet transform, artificial neural network and finally SIFT (Scale Invariant Feature Transform). Biopsy is a surgical method which needs to be performed by highly skilled professionals. The rest other methods use MRI images and thus are non-invasive. SIFT technique which we are using in our project gives good accuracy, is cost effective and most importantly is invariant to translation, scale, rotation, affine transform, change in illumination, etc.
Optimal distributed generation planning based on NSGA-II and MATPOWER

ii Acknowledgements . iii Declaration . iv Statement of Copyright . v List of Tables. x List of Figures . xii List of Abbreviations . xv Introduction . 1 1.1 Background . 1 1.2 Aim and Objectives . 3 1.3 Contribution to Knowledge . 4 1.4 Thesis Outline . 4 1.6 List of Publications . 5 Literature Review . 6 2.
Healthy Life Expectancy in the EU Member States

A shift in emphasis from increasing survival to improving both the length and quality of peoples’ lives has led to a greater policy interest in the United Kingdom and in Europe as a whole in summary measures of population health. Since health expectancy is a complex, multi-faceted concept, there are consequently as many types of health expectancies as there are empirical measures of sub-optimal health. Thus, this paper is essentially about the transition of health states and the dynamics of health and addresses two questions. First, what is the probability that an individual will be in the same health state next year, be free of disability, be in worse health, have left for a permanent health care institution or be dead? Secondly, what is the expected time spent in each health state, given that an individual is initially in a specified health category?
The Dance Heritage Coalition: Passing on the Vitality of American Dance

Dance, an art that Merce Cunningham described as “impermanent as breathing,” faces greater challenges than any other in the establishment and preservation of its heritage. ∗ Furthermore, dance remains perennially too little known, understood, or appreciated by the general public. The Dance Heritage Coalition (DHC), the only national organization dedicated to documenting and preserving America’s dance legacy, exists to respond to these challenges. Through diverse initiatives undertaken with many partners and member institutions, the DHC supports the dance community and the field of dance scholarship by seeking to improve the sustainability and accessibility of collections relating to dance and to promote the best practices for the organization, preservation, and use of legacy material. The DHC was founded in 1992 as a response to the findings of a fieldwide study of how dance was documented and preserved, funded by The Andrew W. Mellon Foundation. These findings suggested that dance archives and collections would be strengthened by the formation of a national alliance of institutions, scattered across the United States, holding major collections of dance material—a move that would also promote communication across organizations, joint programming, and coordinated policy-making and advocacy. The DHC has grown to include eleven participating institutions, each of which is represented on the organization’s board of directors:
Handwritten Chinese Character Blind Inpainting with Conditional Generative Adversarial Nets

It is very common to use a regular grid like Tian-zi-ge or Mi-zi-ge to help writing in Chinese handwriting environment, especially in education and postal area. Although regular grid is helpful for writing, it is a disaster for recognition. This paper focuses on handwritten Chinese character blind inpainting with regular grid and spot. To solve this problem, we use the recently proposed conditional generative adversarial nets (GANs). Different from the traditional engineering based method like line detection or edge detection, conditional GANs learn a map between target and training data. The generator reconstructs character directly from the data and the discriminator guides the training process to make the generated character more realistic. In this paper, we can automatically remove regular grid in handwritten Chinese character and reconstruct the character's strokes correctly. Moreover, the evaluation on classification task achieved a near state-of-the-art performance on the simulation database and got a convincing result on real world regular grid handwritten Chinese character database.
Rhythmical variation in Hungarian

The kinds of rhythmical variation I will examine in this paper can be observed in some double-accented Hungarian words, and can be exemplified by the changes that the stressing of the numeric compound ′tizen′három ‘thirteen’ may undergo when it is embedded in phrases. The stressing of this word may become tizen′három in the phrase ′pont tizen′három ‘exactly thirteen’ and ′ tizen′három in the phrase ′ tizen′három ′pont ‘thirteen points’. The two processes, however, are not quite symmetrical in their range of application; the first kind of change occurs in only a subset of the words affected by the second kind. The symbol ′ in the examples represents a pitch accent on the syllable whose orthographic form it precedes. These changes are the Hungarian counterparts of the two kinds of rhythmical variation in English, manifested, for instance, by the different realisations of the word ′thir′teen, when it is embedded in phrases like ′thirteen ′points or ′just thir′teen. However, while rhythmical variation in English is extremely widespread, in Hungarian it is restricted to certain classes of words. Besides, rhythmical variation in English lacks the asymmetry between the two processes; both kinds of change affect the same set of words. This article concentrates on rhythmical variation in Hungarian and on the implications of this variation for metrical theory.
Learning a Smart Convolutional Neural Network with High-Level Semantic Information

