Query: Intrinsic video and applications

Relevant passages:
Efficient Graph-Based Image Segmentation <=SEP=> This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.

Example-based video color grading <=SEP=> In most professional cinema productions, the color palette of the movie is painstakingly adjusted by a team of skilled colorists -- through a process referred to as color grading -- to achieve a certain visual look. The time and expertise required to grade a video makes it difficult for amateurs to manipulate the colors of their own video clips. In this work, we present a method that allows a user to transfer the color palette of a model video clip to their own video sequence. We estimate a per-frame color transform that maps the color distributions in the input video sequence to that of the model video clip. Applying this transformation naively leads to artifacts such as bleeding and flickering. Instead, we propose a novel differential-geometry-based scheme that interpolates these transformations in a manner that minimizes their curvature, similarly to curvature flows. In addition, we automatically determine a set of keyframes that best represent this interpolated transformation curve, and can be used subsequently, to manually refine the color grade. We show how our method can successfully transfer color palettes between videos for a range of visual styles and a number of input video clips.

High Accuracy Optical Flow Estimation Based on a Theory for Warping <=SEP=> We study an energy functional for computing optical flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far. Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optical flow estimation. We show that it is fairly insensitive to parameter variations, and we demonstrate its excellent robustness under noise. In Proc. 8th European Conference on Computer Vision, Springer LNCS 3024, T. Pajdla and J. Matas (Eds.), vol. 4, pp. 25-36, Prague, Czech Republic, May 2004 c © Springer-Verlag Berlin Heidelberg 2004 Received The Longuet-Higgins Best Paper Award.

A Naturalistic Open Source Movie for Optical Flow Evaluation <=SEP=> Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the imageand flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

Determining Optical Flow <=SEP=> Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.

Biometric-oriented Iris Identification Based on Mathematical Morphology <=SEP=> A new method for biometric identification of human irises is proposed in this paper. The method is based on morphological image processing for the identification of unique skeletons of iris structures, which are then used for feature extraction. In this approach, local iris features are represented by the most stable nodes, branches and endpoints extracted from the identified skeletons. Assessment of the proposed method was done using subsets of images from the University of Bath Iris Image Database (1000 images) and the CASIA Iris Image Database (500 images). Compelling experimental results demonstrate the viability of using the proposed morphological approach for iris recognition when compared to a state-of-the-art algorithm that uses a global feature extraction approach.

Genetic Programming + Unfolding Embryology in Automated Layout Planning <=SEP=> ABSTRACT Automated layout planning aims to the implementation of computational methods for the generation and the optimization of floor plans, considering the spatial configuration and the assignment of activities. Sophisticated strategies such as Genetic Algorithms have been implemented as heuristics of good solutions. However, the generative forces that derive from the social structures have been often neglected. This research aims to illustrate that the data that encode the layout’s social and cultural generative forces, can be implemented within an evolutionary system for the design of residential layouts. For that purpose a co-operative system was created, which is composed of a Genetic Programming algorithm and an agent-based unfolding embryology procedure that assigns activities to the spaces generated by the GP algorithm. The assignment of activities is a recursive process which follows instructions encoded as permeability graphs. Furthermore, the Ranking Sum Fitness evaluation method is proposed and applied for the achievement of multiobjective optimization. Its efficiency is tested against the Weighted-Sum Fitness function. The system’s results, both numerical and spatial, are compared to the results of a conventional evolutionary approach. This comparison showed that, in general, the proposed system can yield better solutions.

The Seattle Heart Failure Model: prediction of survival in heart failure. <=SEP=> BACKGROUND
Heart failure has an annual mortality rate ranging from 5% to 75%. The purpose of the study was to develop and validate a multivariate risk model to predict 1-, 2-, and 3-year survival in heart failure patients with the use of easily obtainable characteristics relating to clinical status, therapy (pharmacological as well as devices), and laboratory parameters.


METHODS AND RESULTS
The Seattle Heart Failure Model was derived in a cohort of 1125 heart failure patients with the use of a multivariate Cox model. For medications and devices not available in the derivation database, hazard ratios were estimated from published literature. The model was prospectively validated in 5 additional cohorts totaling 9942 heart failure patients and 17,307 person-years of follow-up. The accuracy of the model was excellent, with predicted versus actual 1-year survival rates of 73.4% versus 74.3% in the derivation cohort and 90.5% versus 88.5%, 86.5% versus 86.5%, 83.8% versus 83.3%, 90.9% versus 91.0%, and 89.6% versus 86.7% in the 5 validation cohorts. For the lowest score, the 2-year survival was 92.8% compared with 88.7%, 77.8%, 58.1%, 29.5%, and 10.8% for scores of 0, 1, 2, 3, and 4, respectively. The overall receiver operating characteristic area under the curve was 0.729 (95% CI, 0.714 to 0.744). The model also allowed estimation of the benefit of adding medications or devices to an individual patient's therapeutic regimen.


CONCLUSIONS
The Seattle Heart Failure Model provides an accurate estimate of 1-, 2-, and 3-year survival with the use of easily obtained clinical, pharmacological, device, and laboratory characteristics.

Can Twitter be used to predict county excessive alcohol consumption rates? <=SEP=> OBJECTIVES
The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.


METHODS
Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis.


RESULTS
Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption.


CONCLUSIONS
Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.

Open-Loop Precision Grasping With Underactuated Hands Inspired by a Human Manipulation Strategy <=SEP=> In this paper, we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of small objects. Taking inspiration from the human grasping strategy for picking up objects from a flat surface, we introduce the flip-and-pinch task, in which the hand picks up a thin object by flipping it into a stable configuration between two fingers. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can interact with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. Even when utilizing only open-loop kinematic playback, this approach is shown to be robust to variation in object size and hand position. Variation of up to 20° in orientation and 10 mm in hand height still result in experimental success rates of 80% or higher. These results suggest that the advantages of underactuated, adaptive robot hands can be carried over from basic grasping tasks to more dexterous tasks.

CHAPTER 5 SOFTWARE TESTING <=SEP=> As more extensively discussed in the Software Quality chapter of the Guide to the SWEBOK, the right attitude towards quality is one of prevention: it is obviously much better to avoid problems, rather than repairing them. Testing must be seen as a means primarily for checking whether the prevention has been effective, but also for identifying anomalies in those cases in which, for some reason, it has been not. It is perhaps obvious, but worth recognizing, that even after successfully completing an extensive testing campaign, the software could still contain faults; nor is defect free code a synonymous for quality product. The remedy to system failures that are experienced after delivery is provided by (corrective) maintenance actions. Maintenance topics are covered into the Software Maintenance chapter of the Guide to the SWEBOK.

Warehousing and Protecting Big Data: State-Of-The-Art-Analysis, Methodologies, Future Challenges <=SEP=> This paper proposes a comprehensive critical survey on the issues of warehousing and protecting big data, which are recognized as critical challenges of emerging big data research. Indeed, both are critical aspects to be considered in order to build truly, high-performance and highly-flexible big data management systems. We report on state-of-the-art approaches, methodologies and trends, and finally conclude by providing open problems and challenging research directions to be considered by future efforts.

Hierarchical Peer-To-Peer Systems <=SEP=> Structured peer-to-peer (P2P) lookup services—such as Chord, CAN, Pastry and Tapestry—organize peers into a flat overlay network and offer distributed hash table (DHT) functionality. In these systems, data is associated with keys and each peer is responsible for a subset of the keys. We study hierarchical DHTs, in which peers are organized into groups, and each group has its autonomous intra-group overlay network and lookup service. The groups themselves are organized in a top-level overlay network. To find a peer that is responsible for a key, the top-level overlay first determines the group responsible for the key; the responsible group then uses its intra-group overlay to determine the specific peer that is responsible for the key. After providing a general framework for hierarchical P2P lookup, we consider the specific case of a two-tier hierarchy that uses Chord for the top level. Our analysis shows that by designating the most reliable peers in the groups as superpeers, the hierarchical design can significantly reduce the expected number of hops in Chord. We also propose a scalable design for managing the groups and the superpeers.

