Growth Signatures of Rosette Plants from Time-Lapse Video.

Plant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model plant. In each frame of the video sequences, potential leaves are detected using a leaf-shape model. These detections are prone to errors due to the complex shape of plants and their changing appearance in the image, depending on leaf movement, leaf growth, and illumination conditions. To cope with this problem, we employ a novel graph-based tracking algorithm which can bridge gaps in the sequence by linking leaf detections across a range of neighboring frames. We use the overlap of fitted leaf models as a pairwise similarity measure, and forbid graph edges that would link leaf detections within a single frame. We tested the method on a set of tobacco-plant growth sequences, and could track the first leaves of the plant, including partially or temporarily occluded ones, along complete sequences, demonstrating the applicability of the method to automatic plant growth analysis. All seedlings displayed approximately the same growth behavior, and a characteristic growth signature was found.

Consistent depth video segmentation using adaptive surface models.

We propose a new approach for the segmentation of 3-D point clouds into geometric surfaces using adaptive surface models. Starting from an initial configuration, the algorithm converges to a stable segmentation through a new iterative split-and-merge procedure, which includes an adaptive mechanism for the creation and removal of segments. This allows the segmentation to adjust to changing input data along the movie, leading to stable, temporally coherent, and traceable segments. We tested the method on a large variety of data acquired with different range imaging devices, including a structured-light sensor and a time-of-flight camera, and successfully segmented the videos into surface segments. We further demonstrated the feasibility of the approach using quantitative evaluations based on ground-truth data.

Local stimulus disambiguation with global motion filters predicts adaptive surround modulation.

Humans have no problem segmenting different motion stimuli despite the ambiguity of local motion signals. Adaptive surround modulation, i.e., the apparent switching between integrative and antagonistic modes, is assumed to play a crucial role in this process. However, so far motion processing models based on local integration have not been able to provide a unifying explanation for this phenomenon. This motivated us to investigate the problem of local stimulus disambiguation in an alternative and fundamentally distinct motion-processing model which uses global motion filters for velocity computation. Local information is reconstructed at the end of the processing stream through the constructive interference of global signals, i.e., inverse transformations. We show that in this model local stimulus disambiguation can be achieved by means of a novel filter embedded in this architecture. This gives rise to both integrative and antagonistic effects which are in agreement with those observed in psychophysical experiments with humans, providing a functional explanation for effects of motion repulsion.

Motion processing with wide-field neurons in the retino-tecto-rotundal pathway.

The retino-tecto-rotundal pathway is the main visual pathway in non-mammalian vertebrates and has been found to be highly involved in visual processing. Despite the extensive receptive fields of tectal and rotundal wide-field neurons, pattern discrimination tasks suggest a system with high spatial resolution. In this paper, we address the problem of how global processing performed by motion-sensitive wide-field neurons can be brought into agreement with the concept of a local analysis of visual stimuli. As a solution to this problem, we propose a firing-rate model of the retino-tecto-rotundal pathway which describes how spatiotemporal information can be organized and retained by tectal and rotundal wide-field neurons while processing Fourier-based motion in absence of periodic receptive-field structures. The model incorporates anatomical and electrophysiological experimental data on tectal and rotundal neurons, and the basic response characteristics of tectal and rotundal neurons to moving stimuli are captured by the model cells. We show that local velocity estimates may be derived from rotundal-cell responses via superposition in a subsequent processing step. Experimentally testable predictions which are both specific and characteristic to the model are provided. Thus, a conclusive explanation can be given of how the retino-tecto-rotundal pathway enables the animal to detect and localize moving objects or to estimate its self-motion parameters.

Contextual interactions in a generalized energy model of complex cells.

We propose a generalized energy model of complex cells to describe modulatory contextual influences on the responses of neurons in the primary visual cortex (V1). Many orientation-selective cells in V1 respond to contrast of orientation and motion of stimuli exciting the classical receptive field (CRF) and the non-CRF, or surround. In the proposed model, a central spatiotemporal filter, defining the CRF, is nonlinearly combined with a spatiotemporal filter extending into the non-CRF. These filters are assumed to describe simple-cell responses, while the nonlinear combination of their responses describes the responses of complex cells. This mathematical operation accounts for the inherent nonlinearity of complex cells, such as phase independence and frequency doubling, and for nonlinear interactions between stimuli in the CRF and surround of the cell, including sensitivity to feature contrast. If only the CRF of the generalized complex cell is stimulated by a drifting grating, the model reduces to the standard energy model. The theoretical predictions of the model are supported by computer simulations and compared with experimental data from V1.

Segment Tracking via a Spatiotemporal Linking Process including Feedback Stabilization in an n-D Lattice Model.

Model-free tracking is important for solving tasks such as moving-object tracking and action recognition in cases where no prior object knowledge is available. For this purpose, we extend the concept of spatially synchronous dynamics in spin-lattice models to the spatiotemporal domain to track segments within an image sequence. The method is related to synchronization processes in neural networks and based on superparamagnetic clustering of data. Spin interactions result in the formation of clusters of correlated spins, providing an automatic labeling of corresponding image regions. The algorithm obeys detailed balance. This is an important property as it allows for consistent spin-transfer across subsequent frames, which can be used for segment tracking. Therefore, in the tracking process the correct equilibrium will always be found, which is an important advance as compared with other more heuristic tracking procedures. In the case of long image sequences, i.e., movies, the algorithm is augmented with a feedback mechanism, further stabilizing segment tracking.

Synchronization from disordered driving forces in arrays of coupled oscillators.

The effects of disorder in external forces on the dynamical behavior of coupled nonlinear oscillator networks are studied. When driven synchronously, i.e., all driving forces have the same phase, the networks display chaotic dynamics. We show that random phases in the driving forces result in regular, periodic network behavior. Intermediate phase disorder can produce network synchrony. Specifically, there is an optimal amount of phase disorder, which can induce the highest level of synchrony. These results demonstrate that the spatiotemporal structure of external influences can control chaos and lead to synchronization in nonlinear systems.

Computing relative motion with complex cells.

Contextual influences shape our perception of local visual stimuli. Relative-motion stimuli represent an important contextual influence, yet the mechanism subserving relative-motion computation remains largely unknown. In the present work, we investigated the responses of an established model for simple and complex cells to relative-motion stimuli. A straightforward mathematical analysis showed that relative-motion computation is inherent in the nonlinear transformation of the complex-cell model. Tuning to relative velocity is achieved by applying a temporal filter to the complex-cell response. The mathematical inference is supported by simulations that quantitatively reproduce measured complex-cell responses in both cat and monkey to a variety of relative-motion stimuli. Importantly, the posited mechanism for cortical computation of relative motion does not require an intermediate neural representation of local velocities and does not require lateral or feedback interactions within a network.