Choi C., Aizawa K., Harashima H. & Takede T. (1994) Analysis and synthesis of facial image sequences in modelbased image coding, IEEE Transactions on Circuits and Systems for Video Technology, 4(3):257-275.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the lossless constraint can be relaxed, assuming that the information to be preserved is the visual appearance. In this way, coding efficiency is improved by reducing the information to transmit. Some examples can be found in the field of video compression for multimedia (like video telephony [16], 5] and surveillance [17] medical imaging [18] as well as in emerging applications like stereoscopic imaging, used to obtain a three dimensional perception of a scene [19] Medical images usually consists of a region representing the part of the body under investigation (i.e. the heart in a ....

C.S. Choi and T. Takebe, "Analysis and synthesis of facial image sequences in model-based image coding," IEEE Trans. on Video Tech., vol. 4, pp. 257-275, June 1994.

....database using a description such as give me a video clip that contains the faces of Arnold Schwarzenegger and Vanessa Williams. In this example, the faces is one of the contents in the retrieved video data. Finally, face recognition technique may make model based video coding be more practical [17, 18]. In model based video coding, the contents of the video sequences will be modeled and the changes of the scene can be described very efficiently using the models. This kind of coding technique is useful for visual communication over very low bit rate channels. a) a video clip captured by CCD ....

Chang Seok Choi, Kiyoharu Aizawa, Hiroshi Harashima, and Tsuyoshi Takebe, "Analysis and Synthesis of Facial Image Sequences in Model-based Image Coding," IEEE Transactions Circuits and Systems for Video Technology, Vol. 4, No. 3, June 1994.

....the lossless constraint can be relaxed, assuming that the information to be preserved is the visual appearance. In this way, coding efficiency is improved by reducing the information to transmit. Some examples can be found in the field of video compression for multimedia (like video telephony [5] [16] and surveillance [17] medical imaging [18] as well as in emerging applications like stereoscopic imaging, used to obtain a 3 D perception of a scene [19] Medical images usually consists of a region representing the part of the body under investigation (i.e. the heart in a CT or MRI chest ....

C. S. Choi and T. Takebe, "Analysis and synthesis of facial image sequences in model-based image coding," IEEE Trans. Video Technol., vol. 4, pp. 257--275, June 1994.

....face to face communication is one of the most important applications of image coding and various facial images generally posse quite similar features, model based facial image coding is currently the most popular trend. Model based facial image coding is composed of the following procedures:[2] 1) Designing a general model of human faces; 2) Matching the general model to a specific image; 3) Storing or transmitting parameters of the model and some texture information if necessary; 4) Reconstructing the image according to model parameters and Texture. The general model can be accessed ....

C. S. Choi, K. Aizawa, H. Harashima and T. Takebe, #Analysis and Synthesis of Facial Image Sequences in Model-Based Image Coding,# IEEE Trans. Circuits and Systems for Video Technology, vol. 4, no. 3, pp.257275, June, 1994.

....as global and local, corresponding to head movements and various facial expressions, respectively. These translations can be represented by fractal transformations [129] of previously coded so called range blocks. Analysis by synthesis coding, which was investigated, e.g. by Choi et al. [124], typically attempts to synthesize the image to be encoded a number of times in order to arrive at the subjectively most attractive quality versus bit rate tradeoff. This inevitably increases the complexity and raises the question of finding an appropriate objective quality measure, which ....

C. S. Choi et al., "Analysis and synthesis of facial image sequences in model-based image coding," IEEE Trans. Circuits Syst. Video Technol., vol. 4, pp. 257--275, June 1994.

.... active shape models (ASM) Baumb96] Edwar98] or energy minimising point tracking techniques [Lucas81] Lien00] The reconstruction of tracked facial feature movements by virtual actors has application in video telecommunication because of the potential for low bandwidth communication [Choi91] [Choi94]. This kind of technology has also been used to lipsynch computer graphic animations with an actor s voice and movements for film entertainment or virtual avatars [Berge85] Willi90] Bregl97] Essa96] Guent98] Ezzat00] The animated characters are either a direct clone of the original (in the ....

Choi C.S., Aizawa K., Harashima H. and Takebe T.: Analysis and synthesis of facial image sequences in model based image coding, IEEE Trans. on Circuits and systems for video technology, Vol. 4, No. 3, pp257275, 1994.

....= x p (u) L p (u) q (2.14) The model based optical flow constraint equation in the image can be found by rewriting (2.13) using (2.14) IL p (u) q I t = 0 (2.15) Formulations which are basically identical to (2. 15) although are often confined to rigid motion) can be found in [Adi85, BAHH92, CAHT94, HW88, LRF93, NH87, NS85] Negahdaripour and Horn [NH87] refers to a formulation such as this as a direct method for motion estimation. The discussion of (2.15) in [BAHH92, NH87, NS85] is specialized for rigid motion, and while still general, requires a lengthy derivation by hand. Using the ....

