waashk/dblp huggingface dataset:
image recolorization for the colorblind. in this paper, we propose a new re-coloring algorithm to enhance the accessibility for the color vision deficient (or colorblind). compared to people with normal color vision, people with color vision deficiency (cvd) have difficulty in distinguishing between certain combinations of colors. this may hinder visual communication owing to the increasing use of colors in recent years. to address this problem, we re-color the image to preserve visual detail when perceived by people with cvd. we first extract the representing colors in an image. then we find the optimal mapping to maintain the contrast between each pair of these representing colors. the proposed algorithm is image content dependent and completely automatic. experimental results on natural images are illustrated to demonstrate the effectiveness of the proposed re-coloring algorithm.
data-driven voice soruce waveform modelling. this paper presents a data-driven approach to the modelling of voice source waveforms. the voice source is a signal that is estimated by inverse-filtering speech signals with an estimate of the vocal tract filter. it is used in speech analysis, synthesis, recognition and coding to decompose a speech signal into its source and vocal tract filter components. existing approaches parameterize the voice source signal with physically- or mathematically-motivated models. though the models are well-defined, estimation of their parameters is not well understood and few are capable of reproducing the large variety of voice source waveforms. here we present a data-driven approach to classify types of voice source waveforms based upon their melfrequency cepstrum coefficients with gaussian mixture modelling. a set of “prototype” waveform classes is derived from a weighted average of voice source cycles from real data. an unknown speech signal is then decomposed into its prototype components and resynthesized. results indicate that with sixteen voice source classes, low resynthesis errors can be achieved.
sound field reproduction around a scatterer in reverberation. we devise a method for sound field reproduction (sfr) around a solid object in a reverberant room. until now, work have focussed on reproducing sound in an empty listening space and, for the most part, in non-reverberant environments. however, in a reverberant, room as soon as a listener steps into the space he alters his acoustic environment, generating a sound component which is body-scattered and successively reverberated throughout the room. building on the model of the sound field around a solid sphere in free space, we extend to reproduction around a human head in a reverberant room. in doing so, we show the relationship between the pressure matching and mode matching approaches of sfr.
using speech transformation to increase speech intelligibility for the hearing- and speaking-impaired. we present two speech transformation approaches designed to increase the intelligibility of speech. the first approach is used in the context of increasing the intelligibility of conversationally spoken speech for hearing-impaired listeners. an initial experiment showed that a relatively simple mapping function can map spectral features of conversationally spoken speech closer to context-equivalent spectral features of clearly spoken speech. the second approach aims to increase the intelligibility of speaking-impaired individuals by the general population. results of listening tests indicated that although an intelligibility increase was not achieved, listeners preferred the transformed speech of the proposed system over that of an alternative system.
compressive confocal microscopy. in this paper, a new framework for confocal microscopy based on the novel theory of compressive sensing is proposed. unlike wide field microscopy or conventional parallel beam confocal imaging systems that use charge-coupled devices (ccd) as acquisition devices in addition to complex mechanical scanning system, the proposed compressive confocal microscopy is a kind of parallel beam confocal imaging system which exploits the rich theory of compressive sensing by using a single pixel detector and a digital micromirror device (dmd) to capture linear projections of the in-focus image. with the proposed system, confocal imaging of high optical sectioning ability can be achieved at sub-nyquist sampling rates. theoretical analysis, simulations and experimental results are shown to demonstrate the characteristics and potential of the proposed approach.
statistical analysis of amplitude modulation in speech signals using an am-fm model. several studies have been dedicated to the analysis and modeling of am-fm modulations in speech and different algorithms have been proposed for the exploitation of modulations in speech applications. this paper details a statistical analysis of amplitude modulations using a multi-band am-fm analysis framework. the aim of this study is to analyze the phonetic- and speaker-dependency of modulations in the amplitude envelope of speech resonances. the analysis focuses on the dependence of such modulations on acoustic features such as, fundamental frequency, formant proximity, phone identity, as well as, speaker identity and contextual features. the results show that the amplitude modulation index of a speech resonance is mainly a function of the speaker-s average fundamental frequency, the phone identity, and the proximity between neighboring formant resonances. the results are especially relevant for speech and speaker recognition application employing modulation features.
