Oriented filters are useful in many early vision and image processing tasks. One often needs to apply the same filter, rotated to different angles under adaptive control, or wishes to calculate the filter response at various orientations. We present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively "steer" a filter to any orientation, and to determine analytically the filter output as a function of orientation.

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research...

Two paradigms for visual analysis are top-down, starting from high-level models or information about the image, and bottom-up, where little is assumed about the image or objects in it. We explore a local, bottom-up approach to image analysis. We develop operators to identify and classify image junctions, whichcontain important visual cues for identifying occlusion, transparency, and surface bends. Like the human visual system, we begin with the application of linear filters which are oriented in all possible directions. Wedevelop an efficientway to create an oriented filter of arbitrary orientation by describing it as a linear combination of basis filters. This approach to oriented filtering, which we call steerable filters, offers advantages for analysis as well as computation. We design a variety of steerable filters, including steerable quadrature pairs, which measure local energy. We show applications of these filters in orientation and texture analysis, and image representation and enhanc...

This paper details the construction of three-dimensional separable steerable filters. The approach presented is an extension of the construction of two-dimensional separable steerable filters outlined in [1]. Additionally, three-dimensional separable steerable filters, both continuous and discrete versions, for the second derivative of the Gaussian and its Hilbert transform are reported. Experimental evaluation demonstrates that the errors in the constructed separable filters are negligible.

Introduction A detailed analysis of the constituents of fitoplancton, including identification and counting, is particularly important to evaluating ecological conditions of water, to preventing and controlling undesirable situations concerning its usage, and to follow up the developing of fitoplanctonic forms. Algae are filaments, which size is variable, thickness is approximately constant, and geometry is not definite. Through an appropriate increasing it is possible to measure the maximum and minimum thickness, corresponding to algae. This parameter enables a computational procedure to detect automatically the presence of algae, after enhancing the edges of the objects in scenario. Additionally the operator can include or exclude image parts, in order to achieve an acceptable measuring of the amount of biomass in the water sample. This work has been developed with participation of the Department of Computer Science, the Department of Zoology in the University of Brasília, an

The problems under consideration in this dissertation centre around the representation of visual spacetime, i.e., (visual) image intensity (irradiance) as a function of two-dimensional spatial position and time. In particular, the overarching goal is to establish a unified approach to representation and analysis of temporal image dynamics that is broadly applicable to the diverse phenomena in the natural world as captured in two-dimensional intensity images. Previous research largely has approached the analysis of visual dynamics by appealing to representations based on image motion. Although of obvious importance, motion represents a particular instance of the myriad spatiotemporal patterns observed in image data. A generative model centred on the concept of spacetime orientation is proposed. This model provides a unified framework for understanding a broad set of important spacetime patterns. As a consequence of this analysis, two new classes of patterns are distinguished that have previously not been considered directly in terms of their constituent spacetime oriented structure, namely multiplicative motions (e.g., translucency) and stochastic-related phenomena (e.g.,