An Optical 6-axis Force Sensor for Brain Function Analysis using fMRI <=SEP=> This paper presents an 6-axis optical force sensor which can be used in fMRI. Recently, fMRIs are widely used for studying human brain function. Simultaneous measurement of brain activity and peripheral information, such as grip force, enables more precise investigations in studies of motor function. However, conventional force sensors cannot be used in fMRI environment, since metal elements generate noise which severely contaminate the signals of fMRI. An optical 2-axis force sensor has been developed using photo sensors and optical fibers by Tada et. al.[1], that resolved these problems. The developed force sensor removed all magnetic components from the sensing part. It detected minute displacements by measure amount of light and light traveled through the optical fibers. However, there still remain several problems on this optical force sensor. Firstly, the accuracy is not high compared to the conventional force sensors. Secondly, the robustness is not enough against the contact force to the optical fibers. In this paper, the problems concerning to the accuracy and the sensor output stability has been improved by novel methods of fixing fibers and arithmetic circuit. Furthermore, an optical 6-axis force sensor is developed based on these improvements, and usefulness of our sensor for brain function analysis is confirmed in fMRI experimentations.

Pathologies in information bottleneck for deterministic supervised learning <=SEP=> Information bottleneck (IB) is a method for extracting information from one random variable X that is relevant for predicting another random variable Y . To do so, IB identifies an intermediate “bottleneck” variable T that has low mutual information I(X;T ) and high mutual information I(Y ;T ). The IB curve characterizes the set of bottleneck variables that achieve maximal I(Y ;T ) for a given I(X;T ), and is typically explored by optimizing the IB Lagrangian, I(Y ;T )− βI(X;T ). Recently, there has been interest in applying IB to supervised learning, particularly for classification problems that use neural networks. In most classification problems, the output class Y is a deterministic function of the input X , which we refer to as “deterministic supervised learning”. We demonstrate three pathologies that arise when IB is used in any scenario where Y is a deterministic function of X: (1) the IB curve cannot be recovered by optimizing the IB Lagrangian for different values of β; (2) there are “uninteresting” solutions at all points of the IB curve; and (3) for classifiers that achieve low error rates, the activity of different hidden layers will not exhibit a strict trade-off between compression and prediction, contrary to a recent proposal. To address problem (1), we propose a functional that, unlike the IB Lagrangian, can recover the IB curve in all cases. We finish by demonstrating these issues on the MNIST dataset.

Convolution Aware Initialization <=SEP=> Initialization of parameters in deep neural networks has been shown to have a big impact on the performance of the networks (Mishkin & Matas, 2015). The initialization scheme devised by He et al, allowed convolution activations to carry a constrained mean which allowed deep networks to be trained effectively (He et al., 2015a). Orthogonal initializations and more generally orthogonal matrices in standard recurrent networks have been proved to eradicate the vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of current initialization schemes do not take fully into account the intrinsic structure of the convolution operator. Using the duality of the Fourier transform and the convolution operator, Convolution Aware Initialization builds orthogonal filters in the Fourier space, and using the inverse Fourier transform represents them in the standard space. With Convolution Aware Initialization we noticed not only higher accuracy and lower loss, but faster convergence. We achieve new state of the art on the CIFAR10 dataset, and achieve close to state of the art on various other tasks.

A Comprehensive Survey on Bengali Phoneme Recognition <=SEP=>  SUST, ICERIE Abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed. Comparison among these methods is discussed.

