Query: The POSTECH face database (PF07) and performance evaluation

Relevant passages:
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection <=SEP=> We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space—if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher’s Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed “Fisherface” method has error rates that are lower than those of the Eigenface technique for tests on the Harvard and Yale Face Databases. Index Terms —Appearance-based vision, face recognition, illumination invariance, Fisher’s linear discriminant. —————————— ✦ ——————————

The CMU Pose, Illumination, and Expression Database <=SEP=> In the Fall of 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this the CMU Pose, Illumination, and Expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.

Eigenfaces for Recognition <=SEP=> We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

Probabilistic Visual Learning for Object Representation <=SEP=> We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands.

Combining Models and Exemplars for Face Recognition : An Illuminating Example <=SEP=> We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.

Hardware, Design and Implementation Issues on a Fpga-Based Smart Camera <=SEP=> Processing images to extract useful information in real-time is a complex task, dealing with large amounts of iconic data and requiring intensive computation. Smart cameras use embedded processing to save the host system from the low-level processing load and to reduce communication flows and overheads. Field programmable devices present special interest for smart cameras design: flexibility, reconfigurability and parallel processing skills are some specially important features. In this paper we present a FPGA-based smart camera research platform. The hardware architecture is described, and some design issues are discussed. Our goal is to use the possibility to reconfigure the FPGA device in order to adapt the system architecture to a given application. To that, a design methodology, based on pre-programmed processing elements, is proposed and sketched. Some implementation issues are discussed and a template tracking application is given as example, with its experimental results.

Oboe: auto-tuning video ABR algorithms to network conditions <=SEP=> Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.

An efficient method of license plate location <=SEP=> License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method. 2005 Elsevier B.V. All rights reserved.

How to Increase Users' Social Commerce Engagement? A Technology Attractiveness Model <=SEP=> With the proliferation of social networking and electronic commerce, social commerce helps people engage in various forms of online social commercial activities through sharing their product or service knowledge and experiences. A better understanding of users’ engagement in social commerce websites thus become increasingly important. Based on the attractiveness theory, this study proposes a research model that highlights the unique role of technology attractiveness, including task, social, and physical attractiveness, in promoting user involvement, which in turn affects social commerce engagement. Results demonstrate that users’ perceptions of technology attractiveness are positively associated with their involvement with social commerce websites, and further stimulate engagement. In addition, website involvement partially and fully mediates the effects of social and physical attractiveness, respectively, on social commerce engagement. The limitations and implications of this study for research and practice are further discussed.

Teachable robots: Understanding human teaching behavior to build more effective robot learners <=SEP=> While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot’s learning behavior.

Kinematics and Jacobian analysis of a 6UPS Stewart-Gough platform <=SEP=> In this paper a complete kinematics analysis of a 6UPS Stewart-Gough platform is performed. A new methodology to deal with the forward kinematics problem based on a multibody formulation and a numerical method is presented, resulting in a highly efficient formulation approach. The idea of a multibody formulation is basically to built for each joint that links the body of the robot, a set of equations that define the constraints on their movement so that the joint variables are included in these equations. Once the constraint function is defined, we may use a numerical method to find the kinematic solution. In order to avoid the problem reported with the orientation (Euler's Angles) of the moving platform, a generalized coordinates vector using quaternions is applied. Then, the Jacobian analysis using reciprocal screw systems is described and the workspace is determined. Finally, a design of a simulator using MATLAB as programming tool is presented.

Geometry-Driven Diffusion in Computer Vision <=SEP=> Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

3D face matching and registration based on hyperbolic Ricci flow <=SEP=> 3D surface matching is fundamental for shape analysis. As a powerful method in geometric analysis, Ricci flow can flexibly design metrics by prescribed target curvature. In this paper we describe a novel approach for matching surfaces with complicated topologies based on hyperbolic Ricci flow. For surfaces with negative Euler characteristics, such as a human face with holes (eye contours), the canonical hyperbolic metric is conformal to the original and can be efficiently computed. Then the surface can be canonically decomposed to hyperbolic hexagons. By matching the corresponding hyperbolic hexagons, the matching between surfaces can be easily established. Compared to existing methods, hyperbolic Ricci flow induces diffeomorphisms between surfaces with complicated topologies with negative Euler characteristics, while avoiding singularities. Furthermore, all the boundaries are intrinsically mapped to hyperbolic lines as alignment constraints. Finally, we demonstrate the applicability of this intrinsic shape representation for 3D face matching and registration.

Focusing Attention: Towards Accurate Text Recognition in Natural Images <=SEP=> Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon “attention drift”. To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods.

The weight consistency matrix framework for general non-binary LDPC code optimization: Applications in flash memories <=SEP=> Transmission channels underlying modern memory systems, e.g., Flash memories, possess a significant amount of asymmetry. While existing LDPC codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are fairly inadequate. We propose a new, general, combinatorial framework for the analysis and design of non-binary LDPC (NB-LDPC) codes for asymmetric channels. We introduce a refined definition of absorbing sets, which we call general absorbing sets (GASs), and an important subclass of GASs, which we refer to as general absorbing sets of type two (GASTs). Additionally, we study the combinatorial properties of GASTs. We then present the weight consistency matrix (WCM), which succinctly captures key properties in a GAST. Based on these new concepts, we then develop a general code optimization framework, and demonstrate its effectiveness on the realistic highly-asymmetric normal-Laplace mixture (NLM) Flash channel. Our optimized codes enjoy over one order (resp., half of an order) of magnitude performance gain in the uncorrectable BER (UBER) relative to the unoptimized codes (resp. the codes optimized for symmetric channels).

An approach to interactive deep reinforcement learning for serious games <=SEP=> Serious games receive increasing interest in the area of e-learning. Their development, however, is often still a demanding, specialized and arduous process, especially when regarding reasonable non-player character behaviour. Reinforcement learning and, since recently, also deep reinforcement learning have proven to automatically generate successful AI behaviour to a certain degree. These methods are computationally expensive and hardly scalable to various complex serious game scenarios. For this reason, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts' knowledge to guide the learning process. Thereby, we aim to create a synergistic combination of experts and emergent cognitive systems. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a framework.

Gesture recognition using kinect for sign language translation <=SEP=> Sign Language is a widely used method of communication among the community of deaf-mute people. It contains some series of body gestures, which enables a person to interact without the need of spoken words. Although the use of sign language is very popular among the deaf-mute people but the other communities don't even try to learn it, this creates a gulf of communication and hence becomes a cause of the isolation of physically impaired people. The problem creates a requirement of a system which can facilitate a way of communication between these two communities. This paper presents a novel method for identification of an isolated sign language gesture using Microsoft Kinect. This paper presents the way of extracting some highly robust features from the depth image provided by Kinect and to use them in creating a robust and accurate gesture recognition system, for the purpose of sign language translation. Apart from translation, the proposed system also opens the doors of endless applications in the field of Human Computer Interaction. The proposed algorithm helps in translating a sign language gesture performed by a user, which in-turn can be used as an input for different types of applications.

Software and Algorithms for Graph Queries on Multithreaded Architectures <=SEP=> Search-based graph queries, such as finding short paths and isomorphic subgraphs, are dominated by memory latency. If input graphs can be partitioned appropriately, large cluster-based computing platforms can run these queries. However, the lack of compute-bound processing at each vertex of the input graph and the constant need to retrieve neighbors implies low processor utilization. Furthermore, graph classes such as scale-free social networks lack the locality to make partitioning clearly effective. Massive multithreading is an alternative architectural paradigm, in which a large shared memory is combined with processors that have extra hardware to support many thread contexts. The processor speed is typically slower than normal, and there is no data cache. Rather than mitigating memory latency, multithreaded machines tolerate it. This paradigm is well aligned with the problem of graph search, as the high ratio of memory requests to computation can be tolerated via multithreading. In this paper, we introduce the multithreaded graph library (MTGL), generic graph query software for processing semantic graphs on multithreaded computers. This library currently runs on serial machines and the Cray MTA-2, but Sandia is developing a run-time system that will make it possible to run MTGL-based code on symmetric multiprocessors. We also introduce a multithreaded algorithm for connected components and a new heuristic for inexact subgraph isomorphism We explore the performance of these and other basic graph algorithms on large scale-free graphs. We conclude with a performance comparison between the Cray MTA-2 and Blue Gene/Light for s-t connectivity.

The hymen morphology in normal newborn Saudi girls. <=SEP=> BACKGROUND
Hymen morphology has a medico-legal importance. In view of the lack of national norms, establishing the hymen morphology of Saudi newborn infants is essential.


SUBJECTS AND METHODS
Over a period of 4 months, the genitalia of 345 full-term female newborn infants were examined to determine the shape of the hymen. A previously described labia traction technique was used to classify the hymen morphology into annular, sleeve-like, fimbriated, crescentric, and other types.


RESULTS
The hymen was present in all 345 female newborn infants examined. A total of 207 (60%) were of the annular type, 76 (22%) were sleeve-like, 43 (12.5%) fimbriated, 17 (4.9%) crescentric, and 2 (0.6%) of other types.