detecting lsb matching by characterizing the amplitude of histogram. in this paper, we present an improved method for detecting lsb matching steganography in gray-scale image. our improvements focus on three aspects: (1) instead of using the amplitude of local extrema of the image's histogram in the previous work, we turn to considering the sum of the amplitude of each point in the histogram; (2) incorporating the calibration (downsample) technique with the current method; (3) the sum/difference image (which is defined as the sum or difference of two adjacent pixels in the original image) is taken into consideration to provide additional statistical features. extensive experimental results show that the novel steganalyzer out-performs the previous ones.
cross-lingual speech recognition under runtime resource constraints. this paper proposes and compares four cross-lingual and bilingual automatic speech recognition techniques under the constraint that only the acoustic model (am) of the native language is used at runtime. the first three techniques fall into the category of lexicon conversion where each phoneme sequence (phs) in the foreign language (fl) lexicon is mapped into the native language (nl) phoneme sequence. the first technique determines the phs mapping through the international phonetic alphabet (ipa) features; the second and third techniques are data-driven. they determine the mapping by converting the phs into corresponding context-independent and context-dependent hidden markov models (hmms) respectively and searching for the nl phs with the least kullback-leibler divergence (kld) between the hmms. the fourth technique falls into the category of am merging where the fl's am is merged into the nl's am by mapping each senone in the fl's am to the senone in the nl's am with the minimum kld. we discuss the strengths and limitations of each technique developed, report empirical evaluation results on recognizing english utterances with a korean recognizer, and demonstrate the high correlation between the average kld and the word error rate (wer). the results show that the am merging technique performs the best, achieving 60% relative wer reduction over the ipa-based technique.
learning on demand - course lecture distillation by information extraction and semantic structuring for spoken documents. this paper presents a new approach of organizing the course lectures (as spoken documents) for efficient learning on demand by the users. by the properly matching the course lectrues with the slides used, we divide the course lectures into hierarchical “major segments” with variable length based on the tiopics discussed. key term extraction, hierarhical summarization and semantic structuring are then performed over these “major segments”. a key term graph is also constructed, based on which the various major segments of the course can be linked. in this way, the user can ask questions to the system, and develop his own road map of learning the knowledge he needs considring his available time and his background knowledge, based on the semantic structure provided by the system. a preliminary prototype system has been successfully developed with encouraging initial test results.
improved speaker diarization system for meetings. in this paper, we investigate new approaches to improve speech activity detection, speaker segmentation and speaker clustering. the main idea behind them is to deal with the problem of speaker diarization for meetings where error rates are relatively high. in opposition to existing methods, a new iterative scheme is proposed considering those three tasks as only one problem. new bidirectional source segmentation is proposed based on the glr/bic method. the well-known bic clustering is also reviewed and a new unsupervised post-processing is added to increase clusters purity. those new proposals applied on meeting data show a relative improvement of about 40% compared to a standard speaker diarization system.
improving mispronunciation detection using machine learning. in this paper, we investigate the problem of mispronunciation detection by considering the influence of speaker and syllables. machine learning techniques are used to make our method more convenient and flexible for new features, such as syllables normalization. the experimental results on our database, consisting of 9898 syllables pronounced by 100 speakers, show the effectiveness of our method by reducing the average false acceptance rate (far) by 42.5% using data set generated by model without adaptation to observation set and reducing average far by 32.5% using data set generated by model with adaptation to observation set.
preface. there are times when those who have attended a symposium as well as those who purchase the proceedings afterwards, wish to communicate directly with the author or authors of a specific paper or even a session chairman. therefore, for your convenience we have included the following list of names and addresses.