With the wide application of big data and the development of computer computing capability, deep Convolutional Neural Network (CNN) has been widely applied in the field of computer vision. The current architecture of deep neural network is becoming deeper and more complex for achieving a better performance. However, their natural disadvantages such as larger consumption of computation or memory, and longer run-time make CNN models difficult to be applied to the mobile and embedded devices. In this paper, we learn a Smart Convolutional Neural Network (S-CNN) under the guide of neurons' high-level semantic information distilled from a cumbersome neural network. S-CNN can be seen as an improved CNN model, which is with less consumption of computation and memory in the predicted process. We verify the superiority of S-CNN in terms of image classification task on three benchmarking datasets, including CIFAR-10, CIFAR-100 and SVHN. Experimental results clearly demonstrate that the proposed S-CNN can get an exciting performance compared with traditional CNN models.
Extending the Full Procrustes Distance to Anisotropic Scale in Shape Analysis

The full Procrustes distance between the configuration matrices of landmarks is the most fundamental landmark-based distance in shape analysis. To summarize, it is obtained by matching landmarks on a shape with those on another shape as closely as possible over the similarity transformations that consist of translation, rotation, and isotropic scaling. Thus, it considers similarity transformations only. Accordingly, it often does not work well for shapes skewed by non-similarity transformations. In this paper, we provide an efficient solution to this problem by extending the full Procrustes distance to anisotropic scale. With several shape datasets, we demonstrate that the extended full Procrustes distance is more effective in shape retrieval than typical distances, including the original full Procrustes distance.
Fundamentals of Urology

Fundamentals of Urology , edited by Jack Lapides and dedicated to Reed Nesbit, is a must for every medical student and should certainly have a place in the library of the surgical resident. All too often, urology is given a low priority in medical school curricula. In a few evenings of easy reading, Fundamentals of Urology can make the student feel comfortable and confident in his initial contact with the urological patient. Few subjects are left uncovered. The introductory chapters on the anatomy, physiology, and embryology of the genitourinary tract are excellent. In just 20 well-illustrated, concise chapters, the orderly discipline of genitourinary surgery is described. The workup of patients, whether it be for the more unusual diseases such as Cushing syndrome or more common disorders such as hematuria or urinary calculi, is clear and concise. A well-selected bibliography concludes each chapter so that the student may pursue a topic further
Hierarchical Co-salient Object Detection via Color Names

In this paper, a bottom-up and data-driven model is introduced to detect co-salient objects from an image pair. Inspired by the biologically-plausible across-scale architecture, we propose a multi-layer fusion algorithm to extract conspicuous parts from an input image. At each layer, two existing saliency models are first combined to obtain an initial saliency map, which simultaneously codes for the color names based surrounded cue and the background measure based boundary connectivity. Then a global color cue with respect to color names is invoked to refine and fuse single-layer saliency results. Finally, we exploit the color names based distance metric to measure the color consistency between a pair of saliency maps and remove those non-co-salient regions. The proposed model can generate both saliency and co-saliency maps. Experimental results show that our model performs favorably against 14 saliency models and 6 co-saliency models on the Image Pair data set.
A Deep Learning Approach to Appearance-Based Gaze Estimation under Head Pose Variations

In this paper, we propose a deep learning based gaze estimation algorithm that estimates the gaze direction from a single face image. The proposed gaze estimation algorithm is based on using multiple convolutional neural networks (CNN) to learn the regression networks for gaze estimation from the eye images. The proposed algorithm can provide accurate gaze estimation for users with different head poses, since it explicitly includes the head pose information into the proposed gaze estimation framework. The proposed algorithm can be widely used for appearance-based gaze estimation in practice. Our experimental results show that the proposed gaze estimation system improves the accuracy of appearance-based gaze estimation under head pose variations compared to the previous methods.
Robust Multi-scale ORB Algorithm in Real-Time Monocular Visual Odometry

