Evaluation of Edge Detection Techniques towards Implementation of Automatic Target Recognition

Sengottuvelan, P.; Wahi, Amitabh

doi:10.1109/iccima.2007.84

Cited by 17 publications

(10 citation statements)

References 16 publications

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“…Un segundo paso del proceso, reside en realizar el procedimiento de conteo de pines del dispositivo; este conteo se realizó mediante una detección de bordes, generados por las transiciones de oscuro a claro sobre una línea de identificación, tal como se mostró en la Fig. 2 [31][32][33][34].…”

Section: Proceso De Inspección De Imágenesunclassified

Distribución Binomial Aplicada a un Sistema de Clasificación de Piezas con Tratamiento Digital de Imágenes

Beainy¹,

Cáceres²,

López³

et al. 2011

Iteckne

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Resumen-Este trabajo describe el desarrollo de un sistema de clasificación de partes para un lote de producción, donde se utiliza un sistema de procesamiento digital de imágenes que permite reconocer las piezas cuando se reúnen o no las características definidas previamente.Para realizar y analizar el control de calidad para el lote de producción, se utiliza una densidad de probabilidad discreta, que se usa frecuentemente en los procesos de control de calidad. La distribución utilizada fue la distribución binomial, ampliamente empleada en procesos de control de calidad en situaciones cuya solución tiene dos posibles resultados, éxito o fracaso, de un parámetro de un conjunto de muestras establecido.Palabras clave-Distribución Binomial, Reconocimiento de Bordes, Procesamiento de Imágenes, Control de Calidad, Inspección Óptica Automatizada.Abstract-This paper describes the development of a parts classification system for a production lot, where it is used a digital processing system that enables viewers to recognize the pieces when they meet or not previously defined characteristics.To perform and analyze the quality control for the production lot is used a discrete probability density, which is frequently used in quality control processes. This distribution was the Binomial distribution, widely used in quality control processes in situations where a solution has two possible outcomes, success or failure of a parameter of a set of samples provided. INTRODUCCIÓNEn la actualidad las industrias de manufactura o producción realizan actividades de control de calidad donde se espera detectar que ocurra o no un evento específico. Este evento puede ser de éxito o fracaso, sin que haya un punto intermedio de decisión. Cuando se realiza la producción de un componente o artículo, se ejecutan procedimientos de verificación para determinar si este cumple o no con unas características previamente definidas como se referencia en [11][12][13][14][15][16]. Para estas situaciones se utiliza la distribución Binomial.La calidad puede definirse como una combinación de características de ingeniería y de fabricación que determina el grado de satisfacción y utilidad que el producto proporciona al consumidor durante su uso, así mismo la calidad está regida por algún estándar de producción establecida por un ente normalizador.De esta manera se puede entender que el control de calidad es un factor fundamental para la competitividad de los sectores productivos, y por ello, organizaciones del mundo establecen estándares para los campos de producción.Los errores y límites de detección de defectos que han superado la percepción del ojo humano, han promovido el desarrollo de sistemas capaces de ejecutar tareas repetitivas de forma automática, con bajas tolerancias de desviación.

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Section: Proceso De Inspección De Imágenesunclassified

Distribución Binomial Aplicada a un Sistema de Clasificación de Piezas con Tratamiento Digital de Imágenes

Beainy¹,

Cáceres²,

López³

et al. 2011

Iteckne

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“…It is therefore desirable to be able to automatically identify the aircraft model from as little radar information as possible which usually means detecting the aircraft model from only the first pulses that are reflected back from the attacking aircraft. Many schemes of ISAR target recognition have been developed that test only the features taken from the ISAR images with high efficiency, [1]- [4]. This saves the time exhausted while trying to compare the whole target aircraft.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for radar image identification and classification

Zeljković

Tameze

Vincelette

2010

2010 International Conference on High Performance Computing &Amp; Simulation

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We present and compare two different novel methods for classification of aircraft categories of Inverse Synthetic Aperture Radar (ISAR) images. The first method forms numerical equivalents to shape, size and other aircraft features as critical criteria to constitute the algorithm for their correct classification. The second method compares each ISAR image to unions of images of the different aircraft categories. We computer simulated five different categories of ISAR images and took two more from the internet. ISAR images are constructed based on the Doppler shifts of various parts, caused by the rotation of the aircraft and the radar reflection pulse shape which includes the size or duration of the radar pulse. The proposed classification algorithms were tested on these seven categories. All seven different aircraft models are flying a holding pattern. The aim of both algorithms is to quickly match and determine the similarity of the captured aircraft to the seven different categories where the aircraft is in any position of a prescribed holding pattern. Our experimental results clearly indicate that in most parts of the holding pattern the category of the aircraft can be successfully identified with both proposed methods. The union method shows more successful identification results and is superior to the results we obtained in the first proposed method.

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“…Image segmentation is usually done using various edge detection techniques such as Sobel, Prewitt, Roberts, Canny, and other methods [7]. Then, only some features characterizing the ISAR images are tested, to identify what kind of target has been detected [8]- [11]. In fact, the typical algorithms first detect the edge of an ISAR image, and then adopt different 1-D descriptors such as Fourier descriptors (FD) [12] or 2-D descriptors such as Fourierwavelet descriptors [13] for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier

Benedetto¹,

Fulginei²,

Laudani³

et al. 2012

IJACSA

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Abstract-This work proposes a new automatic target classifier, based on a combined neural networks' system, by ISAR image processing. The novelty introduced in our work is twofold. We first present a novel automatic classification procedure, and then we discuss an improved multimedia processing of ISAR images for automatic object detection. The classifier, composed by a combination of 20 feed-forward artificial neural networks, is used to recognize aircraft targets extracted from ISAR images. A multimedia processing by two recently introduced image processing techniques is exploited to improve the shape and features extraction process. Performance analysis is carried out in comparison with conventional multimedia techniques and standard detectors. Numerical results obtained from wide simulation trials evidence the efficiency of the proposed method for the application to automatic aircraft target recognition.

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Evaluation of Edge Detection Techniques towards Implementation of Automatic Target Recognition

Cited by 17 publications

References 16 publications

Distribución Binomial Aplicada a un Sistema de Clasificación de Piezas con Tratamiento Digital de Imágenes

Distribución Binomial Aplicada a un Sistema de Clasificación de Piezas con Tratamiento Digital de Imágenes

Algorithms for radar image identification and classification

Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier

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