Domain Ontology for Programming Languages <=SEP=> Ontology have become a relevant representation formalism and many application domains are  considering adopting them. This attention claims for methods for reusing domain knowledge resources in the development of domain ontologies. Accordingly, in this paper we discuss a general methodology to create domain ontology for more than one object oriented language (OOP) like Java, PHP and C++. A lot of software development methods specially Web applications have presented most of these methods that are focusing on the structure of distributed systems and security, in which they are connected through networks and the internet; resulting in more valuable business and critical assets stored, searched and manipulated by World Wide Web. The aims of this study building domain ontology for OOP language classes for different OOP languages or different versions of the same language is an exciting opportunity for researchers to access the information required under the constant increase in the volume of information disseminated on the Internet. By creating Ontology domain for OOP, we can Improve methods of viewing and 1 E-mail: iabuhassan@newsoft.ps 2 E-mail: akram.othman@gmail.com Article Info: Received : October 14, 2012. Revised : November 19, 2012 Published online : December 30, 2012 76 Domain Ontology for Programming Languages organizing information, improve the way of processing, in addition to increasing the vocabulary and their relationship to terminology as well as the rules used in natural language with OOP languages. The clear identification of the properties and relations of terms is the starting point to become Ontology domain. The importance of the domain Ontology among object oriented programming languages is that through the synthesis of these relationships or Ontology an OOP can be achieved through web by any junior programmers.

Cu pillar bumps as a lead-free drop-in replacement for solder-bumped, flip-chip interconnects <=SEP=> We evaluated Cu pillar bumps with a lead-free SnAg solder cap as 1st-level soldered interconnects in a standard organic BGA (ball grid array) flip chip package. Various pillar heights and solder volumes were investigated at a minimum pad pitch of 150 mum. Flip chip assembly followed the exact process as in the case of collapsible SnAg solder bumps, using an identical material set and identical equipment. Thanks to the properties of Cu pillar bumps, substrate design tolerances could be relaxed compared to solder bump interconnects of the same pitch. This, together with the fact that no solder-on-pad (SOP) is required for Cu pillar bumps, allows for lower substrate cost, which is a major factor in flip chip packaging cost. Cu pillar bumps also offer significantly higher current carrying capability and better thermal performance compared to solder bumps. We found that flip chip assembly with Cu pillar bumps is a robust process with regard to variations in assembly parameters, such as solder cap volume, flux depth, chip placement accuracy and substrate pad size. It is possible to attach the Cu pillar bumps to the metal lines of the non-SOP substrates with line spacing down to 40 mum plusmn15 mum without any solder bridging. It is shown that flip chip packages with suitable 150 mum-pitch Cu pillar bumps provide high mechanical and electrical reliability. No corrosion on the Cu pillars was found after humidity tests (MSL2 level and unbiased HAST for 192 h). Thermal cycling passed 2000 cycles, even at JEDEC JESD22-A104-B condition C (-65 degC to 150 degC). High Temperature Storage passed 2000 h at 150 degC. Cross sections reveal that after 1000 h at 150degC all Sn in the solder is transformed into Cu-Sn intermetallic compounds (IMCs). Preliminary electromigration test results at highly accelerated conditions (Tbump ap 177 degC, I = 0.8 A) show almost 2 orders of magnitude longer lifetime compared to SnAg solder bumps at 200 mum pitch. Cu pillar lifetimes at high current and temperature are expected to highly exceed those of solder bumps, because current crowding in the solder is avoided and solder is transformed into much more stable intermetallics.

Sarcasm Detection on Czech and English Twitter <=SEP=> This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language. We created a large Czech Twitter corpus consisting of 7,000 manually-labeled tweets and provide it to the community. We evaluate two classifiers with various combinations of features on both the Czech and English datasets. Furthermore, we tackle the issues of rich Czech morphology by examining different preprocessing techniques. Experiments show that our language-independent approach significantly outperforms adapted state-of-the-art methods in English (F-measure 0.947) and also represents a strong baseline for further research in Czech (F-measure 0.582).

MIMIC-CXR: A large publicly available database of labeled chest radiographs <=SEP=> Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient’s thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. However, a key challenge in the development of these techniques is the lack of sufficient data. Here we describe MIMIC-CXR, a large dataset of 371,920 chest x-rays associated with 227,943 imaging studies sourced from the Beth Israel Deaconess Medical Center between 2011 2016. Each imaging study can pertain to one or more images, but most often are associated with two images: a frontal view and a lateral view. Images are provided with 14 labels derived from a natural language processing tool applied to the corresponding free-text radiology reports. All images have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in medical computer vision.

