Acoustooptic morphological image processing with electronic feedback

A three-dimensional (3D) laser scanning system is the main part of reverse engineering. It is a comprehensive technological system, including numerical control (NC), a laser, computer-aided design/manufacturing (CAD/CAM), precision mechanisms, computing image sampling, and image processing. Generally, it consists of a motion control system, an image sampling system, and an image processing system, etc. This paper focuses on introducing control principles, hardware constitutions, and software algorithms of the servo motion system, and analyzes and presents the hardware constitutions, software algorithms, and experimental results of the image sampling and processing system in great detail. Finally, the authors come to the conclusion that the errors all occur within the range ±0.1 mm.

In this paper, a novel image denoising method with a morphological opening is proposed. In many image recovery, the regularization has been introduced to prevent overfitting between a recovered image and a noisy observation. In the regularization of an image, the smoothness of the image is assumed. The cost function, which is minimized during the regularization, is defined as a sum of the approximation error term and the smoothness penalty of the image. In morphological image processing, an image is approximated as a union of structuring elements (SEs) by the morphological image opening. In the approximation with the opening, the SE represents the local intensity surface of the image. We hence impose the smoothness penalty on the SE, instead of the recovered image itself, and propose an image opening method with the regularization penalty for the SE. In denoising, the approximation error is minimized under the constraint of the penalty for the SE. In experiments, we show that the proposed filtering method has ability to remove impulsive noises from degraded texture images.

Fast parallel processing of gray-scale images and exact hard-clip thresholding are two important functionalities necessary in optoelectronic implementations of structural processors. The parallel nature of processing stems from optical implementation of local operations with arrays of active smart pixels. We have demonstrated a morphological image processor composed of arrays of bistable optoelectronic transceivers which are connected in differential pairs and work as comparators. The use of differential pairs of optical thyristors fabricated in GaAs technology allows to realize a dual rail architecture for this photonic morphological image processor. The processor consists of a thresholding module and a binary morphological processing module. The thresholding module decomposes gray level images into series of binary slices. In the binary morphological processing module operations are performed within a neighborhood defined by a structuring element implemented as a diffractive fan-out element. In the prototype set-up we demonstrate median filtering, dilation and erosion operations performed for an image of 8x8 pixels and threshold decomposition of 6 gray level images. In principle all rank order as well as morphological filters can be optically calculated in the set-up. Additional functionality of the processor is achieved with use of the electronic layer with digital cellular processors. The electronic layer, designed as an array of simple digital processors, realizes a set of operations on binary images using 4 bit programmable weights. Simulation results for 0.8 µm CMOS technology are presented. We discuss the limitations of the photonic morphological image processing with respect to bandwidth, parallelism and architecture of the processor.

RETRACTED: Urban ecological forestry analysis based on FPGA and dynamic image sampling

We present a demonstrator photonic system for mathematical morphology image processing. Optically interconnected arrays of differential pairs of optoelectronic transceivers realize morphological erosion and dilation in an 8x8 pixel size image.

Decomposition of geometric-shaped structuring elements using morphological transformations on binary images

Assessment of the Miocene aquifer in Wadi El Farigh area by using GIS techniques

Edge detection is a fundamental step in many machine vision or image processing applications and systems. The importance of edge detection increases as one seeks to increase the level of automation in the image processing system. The paper reports an edge detection operator which is fast in application to grey scale images and is sensitive to 'difficult' edges but not sensitive to artifacts such as scanner interlacing artifacts. The edge detection operator presented is simple but provides good detection of edges and clean and continuous edges across most of the image.

