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S. Tachi - One of the best experts on this subject based on the ideXlab platform.
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Space Perception Model with generates horopter
[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991Co-Authors: T. Maeda, S. TachiAbstract:Neural network Models of space Perception using binocular vision are presented to find out how the convergence angle and the bipolar latitude are mapped onto the depth sensation. It is considered that the horopter and parallel distance alleys, which have been thought to be phenomena in psychology, are actually structural characteristics of signal processing in the human neural network, resulting from the learned transformation of binocular eye movement information into a subjective orthogonal coordinate system. Psychological and physiological knowledge offers several space Perception Models with different signal processing structures. In learning simulation experiments, the Models generated horopter, parallel alley, and distance alley results which were similar to those of a human being. On the basis of these results, it is concluded that the signal processing system in human space Perception uses the essential signal space for each Perception as the signal space for interaction between signals. >
Torsten Dau - One of the best experts on this subject based on the ideXlab platform.
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A speech-based computational auditory signal processing and Perception Model.
The Journal of the Acoustical Society of America, 2019Co-Authors: Helia Relaño-iborra, Johannes Zaar, Torsten DauAbstract:A new speech intelligibility prediction Model is presented which is based on the Computational Auditory Signal Processing and Perception Model (CASP) of Jepsen, Ewert, and Dau [(2008). J. Acoust. Soc. Am. 124(1), 422–438]. The Model combines a non-linear auditory-inspired preprocessing with a backend based on the cross-correlation between the clean and the degraded speech representations in the modulation envelope domain. Several speech degradation and speech enhancement algorithms were considered to study the ability of the Model to predict data from normal-hearing listeners. Degradations of speech intelligibility due to additive noise, phase-jitter distortion, and single-channel noise reduction as well as improved speech intelligibility due to ideal binary mask processing are shown to be successfully accounted for by the Model. Furthermore, the Model reflects stimulus-level dependent effects of auditory Perception, including audibility limitations at low levels and degraded speech intelligibility at high levels. Given its realistic non-linear auditory processing frontend, the speech-based computational auditory signal processing and Perception Model may provide a valuable computational framework for studying the effects of sensorineural hearing impairment on speech intelligibility.A new speech intelligibility prediction Model is presented which is based on the Computational Auditory Signal Processing and Perception Model (CASP) of Jepsen, Ewert, and Dau [(2008). J. Acoust. Soc. Am. 124(1), 422–438]. The Model combines a non-linear auditory-inspired preprocessing with a backend based on the cross-correlation between the clean and the degraded speech representations in the modulation envelope domain. Several speech degradation and speech enhancement algorithms were considered to study the ability of the Model to predict data from normal-hearing listeners. Degradations of speech intelligibility due to additive noise, phase-jitter distortion, and single-channel noise reduction as well as improved speech intelligibility due to ideal binary mask processing are shown to be successfully accounted for by the Model. Furthermore, the Model reflects stimulus-level dependent effects of auditory Perception, including audibility limitations at low levels and degraded speech intelligibility at hig...
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A speech-based computational auditory signal processing and Perception Model.
The Journal of the Acoustical Society of America, 2019Co-Authors: Helia Relaño-iborra, Johannes Zaar, Torsten DauAbstract:A new speech intelligibility prediction Model is presented which is based on the Computational Auditory Signal Processing and Perception Model (CASP) of Jepsen, Ewert, and Dau [(2008). J. Acoust. Soc. Am. 124(1), 422-438]. The Model combines a non-linear auditory-inspired preprocessing with a backend based on the cross-correlation between the clean and the degraded speech representations in the modulation envelope domain. Several speech degradation and speech enhancement algorithms were considered to study the ability of the Model to predict data from normal-hearing listeners. Degradations of speech intelligibility due to additive noise, phase-jitter distortion, and single-channel noise reduction as well as improved speech intelligibility due to ideal binary mask processing are shown to be successfully accounted for by the Model. Furthermore, the Model reflects stimulus-level dependent effects of auditory Perception, including audibility limitations at low levels and degraded speech intelligibility at high levels. Given its realistic non-linear auditory processing frontend, the speech-based computational auditory signal processing and Perception Model may provide a valuable computational framework for studying the effects of sensorineural hearing impairment on speech intelligibility.