Privacy and Security of Big Data: Current Challenges and Future Research Perspectives <=SEP=> Privacy and security of Big Data is gaining momentum in the research community, also due to emerging technologies like Cloud Computing, analytics engines and social networks. In response of this novel research challenge, several privacy and security of big data models, techniques and algorithms have been proposed recently, mostly adhering to algorithmic paradigms or model-oriented paradigms. Following this major trend, in this paper we provide an overview of state-of-the-art research issues and achievements in the field of privacy and security of big data, by highlighting open problems and actual research trends, and drawing novel research directions in this field.

A comparative study on content-based music genre classification <=SEP=> Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.

Attention-Fused Deep Matching Network for Natural Language Inference <=SEP=> Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence. Recent progress on this task only relies on a shallow interaction between sentence pairs, which is insufficient for modeling complex relations. In this paper, we present an attention-fused deep matching network (AF-DMN) for natural language inference. Unlike existing models, AF-DMN takes two sentences as input and iteratively learns the attention-aware representations for each side by multi-level interactions. Moreover, we add a selfattention mechanism to fully exploit local context information within each sentence. Experiment results show that AF-DMN achieves state-of-the-art performance and outperforms strong baselines on Stanford natural language inference (SNLI), multigenre natural language inference (MultiNLI), and Quora duplicate questions datasets.

Turing Computability with Neural Nets <=SEP=> This paper shows the existence of a finite neural network, made up of sigmoidal nenrons, which simulates a nniversal Turing machine. It is composed of less then lo5 synchronously evolving processors, interconnected linearly. High-order connections are not required.

“Exascale Computing and Big Data: The Next Frontier,” <=SEP=> For scientific and engineering computing, exascale (10 operations per second) is the next proxy in the long trajectory of exponential performance increases that has continued for over half a century. Similarly, large-scale data preservation and sustainability within and across disciplines, metadata creation and multidisciplinary fusion, and digital privacy and security define the frontiers of big data. Solving the myriad technical, political and economic challenges of exascale computing will require coordinated planning across government, industry and academia, commitment to data sharing and sustainability, collaborative research and development, and recognition that both international competition and collaboration will be necessary.

An event-based conceptual model for context-aware movement analysis <=SEP=> Current tracking technologies enable collection of data describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods, etc. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach.

Efficient Discovery of Ontology Functional Dependencies <=SEP=> Functional Dependencies (FDs) define attribute relationships based on syntactic equality, and, when used in data cleaning, they erroneously label syntactically different but semantically equivalent values as errors. We enhance dependency-based data cleaning with Ontology Functional Dependencies (OFDs), which express semantic attribute relationships such as synonyms and is-a hierarchies defined by an ontology. Our technical contributions are twofold: 1) theoretical foundations for OFDs, including a set of sound and complete axioms and a linear-time inference procedure, and 2) an algorithm for discovering OFDs (exact ones and ones that hold with some exceptions) from data that uses the axioms to prune the exponential search space in the number of attributes. We demonstrate the efficiency of our techniques on real datasets, and we show that OFDs can significantly reduce the number of false positive errors in data cleaning techniques that rely on traditional FDs.

Attention estimation by simultaneous analysis of viewer and view <=SEP=> This paper introduces a system for estimating the attention of a driver wearing a first person view camera using salient objects to improve gaze estimation. A challenging data set of pedestrians crossing intersections has been captured using Google Glass worn by a driver. A challenge unique to first person view from cars is that the interior of the car can take up a large part of the image. The proposed system automatically filters out the dashboard of the car, along with other parts of the instrumentation. The remaining area is used as a region of interest for a pedestrian detector. Two cameras looking at the driver are used to determine the direction of the driver's gaze, by examining the eye corners and the center of the iris. This coarse gaze estimation is then linked to the detected pedestrians to determine which pedestrian the driver is focused on at any given time.

Distributed Neural Networks for Internet of Things: The Big-Little Approach <=SEP=> Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback for neural networks. In order to exploit the apparent trade-off between utilizing the available though limited embedded computing power of the IoT devices at high speed/stable latency and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes can be used to calculate an output on the embedded devices, while a more specific classification can be calculated in the Cloud only when required. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.