editorial. from a mere technical point of view, the tv/video screen comes alive by a controlled beam of electrons in the cathode-ray tube. for "energie!" an uncontrolled high-voltage discharge of approximately 30.000 volts exposes photographic paper, which is then arranged in time to create new visual systems of electron organization. even though the result is abstract, it tells a universal story older than the world itself.
data association and occlusion handling for vision-based people tracking by mobile robots. this paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. first, an adaptive colour model is incorporated into the measurement model of the tracker. second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). third, explicit occlusion handling is incorporated into the tracker. the paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets.
towards modelling complex robot training tasks through system identification. previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using narmax system identification, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting - the robot responds directly to the sensor stimuli without having internal states or memory. however, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. in these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. the contribution of this paper to knowledge is to show how fundamental, simple narmax models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach. we argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. to achieve this we propose a novel method which relates distinctive locations in the environment to the state of the robot, using an unsupervised clustering algorithm. once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: we obtain a polynomial model which models the complex task as a function of predefined low level sensor-motor controllers and raw sensory data. the proposed method has been used to teach scitos g5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning.
6d scan registration using depth-interpolated local image features. this paper describes a novel registration approach that is based on a combination of visual and 3d range information. to identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3d laser scanner. the matched depth-interpolated image features allow one to apply registration with known correspondences. we compare several icp variants in this paper and suggest an extension that considers the spatial distance between matching features to eliminate false correspondences. experimental results are presented in both outdoor and indoor environments. in addition to pair-wise registration, we also propose a global registration method that registers all scan poses simultaneously.
novel solutions for global urban localization. in this study, novel solutions to global urban localization problem is proposed and examined rigorously. classical approaches including particle filter, mixture of gaussians, as well as novel solutions like viterbi algorithm and differential evolution are evaluated. the contribution of this paper is twofold: the viterbi algorithm is extended by exploiting the structure of the problem at hand that is the states are partially connected temporally. differential evolution is modified by taking into account the covariance matrix of states. thus states encoded in genes are only allowed to interact locally within the region described by covariance matrix. this prevents the differential evolution from getting trapped into false maxima in the early stages of optimization. finally, it is demonstrated with extensive experiments that solution of global urban localization problem is possible.
sift, surf & seasons: appearance-based long-term localization in outdoor environments. in this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. we investigate a straightforward method that relies on local image features to compare single-image pairs. we first look into which of the dominating image feature algorithms, sift or the more recent surf, that is most suitable for this task. we then fine-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. the final localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. the final localization rate in the single-image matching, cross-seasonal case is between 80% to 95%.
structural synthesis of 5 dofs 3t2r parallel manipulators with prismatic actuators on the base. a method is presented to synthesize 5 degrees of freedom (dofs) of 3 translational and 2 rotational (3t2r) parallel kinematic structures. this method is based on the theory of linear transformation and geometrical analysis. central to this method is a set of novel 5 dofs 3t2r parallel mechanisms (pms). based on the legs configuration, the generated mechanisms are classified. moreover, the promising mechanisms of each class are introduced with respect to some criteria, i.e.: (a) degree of coupling between the actuators and degrees of freedom; (b) easy kinematics and control command; (c) easy construction (or low cost construction); and, (d) manufacturability. with reference to these criteria, some discussions are given on the promising mechanisms. finally, to demonstrate the role of legs configuration in relation to the characteristics of a manipulator, the kinematic analysis of two of the introduced mechanisms is compared.
planar bipedal locomotion control based on state discretization. in this paper, a new control method for a planar bipedal robot, which we call graph-based model predictive control, is proposed. this method makes use of a directed graph constructed on the state space of the robot. the vertices of the directed graph are called waypoints, and they serve as intermediate target states to compose complex motions of the robot. by simply tracing the directed edges of the graph, one can achieve model predictive control over the waypoint set. such a directed graph is pre-designed and stored into the controller's memory to significantly reduce the computational effort required in real time. in addition, by constructing multiple directed graphs based on different objective functions, one can design multiple motions and switching trajectories among them in a uniform way. the proposed method is applied to variable-speed walking control of a bipedal walker on a two-dimensional plane, and its effectiveness is verified by numerical simulations.
