Extracting a Bounded Region from a Map Using Flood Fill Algorithm

2021 ◽  
pp. 14-20
Author(s):  
S. M. Abdullah Al Shuaeb ◽  
Md. Kamruzzaman ◽  
Mohammad Hazrat Ali
Keyword(s):  
Image Processing ◽  
Important Task ◽  
Second Phase ◽  
Bounded Region ◽  
Separate Sheet ◽  
Flood Fill

Extracting the needed portion from a bounded region is an important task in image processing. Editing a map and extracting a region from the map is challenging. It is useful in some contexts to have a region in a separate sheet. In this image processing, we have used the Flood Fill algorithm to extract a region from the image map. To achieve that goal, we had worked in our study to separate a bounded region on a map. Usually, a scanned map may contain a lot of useless information. So we have to process the image to remove useless information from the map. We had quantized the image to a binary one. In the second phase, we have applied a gray color to separate the desired position from a map. Our main objective of the study to extract a bounded region from mapping an image that contains useless information and removes it. We have experimented with several maps and it works successfully.

Atomic structure of interface of intermetallic compound TiAl and nitride Ti2AIN using HREM and image processing

1991 ◽  
Vol 49 ◽  
pp. 570-571
Author(s):  
E. Sukedai ◽  
H. Mabuchi ◽  
H. Hashimoto ◽  
Y. Nakayama
Keyword(s):  
Mechanical Properties ◽  
Image Processing ◽  
Composite Material ◽  
Intermetallic Compound ◽  
Side Surface ◽  
High Resolution Electron Microscopy ◽  
Hexagonal Structure ◽  
Second Phase ◽  
Resolution Electron ◽  
Compound Tial

In order to improve the mechanical properties of an intermetal1ic compound TiAl, a composite material of TiAl involving a second phase Ti2AIN was prepared by a new combustion reaction method. It is found that Ti2AIN (hexagonal structure) is a rod shape as shown in Fig.1 and its side surface is almost parallel to the basal plane, and this composite material has distinguished strength at elevated temperature and considerable toughness at room temperature comparing with TiAl single phase material. Since the property of the interface of composite materials has strong influences to their mechanical properties, the structure of the interface of intermetallic compound and nitride on the areas corresponding to 2, 3 and 4 as shown in Fig.1 was investigated using high resolution electron microscopy and image processing.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Fuzzy Clustering of Vertical Two Phase Flow Regimes Based on Image Processing Technique

2010 ◽  
Author(s):  
Soheil Ghanbarzadeh ◽  
Pedram Hanafizadeh ◽  
Mohammad Hassan Saidi ◽  
Ramin Bozorgmehry B.
Keyword(s):  
Image Processing ◽  
Two Phase Flow ◽  
Extraction Procedure ◽  
Flow Regimes ◽  
Flow Patterns ◽  
Image Processing Technique ◽  
Test Facility ◽  
Second Phase ◽  
Phase Flow ◽  
Two Phase

In order to safe design and optimize performance of industrial systems which work under two phase flow conditions, it’s often needed to categorize flow into different regimes. In present work the experiments of two phase flow were done in a large scale test facility with length of 6m and 5cm diameter. Four main flow regimes were observed in vertical air-water two phase flows at moderate superficial velocities of gas and water: Bubbly, Slug, Churn and Annular. Some image processing techniques were used to extract information from each picture. This information include number of bubbles or objects, area, perimeter, height and width of objects (second phase). Also a texture feature extraction procedure was applied to images of different regimes. Some features which were adequate for regime identification were extracted such as Contrast, Energy, Entropy and etc. To identify flow regimes a fuzzy interface was introduced using characteristic of second phase in picture. Also an Adaptive Neuro Fuzzy (ANFIS) was used to identify flow patterns using textural features of images. The experimental results show that these methods can accurately identify the flow patterns in a vertical pipe.

scholarly journals Efficient Processing of Image Processing Applications on CPU/GPU

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Najia Naz ◽  
Abdul Haseeb Malik ◽  
Abu Bakar Khurshid ◽  
Furqan Aziz ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Image Processing ◽  
Heterogeneous Systems ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Phase Image ◽  
Second Phase ◽  
Healthcare Applications ◽  
Big Visual Data ◽  
Workload Distribution ◽  
Two Phases

Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

Image Processing Methodology for Determining SI Precipitate Size and Density in Oxide Layers from Conical Dark Field TEM Micrographs

2001 ◽  
Vol 7 (S2) ◽  
pp. 832-833
Author(s):  
A. Domenicucci
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Dark Field ◽  
Precipitate Size ◽  
Second Phase ◽  
Oxide Breakdown ◽  
Image Processing Techniques ◽  
Transmission Electron ◽  
The Matrix ◽  
Processing Techniques

Image processing techniques have been used for decades in many branches of science. with the advent of low cost, highresolution CCD cameras and the advances in personal computing, techniques previously used in other disciplines are increasingly being applied by transmission electron microscopists. The present paper gives an example of using image processing techniques for characterizing the number and size of second phase precipitates in an oxide matrix.Si inclusions in the form of Si precipitates can occur in silicon dioxide films. The inclusions are contained within the films and effectively reduce the local thickness of the oxide. This thinning results in a reduction in the voltage necessary to cause oxide breakdown; the larger is the precipitate, the lower the breakdown voltage. Knowledge of the precipitate size and density is therefore important when assessing the dielectric integrity of these films. The Si precipitates are crystalline and more or less randomly oriented within the matrix.

