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Yemez, Yücel

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Yücel

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Researcher Of Publications: search.filters.isAuthorOfPublication.23c08ce5-6539-43b2-a2fa-ce7e80c2b52d

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Now showing 1 - 10 of 121
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    Subspace-based techniques for retrieval of general 3D models
    (IEEE, 2009) Sankur, Bülent; Dutaǧac, Helin; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; 107907
    In this paper we investigate the potential of subspace techniques, such as, PCA, ICA and NMF in the case of indexing and retrieval of generic 3D models. We address the 3D shape alignment problem via continuous PCA and the exhaustive axis labeling and reflections. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. Our performance on the Princeton Shape Benchmark is on a par with the state-of-the-art methods. ©2009 IEEE.
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    Hotspotizer: end-user authoring of mid-air gestural interactions
    (Association for Computing Machinery, 2014) N/A; Department of Computer Engineering; Department of Media and Visual Arts; Baytaş, Mehmet Aydın; Yemez, Yücel; Özcan, Oğuzhan; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Media and Visual Arts; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Social Sciences and Humanities; College of Engineering; College of Social Sciences and Humanities; N/A; 107907; 12532
    Drawing from a user-centered design process and guidelines derived from the literature, we developed a paradigm based on space discretization for declaratively authoring mid-air gestures and implemented it in Hotspotizer, an end-to-end toolkit for mapping custom gestures to keyboard commands. Our implementation empowers diverse user populations - including end-users without domain expertise - to develop custom gestural interfaces within minutes, for use with arbitrary applications.
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    An audio-driven dancing avatar
    (Springer, 2008) Balci, Koray; Kizoglu, Idil; Akarun, Lale; Canton-Ferrer, Cristian; Tilmanne, Joelle; Bozkurt, Elif; Erdem, A. Tanju; Department of Computer Engineering; N/A; N/A; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Yemez, Yücel; Ofli, Ferda; Demir, Yasemin; Erzin, Engin; Tekalp, Ahmet Murat; Faculty Member; PhD Student; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 107907; N/A; N/A; 34503; 26207
    We present a framework for training and synthesis of an audio-driven dancing avatar. The avatar is trained for a given musical genre using the multicamera video recordings of a dance performance. The video is analyzed to capture the time-varying posture of the dancer's body whereas the musical audio signal is processed to extract the beat information. We consider two different marker-based schemes for the motion capture problem. The first scheme uses 3D joint positions to represent the body motion whereas the second uses joint angles. Body movements of the dancer are characterized by a set of recurring semantic motion patterns, i.e., dance figures. Each dance figure is modeled in a supervised manner with a set of HMM (Hidden Markov Model) structures and the associated beat frequency. In the synthesis phase, an audio signal of unknown musical type is first classified, within a time interval, into one of the genres that have been learnt in the analysis phase, based on mel frequency cepstral coefficients (MFCC). The motion parameters of the corresponding dance figures are then synthesized via the trained HMM structures in synchrony with the audio signal based on the estimated tempo information. Finally, the generated motion parameters, either the joint angles or the 3D joint positions of the body, are animated along with the musical audio using two different animation tools that we have developed. Experimental results demonstrate the effectiveness of the proposed framework.
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    Multimodal analysis of speech prosody and upper body gestures using hidden semi-Markov models
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Bozkurt, Elif; Asta, Shahriar; Özkul, Serkan; Yemez, Yücel; Erzin, Engin; PhD Student; PhD Student; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 107907; 34503
    Gesticulation is an essential component of face-to-face communication, and it contributes significantly to the natural and affective perception of human-to-human communication. In this work we investigate a new multimodal analysis framework to model relationships between intonational and gesture phrases using the hidden semi-Markov models (HSMMs). The HSMM framework effectively associates longer duration gesture phrases to shorter duration prosody clusters, while maintaining realistic gesture phrase duration statistics. We evaluate the multimodal analysis framework by generating speech prosody driven gesture animation, and employing both subjective and objective metrics.
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    Multicamera audio-visual analysis of dance figures
    (IEEE, 2007) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Ofli, Ferda; Erzin, Engin; Yemez, Yücel; Tekalp, Ahmet Murat; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 34503; 107907; 26207
    We present an automated system for multicamera motion capture and audio-visual analysis of dance figures. the multiview video of a dancing actor is acquired using 8 synchronized cameras. the motion capture technique is based on 3D tracking of the markers attached to the person's body in the scene, using stereo color information without need for an explicit 3D model. the resulting set of 3D points is then used to extract the body motion features as 3D displacement vectors whereas MFC coefficients serve as the audio features. in the first stage of multimodal analysis, we perform Hidden Markov Model (HMM) based unsupervised temporal segmentation of the audio and body motion features, separately, to determine the recurrent elementary audio and body motion patterns. then in the second stage, we investigate the correlation of body motion patterns with audio patterns, that can be used for estimation and synthesis of realistic audio-driven body animation.