Statistical kinetics for pulverized coal combustion

Performance of an image analysis processing system for hen tracking in an environmental preference chamber

An image processing system that incorporated some retinal properties was investigated for the processing of two dimensional images. The system was required to carry out basic image processing tasks such as edge detection. A new filtering technique was deduced from the physiological findings on the distribution of the receptive fields of the retinal ganglion cells. This filtering technique was then incorporated in the design of an image processing system in, which the spatial resolution increased linearly towards the centre of, the image. The design, was based on a discrete polar distribution of processing areas on an inhomogeneous triangular sampling grid. This resulted in a highly localized processing system which simplified the development of the higher image processing tasks such asboundary following. The retinal image processing system was simulated on a VAX 11/750. The computational cost of conducting operations such as edge detection, boundary detection and boundary following, using the designed system, was evaluated and compared with that of the conventional image processing system.

ABSTRACT: The aim of this paper was to verify the viability of digital image processing in the measurement of rice seedling and its potential use as seed vigor test. The experiment took place at the Laboratório Didático de Análise de Sementes, located in Capão do Leão (RS), using seeds of eight rice cultivars: Irga 424, Puitá Inta CL, BRS Esmeralda, SCS 116, Guri Inta CL, BRS Pampa, BRSGO Serra Dourada and Olimar. An image processing system (IPS) was used to measure length and area of shoot and root as an index of seed vigor in rice seeds. The experiment design was completely randomized with five replications. The evaluated variables were root and shoot length in graded ruler and by image, root and shoot area, and field emergence. Results obtained with the IPS were correlated with other quality tests though the Pearson tests. Determination of shoot length and leaf area of the seedlings obtained through IPS is effective for assessing the vigor of rice seeds.

With increasing applications of image processing in manufacturing, it has become necessary to provide industry with powerful as well as flexible means of adapting the technology to the solution of different kinds of tasks at hand. Besides the standard components of an image processing system, the availability ofvarious software tools to fill the gap between design, digitization, measurement and the manufacturing phases should be emphasized. The users could e.g., desire to transfer grabbed images into certain computer graphics data format for further processing. They may want to apply image processing for the measurement of machine parts interactively or automatically, or they may want to convert the pictures processd, e.g. with edge detectors, into some CAD data format, or even to generate a numerical control file to machine the parts directly from the image processing data. With this motivation, two software packages FIPS—Flexible Image Processing System, and IQIS— Integrated QUality Inspection System have been developed for manufacturing and quality inspection and have been used by industry for various purposes from simple image digitization and processing, luminance adjustment, data and file format conversion, generation of control files for numerical control in laser cuffing, to direct control of robot movement, and interactive as well as automatic machine part measurement.

A morphological image enhancement method based on the GAN (General Adaptive Neighborhood) structuring elements is proposed for some problems such as low overall contrast, noise and inter-target edge of some remote sensing images. Firstly, a pixel to be processed is selected as a seed point, and an adaptive neighborhood is defined according to the image pixel characteristic and the neighborhood constraint relation to obtain the self-adaptive structuring elements set. A corresponding self-adaptive morphological operation based on the self-adaptive structuring elements set is constructed with reference to the classical morphological operation definition, and the self-adaptive alternative filter is constructed by using the adaptive morphological operation combination so as to realize the enhancement of the remote sensing image. The experimental results show that the method can preserve the accurate positioning of the objects edge while enhancing the image, avoid the loss of information, has better noise removing performance. Compared with the classical morphological filter operator, the single-scale Retinex algorithm and bi-histogram equalization (BHE) algorithm, the contrast ratio and the signal-to-noise ratio of the enhanced image are improved.

In this paper, novel binary adaptive neighborhood morphological operators are proposed to solve the problem that the fixed structure elements cannot adapt to the whole image when processing images in classical mathematical morphology. The key of adaptive morphology is to select suitable structure elements according to the different attributes of an image. On the basis of image local features and pixel position information, the structure elements selected in this paper can be adaptively adjusted. Besides, the new structure elements have symmetry, namely they are symmetrical about the center point. New binary adaptive neighborhood morphological operators are also defined according to the new structure elements. Comparing with the experimental results of the classical morphological operations, the new operators can retain the details of the image better and be more similar to the original image, which shows the new operators is more effective and practical.