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Predicting consonant recognition and confusions using a microscopic speech Perception Model
The Journal of the Acoustical Society of America, 2017Co-Authors: Johannes Zaar, Torsten DauAbstract:The Perception of consonants has been investigated in various studies and shown to critically depend on fine details in the stimuli. The present study proposes a microscopic speech Perception Model that combines an auditory processing front end with a correlation-based template-matching back end to predict consonant recognition and confusions. The Model represents an extension of the auditory signal processing Model by Dau et al. [(1997), J. Acoust. Soc. Am. 102, 2892-2905] toward predicting microscopic speech Perception data. Model predictions were computed for the extensive consonant Perception data set provided by Zaar and Dau [(2015), J. Acoust. Soc. Am. 138, 1253-1267], obtained with consonant-vowels (CVs) in white noise. The predictions were in good agreement with the perceptual data both in terms of consonant recognition and confusions. The Model was further evaluated with respect to perceptual artifacts induced by (i) different hearing-aid signal processing strategies and (ii) simulated cochlear-i...
Kwanghoon Sohn - One of the best experts on this subject based on the ideXlab platform.
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Stereoscopic image quality metric based on binocular Perception Model
2012 19th IEEE International Conference on Image Processing, 2012Co-Authors: Kwanghoon SohnAbstract:Measuring a perceptual quality of an image is one of the important tasks in various applications such as image coding, processing, enhancement, and monitoring system. Although active researches have been made for objective quality assessment of 2D images for some decades, still very few efforts have been concentrated on 3D image quality assessment. In this paper, we propose a new quality metric for stereoscopic images based on the binocular Perception Model considering asymmetric property of a stereoscopic image pair. Experiments for publicly available databases show that the proposed metric provides consistent correlations with subjective quality scores. The results also show that the proposed metric outperforms state-of-the-arts metrics.
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ICIP - Stereoscopic image quality metric based on binocular Perception Model
2012 19th IEEE International Conference on Image Processing, 2012Co-Authors: Seungchul Ryu, Dong-hyun Kim, Kwanghoon SohnAbstract:Measuring a perceptual quality of an image is one of the important tasks in various applications such as image coding, processing, enhancement, and monitoring system. Although active researches have been made for objective quality assessment of 2D images for some decades, still very few efforts have been concentrated on 3D image quality assessment. In this paper, we propose a new quality metric for stereoscopic images based on the binocular Perception Model considering asymmetric property of a stereoscopic image pair. Experiments for publicly available databases show that the proposed metric provides consistent correlations with subjective quality scores. The results also show that the proposed metric outperforms state-of-the-arts metrics.
T. Maeda - One of the best experts on this subject based on the ideXlab platform.
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Space Perception Model with generates horopter
[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991Co-Authors: T. Maeda, S. TachiAbstract:Neural network Models of space Perception using binocular vision are presented to find out how the convergence angle and the bipolar latitude are mapped onto the depth sensation. It is considered that the horopter and parallel distance alleys, which have been thought to be phenomena in psychology, are actually structural characteristics of signal processing in the human neural network, resulting from the learned transformation of binocular eye movement information into a subjective orthogonal coordinate system. Psychological and physiological knowledge offers several space Perception Models with different signal processing structures. In learning simulation experiments, the Models generated horopter, parallel alley, and distance alley results which were similar to those of a human being. On the basis of these results, it is concluded that the signal processing system in human space Perception uses the essential signal space for each Perception as the signal space for interaction between signals. >
Özer Ciftcioglu - One of the best experts on this subject based on the ideXlab platform.
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VISUAL SPACE Perception Model IDENTIFICATION BY EVOLUTIONARY SEARCH
2006Co-Authors: Michael S. Bittermann, S. Sariyildiz, Özer CiftciogluAbstract:Visual Perception of Spaces is relevant for design. Designs, which satisfy perceptual requirements are found based on assessments of perceptual implications. For this purpose a probabilistic Model of human visual space Perception is used. Focus of this paper is the identification of optimal Model parameters, so that the Perception Model matches the Perception of human experimenters. This is accomplished by genetic algorithm, which is an evolutionary optimization method from the domain of computational intelligence, which is able to deal with the probabilistic and discrete nature of the Perception Model to be identified.
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APGV - Application of a visual Perception Model in virtual reality
Proceedings of the 3rd symposium on Applied perception in graphics and visualization - APGV '06, 2006Co-Authors: Özer Ciftcioglu, Michael S. Bittermann, I. Sevil SariyildizAbstract:Visual Perception is an important source of information for a human. It is directly related to vision although this relation is commonly not quantified but qualitatively well described. This research aims to establish a human visual Perception Model to analyse the Perception process and thereby quantify its properties. Recognizing the relation between vision and Perception, which are deterministic and probabilistic in nature, respectively, a probabilistic theory for Perception is developed. From the theoretical results, the Perception is defined in mathematical terms and based on this a Perception Model is devised in virtual reality for the verification of the theory.