human-robot communication for collaborative decision making - a probabilistic approach. humans and robots need to exchange information if the objective is to achieve a task collaboratively. two questions are considered in this paper: what and when to communicate. to answer these questions, we developed a human-robot communication framework which makes use of common probabilistic robotics representations. the data stored in the representation determines what to communicate, and probabilistic inference mechanisms determine when to communicate. one application domain of the framework is collaborative human-robot decision making: robots use decision theory to select actions based on perceptual information gathered from their sensors and human operators. in this paper, operators are regarded as remotely located, valuable information sources which need to be managed carefully. robots decide when to query operators using value-of-information theory, i.e. humans are only queried if the expected benefit of their observation exceeds the cost of obtaining it. this can be seen as a mechanism for adjustable autonomy whereby adjustments are triggered at run-time based on the uncertainty in the robots' beliefs related to their task. this semi-autonomous system is demonstrated using a navigation task and evaluated by a user study. participants navigated a robot in simulation using the proposed system and via classical teleoperation. results show that our system has a number of advantages over teleoperation with respect to performance, operator workload, usability, and the users' perception of the robot. we also show that despite these advantages, teleoperation may still be a preferable driving mode depending on the mission priorities.
online topological map building and qualitative localization in large-scale environment. a novel topological map representation as well as an online map construction approach is presented in this paper. by virtue of the topological map whose nodes are represented with the free beams of the laser range finder together with the visual scale-invariant features, the mobile robot can autonomously navigate in unknown, large-scale and indoor environments. different from the traditional navigation methods that rely on precise global localization, the robot locates itself qualitatively by location recognition rather than calculating its global pose in the world reference frame. by combining the reactive navigational method, beam curvature method (bcm), with the topological map, a smooth, real-time navigation without precise localization can be realized.
continuous free-crab gaits for hexapod robots on a natural terrain with forbidden zones: an application to humanitarian demining. autonomous robots are leaving the laboratories to master new outdoor applications, and walking robots in particular have already shown their potential advantages in these environments, especially on a natural terrain. gait generation is the key to success in the negotiation of natural terrain with legged robots; however, most of the algorithms devised for hexapods have been tested under laboratory conditions. this paper presents the development of crab and turning gaits for hexapod robots on a natural terrain characterized by containing uneven ground and forbidden zones. the gaits we have developed rely on two empirical rules that derive three control modules that have been tested both under simulation and by experiment. the geometrical model of the silo-6 walking robot has been used for simulation purposes, while the real silo-6 walking robot has been used in the experiments. this robot was built as a mobile platform for a sensory system to detect and locate antipersonnel landmines in humanitarian demining missions.
agent formations in 3d spaces with communication limitations using an adaptive q-structure. in this article, we further extend the queue-formation structure (or q-structure) in 3d spaces with additional features including: (i) specifying orientation information, (ii) a mechanism for forming sub-formations before the convergence into the final formation, and (iii) adapting the communication structure when communications are limited. the virtual bobber-agents are used to guide each vehicle toward the appropriate queue, by acting as intermediate targets. in addition, virtual constellation-agents bias the motion of each vehicle to within a user-defined cone to the front of the vehicle so that abrupt direction changes are avoided as far as possible. the proposed scheme relies mainly on simple behaviors between embodied and virtual agents and is computationally inexpensive method. extensive simulations show the effectiveness of the proposed method.
distributed control of multi-robot systems using bifurcating potential fields. the distributed control of multi-robot systems has been shown to have advantages over that of conventional single-robot systems. these include scalability, flexibility and robustness to failures. this paper considers pattern formation and reconfigurability in a multi-robot system using a new control algorithm developed through bifurcating potential fields. it is shown how various patterns can be achieved autonomously through a simple free parameter change, with the stability of the system proven to ensure that desired behaviours always occur.