Border Figure Detection Using a Phase Oscillator Network with Dynamical Coupling

Oscillator networks have been developed in order to perform specific tasks related to image processing. Here we analytically investigate the existence of synchronism in a pair of phase oscillators that are short-range dynamically coupled. Then, we use these analytical results to design a network able of detecting border of black-and-white figures. Each unit composing this network is a pair of such phase oscillators and is assigned to a pixel in the image. The couplings among the units forming the network are also dynamical. Border detection emerges from the network activity.

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Integumentary Colour Allocation in the Stork Family (Ciconiidae) Reveals Short-Range Visual Cues for Species Recognition

Birds ◽

10.3390/birds2010010 ◽

2021 ◽

Vol 2 (1) ◽

pp. 138-146

Author(s):

Eduardo J. Rodríguez-Rodríguez ◽

Juan J. Negro

Keyword(s):

Short Range ◽

Visual Cues ◽

Species Recognition ◽

The Body ◽

Whole Body ◽

Divergent Evolution ◽

Black And White ◽

Extant Species ◽

Chromatic Spectrum ◽

Mixed Species Flocks

The family Ciconiidae comprises 19 extant species which are highly social when nesting and foraging. All species share similar morphotypes, with long necks, a bill, and legs, and are mostly coloured in the achromatic spectrum (white, black, black, and white, or shades of grey). Storks may have, however, brightly coloured integumentary areas in, for instance, the bill, legs, or the eyes. These chromatic patches are small in surface compared with the whole body. We have analyzed the conservatism degree of colouration in 10 body areas along an all-species stork phylogeny derived from BirdTRee using Geiger models. We obtained low conservatism in frontal areas (head and neck), contrasting with a high conservatism in the rest of the body. The frontal areas tend to concentrate the chromatic spectrum whereas the rear areas, much larger in surface, are basically achromatic. These results lead us to suggest that the divergent evolution of the colouration of frontal areas is related to species recognition through visual cue assessment in the short-range, when storks form mixed-species flocks in foraging or resting areas.

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Mathematical Morphology and Fourier Analysis on Time Scales

Handbook of Fourier Analysis & Its Applications ◽

10.1093/oso/9780195335927.003.0017 ◽

2009 ◽

Author(s):

Robert J Marks II

Keyword(s):

Image Processing ◽

Fourier Analysis ◽

Mathematical Morphology ◽

Time Scales ◽

Time Scale ◽

Time Signal ◽

Scale Theory ◽

Black And White ◽

Introductory Material ◽

Special Cases

Mathematical morphology, used extensively in image processing, tracks the support domains for the operation of convolution and deconvolution. Morphology is also important in the modelling of signals on time scales. Time scale theory provides a generalization tent under which the operations of discrete and continuous time signal and Fourier analysis rest as special cases. The time scale paradigm provides modelling under which a rich class of hybrid signals and systems can be analyzed. We begin with introductory material on mathematical morphology which is foundational to the development of time scale theory. The support of convolution is related to the operation of dilation in mathematical morphology. Mathematical morphology is most commonly associated with image processing. Applications of morphology was initially applied to binary black and white images by Matheron [966]. The field is richly developed [506, 578]. Here, we outline the fundamentals. In N dimensions, let X and H denote a set of vectors or, in the degenerate case of one dimension, a set of real numbers.