In this paper, a novel multi-scale ORB algorithm with lower computation is proposed applied for increasing correct matches of feature points in visual odometry when image scale changes. Since ORB algorithm has little scale invariance for feature points matching, the visual odometry employing the ORB algorithm directly performs poorly in the position and orientation estimation. Therefore, the proposed algorithm combines the ORB with SURF by added the scale space. In addition, single layer non-maximum suppression is applied to the selection of stable feature points to decrease spending time in matching step. Experimental results present that the proposed algorithm achieves good matching performance in terms with scale invariance taking into consideration. It was found that the position estimation and the orientation estimation was improved compared to the visual odometry based on the ORB algorithm while the spend of time has only increased a little.
Predicting Segmentation "Easiness" from the Consistency for Weakly-Supervised Segmentation

Weakly-supervised segmentation has come to draw a lot of attention, since it costs very high to create pixel-wise an-notated image datasets for fully-supervised segmentation. Recently, it has achived great progress by the method of (re-)training of a fully-supervised segmentation model with roughly estimated inital masks which is proposed by Wei et al. [25]. However, the initial estimated masks tend to include some noise, which sometimes causes erroneous results. Therefore in this paper we focus on improving of quality of initial estimated masks for (re-)training of a fully-supervised segmentation model. We propose a novel algorithm to retrieve "good seeds" by predicting segmentation "Easiness" of images based on consistency among the out-puts with different conditions. We show that there is a trade-off between training data quality and the number of selected images, and our proposed method can improved the trained model using data augmentation. We have achieved state-of-the-art in a weakly-supervised segmentation setting on Pascal VOC 2012 segmentation benchmark dataset.
Clustering Based on the In-Tree Graph Structure and Affinity Propagation

A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree. This ND-based In-tree structure proves able to reveal the clustering structure underlying the dataset, except one imperfect place, that is, there are some undesired edges in this In-tree which require to be removed. Here, we propose an effective way to automatically remove the undesired edges in In-tree via an effective combination of the In-tree structure with affinity propagation (AP). The key for the combination is to add edges between the reachable nodes in In-tree before using AP to remove the undesired edges. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed method.
Self-Correction Method for Automatic Data Annotation

Supervised learning from data, especially using deep neural networks, usually needs tons of labeled data. Considering there are plenty of handwritten samples in the Internet, how to make use of the large amount of dirty data or make them cleaner? This paper presents self-correction learning method to automatically label dirty data. The methods lend inspiration from self-training framework. It trains the classifier iteratively on the dirty data, and shifts the cluster centers, corrects or deletes samples according to the estimated confidence during the iterations. Empirical results demonstrate that the proposed method can effectively improve the quality of the data and reduce great human annotation efforts.
Reweighted Low Rank Representation Based on Fractional-Order Function

Low Rank Representation (LRR) achieves state-of-the-art clustering performance via solving a nuclear norm minimization problem which is a convex relaxation of rank minimization. In this paper, we propose a unified fractional-order function based weighted nuclear norm minimization framework (FWNNM), which can approximate rank minimization better than nuclear norm minimization. Based on the unified framework, a fractional-order function is introduced to reweight the low rank representation (FRLRR) to further improve the lower rank representation of data. By imposing constraints on the eigenvalues of coefficient matrix, the proposed weights are embedded into the formulation to obtain the lower rank representation in each iteration. Experimental results demonstrate the advantage of FRLRR over state-of-the-art methods.
CNet: Context-Aware Network for Semantic Segmentation

Semantic segmentation is one of the great challenges in computer vision. Recently, deep convolutional neural networks (DCNNs) have achieved great success in most of the computer vision tasks. However, in terms of semantic segmentation, it is still difficult for the DCNN methods to take full advantage of context information and determine the fine boundaries of objects. In this paper, we propose a Context-aware Network (CNet), which utilizes robust context information to improve segmentation results. CNet has two significant components: 1) a feature collection module (FCM), which is constructed to extract low-level contextual features including texture, layout, boundary, local and global relationships by different receptive fields to complement high-level feature learning, and 2) a novel layer named ResGate, which is developed to select robust contextual features from the FCM. The two combined components can thoroughly explore context information to improve boundary segmentation accuracy. We evaluate the proposed method on the popular PASCAL VOC2012 dataset, and obtain promising performance compared with related methods, especially in the situation of similar objects or objects in complex scene.
Motion-Pose Recurrent Neural Network with Instantaneous Kinematic Descriptor for Skeleton Based Gesture Detection and Recognition