CONCLUSION
The most common hymen morphology in Saudi newborn girls was annular, followed by sleeve-like, fimbriated, and crescentric. This study may be the first to define normal configuration of the hymen in this community.

Self-Attentive Feature-Level Fusion for Multimodal Emotion Detection <=SEP=> Multimodal emotion recognition is the task of detecting emotions present in user-generated multimedia content. Such resources contain complementary information in multiple modalities. A stiff challenge often faced is the complexity associated with feature-level fusion of these heterogeneous modes. In this paper, we propose a new feature-level fusion method based on self-attention mechanism. We also compare it with traditional fusion methods such as concatenation, outer-product, etc. Analyzed using textual and speech (audio) modalities, our results suggest that the proposed fusion method outperforms others in the context of utterance-level emotion recognition in videos.

Provenance: On and Behind the Screens <=SEP=> Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.

Using Genetic Algorithm to Solve Game of Go-Moku <=SEP=> Genetic algorithm is a stochastic parallel beam search that can be applied to many typical search problems. This paper describes a genetic algorithmic approach to a problem in artificial intelligence. During the process of evolution, the environment cooperates with the population by continuously making itself friendlier so as to lower the evolutionary pressure. Evaluations show the performance of this approach seems considerably effective in solving this type of board games. Game-playing programs are often described as being a combination of search and

How to Normalize Co-Occurrence Data ? An Analysis of Some Well-Known Similarity Measures Nees <=SEP=> AND

An improved algorithm for feature selection using fractal dimension <=SEP=> Dimensionality reduction is an important issue in data mining and machine learning. Traina[1] proposed a feature selection algorithm to select the most important attributes for a given set of n-dimensional vectors based on correlation fractal dimension. The author used a kind of multi-dimensional “quad-tree” structure to compute the fractal dimension. Inspired by his work, we propose a new and simpler algorithm to compute the fractal dimension, and design a novel and faster feature selection algorithm using correlation fractal dimension, whose time complexity is lower than that of Traina’s. The main idea is when computing the fractal dimension of (d-1)-dimensional data, the intermediate generated results of the extended d-dimensional data is reused. It inherits the desirable properties described as in [1]. Also, Our algorithm does not require the grid sizes decrease by half as the original “quad-tree” algorithm. Experiments show our feature selection algorithm has a good efficiency over the test dataset.

Advancing interactive collaborative mediums through tele-immersive dance (TED): a symbiotic creativity and design environment for art and computer science <=SEP=> The Tele-immersive Dance Environment (TED) is a geographically distributed, real-time 3-D virtual room where multiple participants interact independent of physical distance. TED, a highly interactive collaborative environment, offers digital options with multiple viewpoints, enhancing the creative movement composition involved with dance choreography. A symbiotic relationship for creativity and design exists between dance artists and computer scientists as the tele-immersive environment is analyzed as a creativity and learning tool. We introduce the advancements of the interactive digital options, new interface developments, user study results, and the possibility of a computational model for human creativity through Laban Movement Analysis.

A Survey on Security for Mobile Devices <=SEP=> Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.

Elliptic operator for shape analysis <=SEP=> Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt a classical operator from quantum mechanics to the field of shape analysis where we suggest to integrate a scalar function through a unified elliptical Hamiltonian operator. We study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. Then, we evaluate the resulting spectral basis for different applications such as mesh compression and shape matching. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated by the proposed framework that outperforms existing spectral methods, for example, when applied to shape matching benchmarks.

Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation <=SEP=> Ship classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new ship classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation classification (SRC) method is exploited. In particular, to describe the ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).

End-to-End Blind Image Quality Assessment Using Deep Neural Networks <=SEP=> We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks—a distortion identification network and a quality prediction network—sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.

Quality, productivity and economic benefits of software reuse: a review of industrial studies <=SEP=> Systematic software reuse is proposed to increase productivity and software quality and lead to economic benefits. Reports of successful software reuse programs in industry have been published. However, there has been little effort to organize the evidence systematically and appraise it. This review aims to assess the effects of software reuse in industrial contexts. Journals and major conferences between 1994 and 2005 were searched to find observational studies and experiments conducted in industry, returning eleven papers of observational type. Systematic software reuse is significantly related to lower problem (defect, fault or error) density in five studies and to decreased effort spent on correcting problems in three studies. The review found evidence for significant gains in apparent productivity in three studies. Other significant benefits of software reuse were reported in single studies or the results were inconsistent. Evidence from industry is sparse and combining results was done by vote-counting. Researchers should pay more attention to using comparable metrics, performing longitudinal studies, and explaining the results and impact on industry. For industry, evaluating reuse of COTS or OSS components, integrating reuse activities in software processes, better data collection and evaluating return on investment are major challenges.