Process models depict crucial artifacts for organizations regarding documentation, communication,... more Process models depict crucial artifacts for organizations regarding documentation, communication, and collaboration. The proper comprehension of such models is essential for an effective application. An important aspect in process model literacy constitutes the question how the information presented in process models is extracted and processed by the human visual system? For such visuospatial tasks, the visual system deploys a set of elemental operations, from whose compositions different visual routines are produced. This paper provides insights from an exploratory eye tracking study, in which visual routines during process model comprehension were contemplated. More specifically, n = 29 participants were asked to comprehend n = 18 process models expressed in the Business Process Model and Notation 2.0 reflecting diverse mappings (i.e., straight, upward, downward) and complexity levels. The performance measures indicated that even less complex process models pose a challenge regard...
The detection and categorization of animate motions is a crucial task underlying social interacti... more The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision-making. Neural representations of perceived animate objects are built in the primate cortical region STS which is a region of convergent input from intermediate level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to which extent form and motion information contribute to the generation of such representations and what kind of mechanisms are involved in the learning processes. The paper develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning throug...
Evidence suggests that the brain uses an operational set of canonical com-putations like normaliz... more Evidence suggests that the brain uses an operational set of canonical com-putations like normalization, input filtering, and response gain enhance-ment via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit com-bining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity.We present an an-alytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant mod-ulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activa-tion is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to imple-ment center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a ...
Motion transparency occurs when multiple coherent motions are per-ceived in one spatial location.... more Motion transparency occurs when multiple coherent motions are per-ceived in one spatial location. Imagine, for instance, looking out of the window of a bus on a bright day, where the world outside the window is passing by and movements of passengers inside the bus are reflected in the window. The overlay of both motions at the window leads to motion transparency, which is challenging to process. Noisy and ambiguous motion signals can be reduced using a compe-tition mechanism for all encoded motions in one spatial location. Such a competition, however, leads to the suppression of multiple peak re-sponses that encode different motions, as only the strongest response tends to survive. As a solution, we suggest a local center-surround com-petition for population-encoded motion directions and speeds. Similar motions are supported, and dissimilar ones are separated, by represent-ing them as multiple activations, which occurs in the case of motion transparency. Psychophysical findings, suc...
Abstract—In this work a robust method is introduced, which addresses the problem of automatic ext... more Abstract—In this work a robust method is introduced, which addresses the problem of automatic extraction and tracking of facial features in color image sequences. The automatic extraction of facial features is done in two steps. The first step consists of a rough localization of the features while in the second step the facial features are exactly segmented using Active Shape Models (ASM). In contrast to simple ASM another approach is pursued in this work, which contains two modifications of the ASM, which lead to more robustness. The facial feature tracking in video sequences is realized by correspondence determination of individual specific support points which are used to anchor the feature models during movement. This leads to more stability and reliability for tracking and form description of the facial features under image-specific disturbances. Index Terms—Face analysis, Facial features extraction, track-ing, Color image processing, Application I.
2010 International Conference of Soft Computing and Pattern Recognition, 2010
ABSTRACT This work proposes a new approach for facial expression recognition in color image seque... more ABSTRACT This work proposes a new approach for facial expression recognition in color image sequences, based on integrated evaluation of geometric and dynamic features. For this purpose a series of methods is introduced that on the one hand achieve high recognition rates for expressive facial behavior and on the other hand address a couple of common problems in this area of research. In particular we apply physiologically motivated image regions for the detection of dynamic features by using an optical flow method. In this way dynamic features capture the variations caused by facial expression changes. Opposed, geometric features do not contain temporal information but describe spatial feature parameters. These correspond to 3-D based Euclidean distances and angles. Particularly, the hypothesis of this work is that through integrated evaluation of geometric and dynamic features, improved recognition rates can be achieved. Based on comprehensive experimental investigations we show the advantage of the suggested approach.
ABSTRACT This paper presents an overview of the COVIRA project, AIM Project No. 2003. The COVIRA ... more ABSTRACT This paper presents an overview of the COVIRA project, AIM Project No. 2003. The COVIRA consortium has been performing research in the area of Multimodality Image Analysis, with a focus on. Registration and Segmentation. Together with results in the areas of Visualization, User Interface, Digital Anatomy Atlas, Conformal 3D Radiation Therapy Planning, and Cerebral Vessel Tree Reconstruction, COVIRA software has been clinically validated at six clinical sites in five European countries.
Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientat... more Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambi-guity is resolved for localized image features, such as corners or nonoc-clusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which lo-calized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to l...
doi: 10.3389/fpsyg.2014.01287 Adaptive learning in a compartmental model of visual cortex—how fee... more doi: 10.3389/fpsyg.2014.01287 Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement
Abstract—Network inference algorithms can assist life scientists in unraveling gene-regulatory sy... more Abstract—Network inference algorithms can assist life scientists in unraveling gene-regulatory systems on a molecular level. In recent years, great attention has been drawn to the reconstruction of Boolean networks from time series. These need to be binarized, as such networks model genes as binary variables (either “expressed ” or “not expressed”). Common binarization methods often cluster measurements or separate them according to statistical or information theoretic characteristics and may require many data points to determine a robust threshold. Yet, time series measurements frequently comprise only a small number of samples. To overcome this limitation, we propose a binarization that incorporates measurements at multiple resolutions. We introduce two such binarization approaches which determine thresholds based on limited numbers of samples and additionally provide a measure of threshold validity. Thus, network reconstruction and further analysis can be restricted to genes with...
is a sample cover image for this issue. The actual cover is not yet available at this time.) This... more is a sample cover image for this issue. The actual cover is not yet available at this time.) This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
Visual navigation requires the estimation of self-motion as well as the segmentation of objects f... more Visual navigation requires the estimation of self-motion as well as the segmentation of objects from the background. We suggest a definition of local velocity gradients to compute types of self-motion, segment objects, and compute local properties of optical flow fields, such as divergence, curl, and shear. Such velocity gradients are computed as velocity differ-ences measured locally tangent and normal to the direction of flow. Then these differences are rotated according to the local direction of flow to achieve independence of that direction.We propose a bio-inspiredmodel for the computation of these velocity gradients for video sequences. Sim-ulation results show that local gradients encode ordinal surface depth, assuming self-motion in a rigid scene or object motions in a nonrigid scene. For translational self-motion velocity, gradients can be used to distinguish between static and moving objects. The information about ordinal surface depth and self-motion can help steering con...
For elderly people healthcare in ambient living environments, recognizing confusion states in an ... more For elderly people healthcare in ambient living environments, recognizing confusion states in an automatic and non-contact manner is essential. In this work we provide a visual approach to confusion recognition consisting of behavior monitoring and movement pattern analysis. To collect data for evaluation, we created a dataset from a search experiment. After extracting and analyzing the movement patterns, we achieved a recognition rate of \(89.6\%\) when cross-validating over different subjects and \(88.9\%\) when testing on a new set of samples. To our knowledge, we are the first to investigate confusion recognition using visual information. Our work shows that the mental confusion can be effectively recognized based on the movement pattern.
Process models depict crucial artifacts for organizations regarding documentation, communication,... more Process models depict crucial artifacts for organizations regarding documentation, communication, and collaboration. The proper comprehension of such models is essential for an effective application. An important aspect in process model literacy constitutes the question how the information presented in process models is extracted and processed by the human visual system? For such visuospatial tasks, the visual system deploys a set of elemental operations, from whose compositions different visual routines are produced. This paper provides insights from an exploratory eye tracking study, in which visual routines during process model comprehension were contemplated. More specifically, n = 29 participants were asked to comprehend n = 18 process models expressed in the Business Process Model and Notation 2.0 reflecting diverse mappings (i.e., straight, upward, downward) and complexity levels. The performance measures indicated that even less complex process models pose a challenge regard...
The detection and categorization of animate motions is a crucial task underlying social interacti... more The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision-making. Neural representations of perceived animate objects are built in the primate cortical region STS which is a region of convergent input from intermediate level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to which extent form and motion information contribute to the generation of such representations and what kind of mechanisms are involved in the learning processes. The paper develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning throug...
Evidence suggests that the brain uses an operational set of canonical com-putations like normaliz... more Evidence suggests that the brain uses an operational set of canonical com-putations like normalization, input filtering, and response gain enhance-ment via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit com-bining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity.We present an an-alytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant mod-ulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activa-tion is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to imple-ment center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a ...
Motion transparency occurs when multiple coherent motions are per-ceived in one spatial location.... more Motion transparency occurs when multiple coherent motions are per-ceived in one spatial location. Imagine, for instance, looking out of the window of a bus on a bright day, where the world outside the window is passing by and movements of passengers inside the bus are reflected in the window. The overlay of both motions at the window leads to motion transparency, which is challenging to process. Noisy and ambiguous motion signals can be reduced using a compe-tition mechanism for all encoded motions in one spatial location. Such a competition, however, leads to the suppression of multiple peak re-sponses that encode different motions, as only the strongest response tends to survive. As a solution, we suggest a local center-surround com-petition for population-encoded motion directions and speeds. Similar motions are supported, and dissimilar ones are separated, by represent-ing them as multiple activations, which occurs in the case of motion transparency. Psychophysical findings, suc...
Abstract—In this work a robust method is introduced, which addresses the problem of automatic ext... more Abstract—In this work a robust method is introduced, which addresses the problem of automatic extraction and tracking of facial features in color image sequences. The automatic extraction of facial features is done in two steps. The first step consists of a rough localization of the features while in the second step the facial features are exactly segmented using Active Shape Models (ASM). In contrast to simple ASM another approach is pursued in this work, which contains two modifications of the ASM, which lead to more robustness. The facial feature tracking in video sequences is realized by correspondence determination of individual specific support points which are used to anchor the feature models during movement. This leads to more stability and reliability for tracking and form description of the facial features under image-specific disturbances. Index Terms—Face analysis, Facial features extraction, track-ing, Color image processing, Application I.
2010 International Conference of Soft Computing and Pattern Recognition, 2010
ABSTRACT This work proposes a new approach for facial expression recognition in color image seque... more ABSTRACT This work proposes a new approach for facial expression recognition in color image sequences, based on integrated evaluation of geometric and dynamic features. For this purpose a series of methods is introduced that on the one hand achieve high recognition rates for expressive facial behavior and on the other hand address a couple of common problems in this area of research. In particular we apply physiologically motivated image regions for the detection of dynamic features by using an optical flow method. In this way dynamic features capture the variations caused by facial expression changes. Opposed, geometric features do not contain temporal information but describe spatial feature parameters. These correspond to 3-D based Euclidean distances and angles. Particularly, the hypothesis of this work is that through integrated evaluation of geometric and dynamic features, improved recognition rates can be achieved. Based on comprehensive experimental investigations we show the advantage of the suggested approach.
ABSTRACT This paper presents an overview of the COVIRA project, AIM Project No. 2003. The COVIRA ... more ABSTRACT This paper presents an overview of the COVIRA project, AIM Project No. 2003. The COVIRA consortium has been performing research in the area of Multimodality Image Analysis, with a focus on. Registration and Segmentation. Together with results in the areas of Visualization, User Interface, Digital Anatomy Atlas, Conformal 3D Radiation Therapy Planning, and Cerebral Vessel Tree Reconstruction, COVIRA software has been clinically validated at six clinical sites in five European countries.
Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientat... more Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambi-guity is resolved for localized image features, such as corners or nonoc-clusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which lo-calized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to l...
doi: 10.3389/fpsyg.2014.01287 Adaptive learning in a compartmental model of visual cortex—how fee... more doi: 10.3389/fpsyg.2014.01287 Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement
Abstract—Network inference algorithms can assist life scientists in unraveling gene-regulatory sy... more Abstract—Network inference algorithms can assist life scientists in unraveling gene-regulatory systems on a molecular level. In recent years, great attention has been drawn to the reconstruction of Boolean networks from time series. These need to be binarized, as such networks model genes as binary variables (either “expressed ” or “not expressed”). Common binarization methods often cluster measurements or separate them according to statistical or information theoretic characteristics and may require many data points to determine a robust threshold. Yet, time series measurements frequently comprise only a small number of samples. To overcome this limitation, we propose a binarization that incorporates measurements at multiple resolutions. We introduce two such binarization approaches which determine thresholds based on limited numbers of samples and additionally provide a measure of threshold validity. Thus, network reconstruction and further analysis can be restricted to genes with...
is a sample cover image for this issue. The actual cover is not yet available at this time.) This... more is a sample cover image for this issue. The actual cover is not yet available at this time.) This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
Visual navigation requires the estimation of self-motion as well as the segmentation of objects f... more Visual navigation requires the estimation of self-motion as well as the segmentation of objects from the background. We suggest a definition of local velocity gradients to compute types of self-motion, segment objects, and compute local properties of optical flow fields, such as divergence, curl, and shear. Such velocity gradients are computed as velocity differ-ences measured locally tangent and normal to the direction of flow. Then these differences are rotated according to the local direction of flow to achieve independence of that direction.We propose a bio-inspiredmodel for the computation of these velocity gradients for video sequences. Sim-ulation results show that local gradients encode ordinal surface depth, assuming self-motion in a rigid scene or object motions in a nonrigid scene. For translational self-motion velocity, gradients can be used to distinguish between static and moving objects. The information about ordinal surface depth and self-motion can help steering con...
For elderly people healthcare in ambient living environments, recognizing confusion states in an ... more For elderly people healthcare in ambient living environments, recognizing confusion states in an automatic and non-contact manner is essential. In this work we provide a visual approach to confusion recognition consisting of behavior monitoring and movement pattern analysis. To collect data for evaluation, we created a dataset from a search experiment. After extracting and analyzing the movement patterns, we achieved a recognition rate of \(89.6\%\) when cross-validating over different subjects and \(88.9\%\) when testing on a new set of samples. To our knowledge, we are the first to investigate confusion recognition using visual information. Our work shows that the mental confusion can be effectively recognized based on the movement pattern.
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