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Multi-Template Processing of Fingerprint Recognition Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.332 ◽

2014 ◽

Vol 610 ◽

pp. 332-338

Author(s):

Lian Ying Zou ◽

Ying Zhou ◽

Xiang Dong ◽

Yu Chen

Keyword(s):

Image Processing ◽

Recognition Algorithm ◽

Fingerprint Recognition ◽

Fingerprint Image ◽

Ridge Line ◽

Black And White ◽

White Point ◽

Direction Filter ◽

Fingerprint Feature ◽

Line Texture

Using multi-template processing algorithm, the fingerprint features are accurately collected. Through normalization, make the black and white point contrast of the fingerprint image more obviously, strengthen the ridge line texture. Direction calculating algorithm is based on the grey value of the neighborhood pixels. It can be implemented simply and speedily. Through direction filter, noises can be removed, and the contrast of the fingerprint’s ridge lines and valley lines can be enhanced. After binary converting, all information of the fingerprint is stored with 0 and 1. The effect of thinning is to make the fingerprint image more distinct to extract the fingerprint feature point easily. These steps had been implemented on Altera DE2 board with HDL codes. The experimental results indicate that the multi-template algorithm of fingerprint image processing is correct and practicable.

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Image processing techniques for lemons and tomatoes classification

Bragantia ◽

10.1590/s0006-87052008000300029 ◽

2008 ◽

Vol 67 (3) ◽

pp. 785-789 ◽

Cited By ~ 23

Author(s):

Antonio Carlos Loureiro Lino ◽

Juliana Sanches ◽

Inacio Maria Dal Fabbro

Keyword(s):

Image Processing ◽

Classification System ◽

Fruit Shape ◽

Morphological Operations ◽

Border Detection ◽

Image Processing Techniques ◽

Operational Costs ◽

Image Improvement ◽

Processing Techniques ◽

Vegetable Quality

Vegetable quality is frequently referred to size, shape, mass, firmness, color and bruises from which fruits can be classified and sorted. However, technological by small and middle producers implementation to assess this quality is unfeasible, due to high costs of software, equipment as well as operational costs. Based on these considerations, the proposal of this research is to evaluate a new open software that enables the classification system by recognizing fruit shape, volume, color and possibly bruises at a unique glance. The software named ImageJ, compatible with Windows, Linux and MAC/OS, is quite popular in medical research and practices, and offers algorithms to obtain the above mentioned parameters. The software allows calculation of volume, area, averages, border detection, image improvement and morphological operations in a variety of image archive formats as well as extensions by means of "plugins" written in Java.

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AIRCRAFT CLASSIFICATION USING IMAGE PROCESSING TECHNIQUES AND ARTIFICIAL NEURAL NETWORKS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001411009044 ◽

2011 ◽

Vol 25 (08) ◽

pp. 1321-1335 ◽

Cited By ~ 5

Author(s):

ADIL GURSEL KARACOR ◽

ERDAL TORUN ◽

RASIT ABAY

Keyword(s):

Image Processing ◽

Image Data ◽

Radar Data ◽

Still Image ◽

Image Processing Techniques ◽

Black And White ◽

Rf Signals ◽

Different Types ◽

Artificial Neural ◽

Processing Techniques

Identifying the type of an approaching aircraft, should it be a helicopter, a fighter jet or a passenger plane, is an important task in both military and civilian practices. The task in question is normally done by using radar or RF signals. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. The image was transformed to a binary black and white image, using a Matlab script which utilizes Image Processing Toolbox commands of Matlab, in order to extract the necessary features. The extracted image data of four different types of aircraft was fed into a three-layered feed forward artificial neural network for classification. Satisfactory results were achieved as the rate of successful classification turned out to be 97% on average.

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An algorithm to measure unsymmetrical circle shape of intravascular ultrasound image using image processing techniques

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i1.2694 ◽

2021 ◽

Vol 10 (1) ◽

pp. 508-515

Author(s):

Suhaili Beeran Kutty ◽

Rahmita Wirza O. K. Rahmat ◽

Sazzli Shahlan Kassim ◽

Hizmawati Madzin ◽

Hazlina Hamdan

Keyword(s):

Image Processing ◽

Intravascular Ultrasound ◽

Ultrasound Image ◽

Region Of Interest ◽

Processing Technique ◽

Human Interaction ◽

Image Processing Technique ◽

Border Detection ◽

Cross Sectional ◽

Minimum Diameter

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape.

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