....The discussion of (2.15) in [BAHH92, NH87, NS85] is specialized for rigid motion, and while still general, requires a lengthy derivation by hand. Using the modular shape formulation described in Section 2.1 allows for more simple derivations of (2. 15) and is more similar to the description in [CAHT94, LRF93] Another difference between these techniques is noted by their use of either Euler angles or quaternions as the representation for the rotations) There are a number of techniques available for solving (2.15) The most common is the iterative minimization of the quadratic error measure, ....

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C. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Circuits and Systems for Video Technology, 4(3):257--275, 1994.

....Essa and Pentland [25] built a physically based face model and developed a control theoretic technique to fit it to a sequence of images. Decarlo and Metaxas [18] employed a similar model that incorporates possible variations in head shape using anthropometric measurements. Comparable techniques [14,51] are used in model based image coding schemes where the model parameters provide a compact representation of the video frames. These approaches use a 3D articulated face model to derive a model for face 13 motion. Using the estimated motion from an input video, the 3D model can be animated to ....

C.S. Choi, K. Aizawa, H. Harshima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(3):257--274, June 1994.

....attempts to model and animate realistic human faces date back to the early 70 s [55] with many dozens of research papers published since. The applications of face animation include such diverse fields as character animation for films and advertising, computer games [37] video teleconferencing [13], user interface agents and avatars [73] and facial surgery planning [42, 75] So far, no perfectly realistic face animation has ever been generated by computer: no face animation Turing test has ever been passed. There are several factors that make realistic face animation so elusive. First, ....

C. S. Choi, Kiyoharu, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. In IEEE Transactions on Circuits and Systems for Video Technology,volume 4, pages 257 -- 275, June 1994.

....Essa and Pentland [11] built a physically based face model and developed a control theoretic technique to fit it to a sequence of images. Decarlo and Metaxas [8] employed a similar model that incorporates possible variations in head shape using anthropometric measurements. Comparable techniques [1, 6, 17] are used in model based image coding schemes where the model parameters provide a compact representation of video frames. These approaches use a 3D articulated face model to derive a model for facial motion. Using the estimated motion, the 3D model can be animated to synthesize an animation ....

C.S. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(3):257--274, June 1994.

....human face, which can be represented by a face model, e.g. the mask Candide[4] appears in the scene. Besides automatic adaptation of the face model[3] tracking is the main problem. After adaptation, the face model is tracked together with the person s moving head by global motion compensation [3][5][6] The motion of the head, i.e. rotation and translation, is estimated and compensated. Because the motion estimation is inaccurate due to noise, facial expression and inaccurate 3D model shape, the projections of eye and mouth positions of the face model may not match those of the person in ....

C.S. Choi, K.Aizawa, H.Harashima, T.Takebe, "Analysis and synthesis of facial image sequences in model--based image coding", IEEE Trans. on Circuits and Systems for Video Technology, Vol.4, No. 3, pp.257--275, 1994

....be quite difficult, however, especially as the deviation between the model and data becomes large. Model based optical flow: Instead of computing an unconstrained flow field (a grid of arrows) a model based approach explains the optical flow information in terms of motion parameters of the model [1, 5, 9, 23, 28, 35, 36]. While the problem is non linear, these frameworks can use either a single step linear least squares solution [9, 28, 36] or an iterative least squares solution [1, 5, 23, 35] The motion model can be a 2D model of image motion [5, 6] or a 3D model (rigid or non rigid) of object motion [5, 9, ....

.... computing an unconstrained flow field (a grid of arrows) a model based approach explains the optical flow information in terms of motion parameters of the model [1, 5, 9, 23, 28, 35, 36] While the problem is non linear, these frameworks can use either a single step linear least squares solution [9, 28, 36], or an iterative least squares solution [1, 5, 23, 35] The motion model can be a 2D model of image motion [5, 6] or a 3D model (rigid or non rigid) of object motion [5, 9, 28] along with a camera model to relate to the images) It is also possible to compute an unconstrained optical flow field ....

[Article contains additional citation context not shown here]

C. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Circuits and Systems for Video Technology, 4(3):257--275, 1994.

.... instead of blocks, where the regions are formed on the basis of quantised grey level values [9] On the other side of the range there are knowledge based model methods 2 which recognise highly specific objects, e.g. a head and shoulder model, that can be represented almost totally by parameters [6][13] These highly specific coding methods are likely to get the best performance, but are only useful in highly restricted applications. In methods for general video compression a balance has to be found between the generality and the performance of the method. A very basic aspect of the ....

C. S. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Trans. Circuits and Systems for Video Technology, 4(3):257--275, June 1994.

....full set of hand drawn images used by our system. To drive 3D models, geometric model parameters must be recovered. In particular, facial features are tracked in the incoming images and then used to drive the movements of the 3D models, which are rendered with well established graphics techniques [2, 12, 51]. Although facial features can be tracked by motion capture techniques to produce performance driven animation systems [59, 42] systems requiring special markers or devices are unlikely to be adopted by the casual user. Using vision based techniques, facial features can be tracked ....

