A voting-based computational framework for visual motion analysis and interpretation

Most approaches for motion analysis and interpretation rely on restrictive parametric models and involve iterative methods which depend heavily on initial conditions and are subject to instability. Further difficulties are encountered in image regions where motion is not smooth-typically around motion boundaries. This work addresses the problem of visual motion analysis and interpretation by formulating it as an inference of motion layers from a noisy and possibly sparse point set in a 4D space. The core of the method is based on a layered 4D representation of data and a voting scheme for affinity propagation. The inherent problem caused by the ambiguity of 2D to 3D interpretation is usually handled by adding additional constraints, such as rigidity. However, enforcing such a global constraint has been problematic in the combined presence of noise and multiple independent motions. By decoupling the processes of matching, outlier rejection, segmentation, and interpretation, we extract accurate motion layers based on the smoothness of image motion, and then locally enforce rigidity for each layer in order to infer its 3D structure and motion. The proposed framework is noniterative and consistently handles both smooth moving regions and motion discontinuities without using any prior knowledge of the motion model.     

A framework for computational anatomy

The rapid collection of brain images from healthy and diseased subjects has stimulated the development of powerful mathematical algorithms to compare, pool and average brain data across whole populations. Brain structure is so complex and variable that new approaches in computer vision, partial differential equations, and statistical field theory are being formulated to detect and visualize disease-specific patterns. We present some novel mathematical strategies for computational anatomy, focusing on the creation of population-based brain atlases. These atlases describe how the brain varies with age, gender, genetics, and over time. We review applications in Alzheimer's disease, schizophrenia and brain development, outlining some current challenges in the field.     

The detection and measurement of visual motion

The properties of the one and two dimensional Gabor filters are reviewed. These filters are optimally localised in both the image space and frequency space. Spatial frequency and temporal frequency are related by the image plane velocity. These properties are exploited in a filter scheme to detect and measure visual motion. Experimental results are reported.     

A computational framework for institutional agency

This paper provides a computational framework, based on defeasible logic, to capture some aspects of institutional agency. Our background is Kanger-Lindahl-Pörn account of organised interaction, which describes this interaction within a multi-modal logical setting. This work focuses in particular on the notions of counts-as link and on those of attempt and of personal and direct action to realise states of affairs. We show how standard defeasible logic (DL) can be extended to represent these concepts: the resulting system preserves some basic properties commonly attributed to them. In addition, the framework enjoys nice computational properties, as it turns out that the extension of any theory can be computed in time linear to the size of the theory itself.     

A computational framework for location analysis

Location analysis decisions are interrelated and should be made within a single decision-making framework. A framework within which a number of location strategies can be placed is presented. Location-allocation models are improved in two ways: 1) the allocation rule is developed to more accurately reflect customer choice processes; and 2) the objective function is developed to incorporate future changes. Computational support for this framework is described.     

A computational algorithm for inners

A Gaussian elimination algorithm is proposed to compute the determinants of inners of certain types of matrices. This algorithm applies to the clustering of roots as well as to the roots distribution of a given polynomial. Several examples on stability, relative stability, and aperiodicity for both continuous and discrete systems are presented.     

A computational model for visual selection.

We propose a computational model for detecting and localizing instances from an object class in static gray-level images. We divide detection into visual selection and final classification, concentrating on the former: drastically reducing the number of candidate regions that require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings that are moderately likely at certain places on the object but rate in the background. We show that the statistics in both populations are stable. The candidate regions are those that contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such as responses to coarse or incomplete patterns (e.g., illusory contours) and to scale and translation invariance in IT. Finally, the algorithm is applied to face and symbol detection.     

A threshold-based thinning algorithm for a visual,automated snow-cover measurement system

An automated snow-cover measuring system is developed to measure the amount of snowfall by analyzing the visual image of a reference pole under an unstructured outdoor environment. The system consists of a reference pole, a CCD camera (including an infra-red module), and a PC which transfers the processed information to remote users via the Internet. The snow depth is estimated based on the lowest uncovered position of the pole with the captured image. After correcting the image distortion through an expansive coefficient curve, the corrected image is compared to a virtual measuring scale (VMS) for the accurate measurement. To enhance the visual measurement process, the captured images are pre-processed and the camera is calibrated for the natural outside light condition of the varied weather. The snow-cover measuring (SCM) algorithm is used to detect the height of the piled snow. This measuring system can also continuously transfer the raw images, as well as the estimated snow depth to remote clients through the Internet. The experimental results show that the system improves the reliability and accuracy of the measurement, and that it is convenient to use.