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    Audio-facial laughter detection in naturalistic dyadic conversations
    (Ieee-Inst Electrical Electronics Engineers Inc, 2017) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Türker, Bekir Berker; Yemez, Yücel; Sezgin, Tevfik Metin; Erzin, Engin; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 107907; 18632; 34503
    We address the problem of continuous laughter detection over audio-facial input streams obtained from naturalistic dyadic conversations. We first present meticulous annotation of laughters, cross-talks and environmental noise in an audio-facial database with explicit 3D facial mocap data. Using this annotated database, we rigorously investigate the utility of facial information, head movement and audio features for laughter detection. We identify a set of discriminative features using mutual information-based criteria, and show how they can be used with classifiers based on support vector machines (SVMs) and time delay neural networks (TDNNs). Informed by the analysis of the individual modalities, we propose a multimodal fusion setup for laughter detection using different classifier-feature combinations. We also effectively incorporate bagging into our classification pipeline to address the class imbalance problem caused by the scarcity of positive laughter instances. Our results indicate that a combination of TDNNs and SVMs lead to superior detection performance, and bagging effectively addresses data imbalance. Our experiments show that our multimodal approach supported by bagging compares favorably to the state of the art in presence of detrimental factors such as cross-talk, environmental noise, and data imbalance.
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    Affect burst detection using multi-modal cues
    (IEEE, 2015) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Türker, Bekir Berker; Erzin, Engin; Marzban, Shabbir; Faculty Member; Faculty Member; PhD Student; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; 107907; N/A; 34503; N/A
    Recently, affect bursts have gained significant importance in the field of emotion recognition since they can serve as prior in recognising underlying affect bursts. In this paper we propose a data driven approach for detecting affect bursts using multimodal streams of input such as audio and facial landmark points. The proposed Gaussian Mixture Model based method learns each modality independently followed by combining the probabilistic outputs to form a decision. This gives us an edge over feature fusion based methods as it allows us to handle events when one of the modalities is too noisy or not available. We demonstrate robustness of the proposed approach on 'Interactive emotional dyadic motion capture database' (IEMOCAP) which contains realistic and natural dyadic conversations. This database is annotated by three annotators to segment and label affect bursts to be used for training and testing purposes. We also present performance comparison between SVM based methods and GMM based methods for the same configuration of experiments.
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    3D progressive compression with octree particles
    (Akademische Verlagsgesellsch Aka Gmbh, 2002) Schmitt, Francis; Department of Computer Engineering; N/A; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; N/A; 107907; N/A
    This paper improves the storage efficiency of the progressive particle-based modeling scheme presented in [14, 15] by using entropy coding techniques. This scheme encodes the surface geometry and attributes in terms of appropriately ordered oc-tree particles, which can then progressively be decoded and rendered by the-viewer by means of a fast direct triangulation technique. With the introduced entropy coding technique, the bitload of the multi-level representation for geometry encoding reduces to 9-14 bits per particle (or 4.5-7 bits per triangle) for 12-bit quantized geometry.
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    Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation
    (Academic Press Inc Elsevier Science, 2010) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907
    We present a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking and easy-to-compute smoothing operator that fairs the surface separately along its tangential and normal directions. The mesh restructuring operator, which is based on edge split, collapse and flip operations, enables the deformable mesh to adapt its shape to the object geometry without suffering from any geometrical distortions. By imposing appropriate minimum and maximum edge length constraints, the deformable mesh, hence the object surface, can be represented at increasing levels of detail. This coarse-to-fine strategy, that allows high resolution reconstructions even with deficient and irregularly sampled range data, not only provides robustness, but also significantly improves the computational efficiency of the deformation process. We demonstrate the performance of the proposed method on several real objects.
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    Coarse-to-fine combinatorial matching for dense isometric shape correspondence
    (Wiley, 2011) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907
    We present a dense correspondence method for isometric shapes, which is accurate yet computationally efficient. We minimize the isometric distortion directly in the 3D Euclidean space, i.e., in the domain where isometry is originally defined, by using a coarse-to-fine sampling and combinatorial matching algorithm. Our method does not require any initialization and aims to find an accurate solution in the minimum-distortion sense for perfectly isometric shapes. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes.