Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

An edge is a set of connected pixels lying on the boundary between two regions in an image that differs in pixel intensity. Accordingly, several gradient-based edge detectors have been developed that are based on measuring local changes in gray value; a pixel is declared to be an edge pixel if the change is significant. However, the minimum value of intensity change that may be considered to be significant remains a question. Therefore, it makes sense to calculate the edge-strength at every pixel on the basis of the intensity gradient at that pixel point. This edge-strength gives a measure of the potentiality of a pixel to be an edge pixel. In this paper, we propose to use a set of fuzzy rules to estimate the edge-strength. This is followed by selecting a threshold; only pixels having edge-strength above the threshold are considered to be edge pixels. This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). Experimental results demonstrate the effectiveness of our proposed edge detection method over some other standard gradient-based methods

Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval

Content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades. While the field is relatively mature, significant research is still required in this area to develop solutions for practical applications. One reason that practical solutions have not yet been realized could be due to a limited understanding of the cognitive aspects of the human vision system. Inspired by three cognitive properties of human vision, namely, hierarchical structuring, color perception and embedded compressive sensing, a new CBIR approach is proposed. In the proposed approach, the Hue, Saturation and Value (HSV) color model and the Similar Gray Level Co-occurrence Matrix (SGLCM) texture descriptors are used to generate elementary features. These features then form a hierarchical representation of the data to which a two-dimensional compressive sensing (2D CS) feature mining algorithm is applied. Finally, a weighted feature matching method is used to perform image retrieval. We present a comprehensive set of results of applying our proposed Hierarchical Visual Perception Enabled 2D CS approach using publicly available datasets and demonstrate the efficacy of our techniques when compared with other recently published, state-of-the-art approaches

Detection of fragmented rectangular enclosures in very high resolution remote sensing images

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.European Prehistor

A computational model of space-variant vision based on a self-organised artificial retina tessellation

Abstract available: p.i-iii

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection

Psychophysical and neurophysiological evidence about the human visual system shows the existence of a mechanism, called surround suppression, which inhibits the response of an edge in the presence of other similar edges in the surroundings. A simple computational model of this phenomenon has been previously proposed by us, by introducing an inhibition term that is supposed to be high on texture and low on isolated edges. While such an approach leads to better discrimination between object contours and texture edges w.r.t. methods based on the sole gradient magnitude, it has two drawbacks: first, a phenomenon called self-inhibition occurs, so that the inhibition term is quite high on isolated contours too; previous attempts to overcome self-inhibition result in slow and inelegant algorithms. Second, an input parameter called “inhibition level” needs to be introduced, whose value is left to heuristics. The contribution of this paper is two-fold: on one hand, we propose a new model for the inhibition term, based on the theory of steerable filters, to reduce self-inhibition. On the other hand, we introduce a simple method to combine the binary edge maps obtained by different inhibition levels, so that the inhibition level is no longer specified by the user. The proposed approach is validated by a broad range of experimental results.

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion