scholarly journals Object Segmentation from Background of 2D Image

2018 ◽  
Vol 26 (5) ◽  
pp. 204-215 ◽  
Author(s):  
Nada Abdullah Rasheed ◽  
Wessam Lahmod Nados
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
High Precision ◽  
Object Segmentation ◽  
The Other ◽  
New Method ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Optimal Classification

One of the difficult tasks in the image processing field and still not solved is segmentation of object from background of 2D image accurately. Therefore, a new method has been proposed for the purpose of segmenting the object from its background for the purpose of enhancing the images and obtains characteristics of the object without the rest of the region of the image. This process is important to provide optimal classification in the process of pattern recognition. Therefore, this paper proposed the method that includes several tasks, after loading the six files of images; this work applies the segmentation al- gorithm depending on the border and the color of the object. Finally, 2D median filtering algorithm was employed to remove noisy objects of various shapes and sizes. The algo- rithm was tested on variety images, and the results are high precision. In the other words, the proposed method is able to segment the objects from the background with promising results.

Computer Vision and robotics in postal automation

1999 ◽  
Vol 18 (3-4) ◽  
pp. 265-273
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Material Handling ◽  
State Of The Art ◽  
Short Description ◽  
The Other ◽  
Functional Requirements ◽  
Postal Automation ◽  
And Robotics

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

PERFORMANCE COMPARISON OF DENOISING METHODS FOR HISTORICAL DOCUMENTS

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Sayed Muchallil ◽  
Fitri Arnia ◽  
Khairul Munadi ◽  
Fardian Fardian
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Performance Comparison ◽  
The Other ◽  
Historical Documents ◽  
Median Filtering ◽  
Document Images ◽  
Post Processing ◽  
Binary Images ◽  
Complete Procedure

Image denoising plays an important role in image processing.  It is also part of the pre-processing technique in a binarization complete procedure that consists of pre-processing, thresholding, and post-processing.  Our previous research has confirmed that the Discrete Cosine Transform (DCT)-based filtering as the new pre-processing process improved the performance of binarization output in terms of recall and precision. This research compares three classical denoising methods; Gaussian, mean, and median filtering with the DCT-based filtering. The noisy ancient document images are filtered using those classical filtering methods. The outputs of this process are used as the input for Otsu, Niblack, Sauvola and NICK binarization methods. Then the resulted binary images of the three classical methods are compared with those of DCT-based filtering. The performance of all denoising algorithms is evaluated by calculating recall and precision of the resulted binary images.  The result of this research is that the DCT based filtering resulted in the highest recall and precision as compared to the other methods. 

Implementation of the Fast Median Filtering Algorithm Based on FPGA

2014 ◽  
Vol 513-517 ◽  
pp. 1055-1058
Author(s):  
Jin Lun Li ◽  
Shao Hui Cui ◽  
Ku Nao Guo
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing System ◽  
Difficult Problem ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Functional Simulation ◽  
Real Time Image ◽  
Embedded Image ◽  
Time Image

Real-time image processing has been a difficult problem in embedded image processing system. The traditional MCU could not meet the real-time demand when large volume of data awaited to be proceed. FPGA is an effective driver to achieve real-time parallel processing of data. The implementation rationale and the design of module have been given in this article; and the Hard Software has been truly achieved. At the end of the article, the simulation waveform graph has been obtained by processing functional simulation on algorithm module by using Modelsim software; and the simulation result shows that this design is able to proper functioning and has good application prospects.

Study on an Improved Median Filtering Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2273-2277
Author(s):  
Heng Zhang ◽  
Min Gao ◽  
Hai Long Ren
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Median Filter ◽  
Median Filtering ◽  
Matlab Simulation ◽  
Filtering Algorithm ◽  
Noise Point ◽  
Real Time Image ◽  
Linear Smoothing ◽  
Time Image

Median filter is a very effective method of non-linear smoothing filtering. However, the data ordering for traditional median filtering (TMF) is very time-consuming and hardly satisfy real-time image processing. This article proposes a kind of fast median filtering algorithm based on grey histogram, the filter seeks the median through the grey histogram of mask window, not the numeric sort, which decreases the comparison times. Moreover, the update of histogram by using the overlap of mask window increases the arithmetic processing speed. Meanwhile, in the proposed algorithm, the two-level self-adapting threshold comparison, with a higher precision of detection, is used to implement the inspection of noise point and improve the image quality and increase the signal-noise ratio by processing the noise point and non-noise point respectively. The experiments by matlab simulation can prove the availability of this algorithm.

Microscopic Image Processing for the Analysis of Nosema Disease

2019 ◽  
pp. 28-46
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-Gonzalez ◽  
Mohamed Salah Gouider ◽  
Melvin Ramírez Bogantes ◽  
Rafael A. Calderon ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
New Technologies ◽  
The Other ◽  
Microscopic Image ◽  
Fundamental Challenge ◽  
Microscopic Image Processing ◽  
Nosema Disease ◽  
Selection Of

In this chapter, the authors tried to develop a tool to automatize and facilitate the detection of Nosema disease. This work develops new technologies in order to solve one of the bottlenecks found on the analysis bee population. The images contain various objects; moreover, this work will be structured on three main steps. The first step is focused on the detection and study of the objects of interest, which are Nosema cells. The second step is to study others' objects in the images: extract characteristics. The last step is to compare the other objects with Nosema. The authors can recognize their object of interest, determining where the edges of an object are, counting similar objects. Finally, the authors have images that contain only their objects of interest. The selection of an appropriate set of features is a fundamental challenge in pattern recognition problems, so the method makes use of segmentation techniques and computer vision. The authors believe that the attainment of this work will facilitate the diary work in many laboratories and provide measures that are more precise for biologists.

The Median Filtering Algorithm’s Fast Implementation in FPGA

2011 ◽  
Vol 230-232 ◽  
pp. 1054-1057
Author(s):  
Dao De Zhang ◽  
Yu Rong Pan ◽  
Xin Yu Hu ◽  
Guang You Yang ◽  
Cheng Xu
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Median Filter ◽  
Clock Cycle ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Fast Implementation ◽  
Exchange Principle

Based on FPGA’S Balance and exchange principle of area and speed, Using the FPGA internal rich logic resources and powerful hardware characteristics , the traditional median filtering algorithm is reduced to 2 clock cycle , Greatly improving the image processing speed . And by using threshold, in a certain extent, reducing the image fuzzy phenomena brought by the median filter . The results of test show that the system runs stability, the time of achieving the median filtering algorithm are narrowed to the shortest clock cycle.

scholarly journals Continuity Detection Method in Binary Image Signal

2020 ◽  
Vol 26 (6) ◽  
pp. 4-9
Author(s):  
Radim Hercik ◽  
Zdenek Machacek ◽  
Jiri Koziorek ◽  
Jan Vanus ◽  
Miroslav Schneider ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Processing Speed ◽  
Detection Method ◽  
Computing Time ◽  
Binary Image ◽  
New Method ◽  
Large Object ◽  
Processing Methods ◽  
Detecting Method

This paper is focused on a description of a new method of detecting continuities in a binary image. The detecting method is called “Binary Large Object” (BLOB). A new algorithm with other elementary parameters, as processing speed and memory capability, are described here. The developed BLOB method with described algorithms is implemented in MATLAB. The simulation of the algorithms is tested in different conditions, with the time dependences determination. The research results of the computing time or the BLOB memory demand during computation are presented as well. The developed BLOB method is usable as one of the elementary image processing methods of the field detection dependencies in the images, like pattern recognition or OCR algorithm.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

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