Chang S. Choi, Kiyoharu, Hiroshi Harashima, and Tsuyoshi Takebe. Analysis and synthesis of facial image sequences in model-based image coding. In IEEE Transactions on Circuits and Systems for Video Technology, volume 4, pages 257--275, June 1994.

....attempts to model and animate realistic human faces date back to the early 70 s [34] with many dozens of research papers published since. The applications of facial animation include such diverse fields as character animation for films and advertising, computer games [19] video teleconferencing [7], user interface agents and avatars [44] and facial surgery planning [23, 45] Yet no perfectly realistic facial animation has ever been generated by computer: no facial animation Turing test has ever been passed. There are several factors that make realistic facial animation so elusive. ....

Chang S. Choi, Kiyoharu, Hiroshi Harashima, and Tsuyoshi Takebe. Analysis and Synthesis of Facial Image Sequences in Model-Based Image Coding. In IEEE Transactions on Circuits and Systems for Video Technology, volume 4, pages 257 -- 275. June 1994.

.... typical videophone applications, usually a human face appears in the scene, which can be represented by a face model, e.g. the mask Candide [4] Besides automatic adaptation of the face model at the beginning of the image sequence [3] 5] tracking a face during the sequence is the main problem [3][6][7] 8] For tracking a face, Kampmann and Ostermann [3] C.S. Choi, K. Aizawa, H. Harashima, T. Takebe [6] and H. Li, R. Forchheimer [7] reported methods based on global motion compensation of a model head. In those algorithms, the 3D motion of the head, i.e. rotation and translation, is estimated ....

....face model, e.g. the mask Candide [4] Besides automatic adaptation of the face model at the beginning of the image sequence [3] 5] tracking a face during the sequence is the main problem [3] 6] 7] 8] For tracking a face, Kampmann and Ostermann [3] C.S. Choi, K. Aizawa, H. Harashima, T. Takebe [6] and H. Li, R. Forchheimer [7] reported methods based on global motion compensation of a model head. In those algorithms, the 3D motion of the head, i.e. rotation and translation, is estimated and compensated. Because the motion estimation is inaccurate due to noise and inaccurate 3D model head ....

C.S. Choi, K. Aizawa, H. Harashima, T.Takebe, "Analysis and synthesis of facial image sequences in model--based image coding", IEEE Trans. on Circuits and Systems for Video Technology, Vol.4, No. 3, June 1994, pp.257--275.

....in real time, subtle failures in facial area segmentation and various noise sources such as variation in lighting condition cause crucial problems. For facial image synthesis applications, many approaches attempt to extract local spatial patterns such as action units (AU) and their combinations [13][14] Real facial motion, however, is never completely localized. Detecting a unique set of action units for a specific facial expression is not guaranteed [14] One promising approach for recognizing up to facial expression intensities is to consider the whole facial image as a single pat C o ....

C. S. Choi, K. Aizawa, H. Harashima, and T. Takebe, "Analysis and synthesis of facial image sequences in model-based image coding," IEEE Trans. on Circuits and Systems for Video Technology, vol. 4, pp. 257--275, 1994.

No context found.

Choi C., Aizawa K., Harashima H. & Takede T. (1994) Analysis and synthesis of facial image sequences in modelbased image coding, IEEE Transactions on Circuits and Systems for Video Technology, 4(3):257-275.

No context found.

C. S. Choi, Kiyoharu, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. In IEEE Transactions on Circuits and Systems for Video Technology, volume 4, pages 257--275, June 1994.

No context found.

C. S. Choi, K. Aizawa, H. Harashima, and T. Takebe, "Analysis and synthesis of facial image sequences in model-based image coding," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 4, No. 3, 1994, pp. 257-275.

No context found.

C. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Circuits and Systems for Video Technology, 4(3):257--275, 1994.

No context found.

C. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(3):257--275, June 1994.

No context found.

C. Choi, K. Aizawa, H. Harashima, and T. Takebe. Analysis and synthesis of facial image sequences in model-based image coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(3):257--275, June 1994.

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C. S. Choi, K. Aizawa, H. Harashima, and T. Takeb, "Analysis and synthesis of facial image sequences in model-based image coding," Special Issue of the IEEE Trans. Circuits and Systems for Video Tech., vol. 4, no. 3, pp. 257--275, June 1994.

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Choi, C.S, K. Aizawa, H. Harashima, and T. Takebe (1994), Analysis and Synthesis of Facial Image Sequences in Model-Based Image Coding, IEEE Trans. On Circuits and Systems for Video Technology 4(3), 257-275.

No context found.

C. S. Choi, K. Aizawa, H. Harashima, T. Takebe, "Analysis and synthesis of facial image sequences in model-based image coding," in IEEE Transactions on Circuits and Systems for Video Technology, Vol. 4, No. 3, June 1994, pp. 257-275.

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