scholarly journals Drivable Road Corridor Detection using Flood Fill Road Detection Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 5011-5014
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Image Processing Technique ◽  
Road Detection ◽  
The Road ◽  
Detection Algorithms ◽  
Color Differences ◽  
On The Road ◽  
Flood Fill

Current image processing techniques for drivable road detection make use of lane markings. However, most roads lack lane markings which make such techniques obsolete. For such conditions, an image processing technique is required which identifies the boundaries of the road based on the color differences between the road and the surroundings. This paper proposes a flood fill road detection approach in which we first analyze a sample of the road and compute its RGB pixel distribution. The pixel range is used to detect the other road pixels in the image. Edge detection algorithms are then applied on the detected road to give road edge. It classifies the road on the basis of the visible differences between the road and its neighborhood. It allows for subtle color differences on the road surface, and unlike a color mask, due to the inherent growing nature of a flood fill algorithm, it does not detect neighborhood elements beyond the boundary having features similar to the road. This technique also manages to detect any obstructions on the road as opposed to other edge detection algorithms. We also propose methods to enable quick computation of otherwise expensive flood-fill algorithm. The method was tested on both marked and unmarked lanes and produced satisfying results for both images and videos.

scholarly journals Image To Speech Synthesizer with Reference to Assamese Numerals

2019 ◽  
Vol 9 (1) ◽  
pp. 900-905
Keyword(s):  
Image Processing ◽  
Template Matching ◽  
Research Work ◽  
Second Phase ◽  
Digital Form ◽  
Speech Synthesizer ◽  
Image Processing Techniques ◽  
Digital World ◽  
Matching Technique ◽  
Processing Techniques

In this research work we have shown the methodology for converting printed Assamese numerals to its corresponding utterance. We have implemented as an initial effort which will read only four digit numerals. We are using Image processing techniques to convert an image of Assamese numerals into textual/digital form. In the second phase the numerals will be pronounced as a number by Google speaker. In this system, images are stored in a dataset and then inputted data is compared with the dataset image using template matching technique. After recognition of the text output will be displayed as a speech waveform. This work has many applications in today’s digital world

scholarly journals Linking of Cell Tracks using Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 3020-3026
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Literature Review ◽  
Parallel Algorithms ◽  
Digital Image ◽  
Important Task ◽  
Basic Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms

In maximum of image processing algorithms Segmentation is a key technique. It splits a digital image into several regions so as to examine them. Once segmentation is done, linking of frames is additionally is very important task. Several image segmentation techniques are developed by the researchers so as to form pictures swish and simple to judge. It is troublesome to process parallel algorithms in serial processors. This paper presents a literature review of basic image segmentation techniques, linking algorithms and want to process in hardware tools.

scholarly journals Cancer Clumps Detection using Image Processing Based on Cell Counting and Artificial Neural Network Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 5124-5126 ◽  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Cancer Cell ◽  
Feedback System ◽  
Area Measurement ◽  
Cell Counting ◽  
Medical Sciences ◽  
Artificial Neural ◽  
Flood Fill

Cancer is one of the main reasons for death among humans. So much research has been done for detecting and diagnosing cancer using image processing and classification and techniques. But the disease remains as one of the deadeist disease. Thus early detection of the disease is only one of the reasons to cure the cancer. In this proposed technique identifying cancer cell by using Image Processing, Artificial Neural Network techniques using cell counting, area measurement and detection of clumps. With the help of proposed technique we detect the cancer traits of any CT image, mammography image of biopsy samples automatically. So many algorithms was proposed but there was a lack of flexibility and the level of accuracy is not consists. Before applying proposed algorithm, the system preprocesses the input images with various techniques like gray scaling, binarization, inversion and flood fill operation. The proposed method can be work on various images and fine tuned with a feedback system and if can effectively used for automatically detection of cancer cells in a unique way and lead to open up new dimension in detecting cancer cell in the field of medical sciences.

Human and Object Detection in Smoke-Filled Space Using Millimeter-Wave Radar Based Measurement

2006 ◽  
Vol 18 (6) ◽  
pp. 760-764 ◽  
Author(s):  
Yoshimitsu Aoki ◽  
◽  
Masaki Sakai
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Millimeter Wave ◽  
Important Task ◽  
3D Image ◽  
3D Image Processing ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
Measurement Experiment ◽  
Do So

One of the greatest problems in rescue operations during fire disasters is the blocking of firefighters’ view by dense smoke. Assuming that a firefighter’s most important task is to understand the situation within a smoke-filled space. We developed a way to do so, starting by scanning space using millimeter-wave radar combined with a gyrosensor. To detect persons and objects, we constructed a 3D map from signal reflection datasets using 3D image processing. We detail our proposal and report results of measurement experiment in actual smoke-filled areas.

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