Skeleton based human gesture detection and recognition have been attracting increasing attention in the field of human action understanding. A number of approaches have been proposed to only explore skeleton inherent pose features by deep learning method. In this paper, we propose a novel instantaneous kinematic descriptor and a motion-pose recurrent neural network (RNN) for skeleton based gesture detection and recognition. Instead of the raw skeleton joint position as input, we propose instantaneous kinematic descriptor to represent not only the skeleton inherent pose but also the instantaneous movement at a current frame. Meanwhile, the proposed network is capable of transforming the gesture detection to the problem of frame labeling, which can model both the frame-wise dynamic motion and long-term temporal context of gestures. The proposed method is evaluated on the Chalearn LAP gesture dataset, and the result demonstrates that our method achieves the state-of-art performance in the task of skeleton-based gesture detection and recognition.
Path Following with Supervised Deep Reinforcement Learning

In this paper, we propose a fast adaptive learning method called supervised deep reinforcement learning to realize path following with high-dimensional input for autonomous driving task. We combine traditional feedback control method with deep reinforcement learning, the former providing a basic steering manipulation technique and the latter further improving the performance, which is similar with human's learning process. We validate our approach on computer simulating driving task. Experiments show that the fusion method steadily improves the performance based on the result of feedback control.
Human Pose Estimation via Multi-resolution Convolutional Neural Network

Human pose estimation is a challenging problem in computer vision tasks and shares all the difficulties of object detection. This paper focuses on the problems of estimating human pose in still images, including the various appearances and non-rigid body parts. To address these problems, we adopt a CNN to extract multi-scale part information. The appearance model is learned by CNN and the deformable model is computed based on appearance feature. Then, a Multi-Resolution Convolutional Neural Network (MR-CNN) is proposed to train and learn the multi-scale feature of each body part. This model is compared with the related work on the the Leeds Sport Dataset (LSP). The experimental results demonstrate the effectiveness of the proposed method.
Coarse-to-Fine Age Progression Based on Conditional GANs

This paper presents a novel method coined Coarse-to-Fine Age Progression based on Conditional GANs (CFAP). CFAP firstly employs the Illumination-Aware Age Progression (IAAP) to achieve the initial results. To overcome the problem of data limited, we select the fine synthetic results of IAAP to construct a series of images pairs (source age image and synthetic target age image) as training set. Then, the powerful image changes tool Conditional GANs is used to learn the aging pattern variations and further refine the aged results. Experimental results demonstrate the advantages of the proposed model compared with previous works.
Deep Domain Adaptation with a Few Samples for Face Identification

Recently, adaptation learning has shown its impressive performance on face identification. However, in most of them, the number of target domain samples always is large no matter whether they are labeled. In many real-world applications, there may be only scarce data we can use i.e. 2 or 3 samples per subject, and directly training with them will lead to over-fitting using DCNNs. To this end, we propose a novel deep domain adaptation approach, Re-stricted Parameter Learning (RPL), to address the challenge of training with scarce target data by restricting the adaptation learning procedure, i.e. reserving the effective part of parameters learned from source domain and re-optimizing them in target domain. Specifically, we transfer the knowledge from source domain to target domain in very deep network, obtaining satisfying results and avoiding over-fitting or structure damage which may be caused by directly training on a few target samples. Furthermore, RPL can effectively be performed when the data distribution in the source and target domain is quite different. To better demonstrate our RPL performances, we test our method on both the challenging databases LFW and Feret for face identification.
Probability Based Voting for Vanishing Point Detection

This paper describes a method to detect vanishing points based on voting with probability. We use a group of lines in stead of a single line to describe the line passes a set of feature points belonging to the same line segment and propose a probability distribution to describe the group of lines. We use it to estimate the probability of all the points on the image plane that can be considered on the detected line. We use the surface of a cube centered at the projection center as the space for voting. We accumulate the probability of each point on the image plane (including the points at infinity) calculated with all the detected lines and project it on to the cube surface. The vanishing points are then detected by finding the cells that are local maximum of the accumulated probabilities and have many lines passed them. Through the comparative experiments, we confirmed that our method could give accurate and stable results and our method is not sensitive to the resolution of the voting space.