Automatic Soil Classification by using Gabor Wavelet & Support Vector Machine in Digital Image Processing

2021 ◽  
Author(s):  
Shraddha Shivhare ◽  
Kanchan Cecil
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Digital Image Processing ◽  
Digital Image ◽  
Soil Classification ◽  
Gabor Wavelet ◽  
Support Vector

scholarly journals SURF Points Versus SIFT Points in Identification of Medicinal Plants

2019 ◽  
Vol 9 (2) ◽  
pp. 602-607
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Digital Image ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

scholarly journals Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1800 ◽  
Author(s):  
Kyi Pyar Win ◽  
Yuttana Kitjaidure ◽  
Kazuhiko Hamamoto ◽  
Thet Myo Aung
Keyword(s):  
Image Processing ◽  
Cervical Cancer ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Digital Image Processing ◽  
Digital Image ◽  
Pap Smear ◽  
Support Vector ◽  
Computer Assisted ◽  
Screening System

Cervical cancer can be prevented by having regular screenings to find any precancers and treat them. The Pap test looks for any abnormal or precancerous changes in the cells on the cervix. However, the manual screening of Pap smear in the microscope is subjective with poorly reproducible criteria. Therefore, the aim of this study was to develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images. The analysis of Pap smear image is important in the cervical cancer screening system. There were four basic steps in our cervical cancer screening system. In cell segmentation, nuclei were detected using a shape-based iterative method, and the overlapping cytoplasm was separated using a marker-control watershed approach. In the features extraction step, three important features were extracted from the regions of segmented nuclei and cytoplasm. RF (random forest) algorithm was used as a feature selection method. In the classification stage, bagging ensemble classifier, which combined the results of five classifiers—LD (linear discriminant), SVM (support vector machine), KNN (k-nearest neighbor), boosted trees, and bagged trees—was applied. SIPaKMeD and Herlev datasets were used to prove the effectiveness of our proposed system. According to the experimental results, 98.27% accuracy in two-class classification and 94.09% accuracy in five-class classification was achieved using the SIPaKMeD dataset. When the results were compared with five classifiers, our proposed method was significantly better in two-class and five-class problems.

scholarly journals A Review on Automatic Soil Classification in Digital Image Processing

2021 ◽  
Vol 9 (VIII) ◽  
pp. 449-455
Author(s):  
Shraddha Shivhare
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
False Alarm ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Time ◽  
Soil Classification ◽  
Research Papers ◽  
Digital World ◽  
Precision Rate

Soil classification is an essential piece of geology. However, many examinations have assessed the precision and consistency of the soil classification using various techniques. This examination starts by evaluating the verifiable advancement of soil classification science. The verifiable audit contextualizes the wordings and the speculations of soil development factors, which supported soil classification frameworks. This paper is intended to review some research papers on soil classification and analyze the limitations of implemented techniques by their parameters. In the age of digital world, it is beneficial to obtain the information from image without any hassle. Machine learning is an approach through which we can obtain the better level of accuracy and minimize the false alarm rate. But machine learning requires so many samples through which we can observe the correct precision that also requires much storage that may takes much processing time that reduces the feasibility of the system. We have to train a system with limited number of samples with high iterations that produces higher precision rate with minimal errors.

scholarly journals Short Communication: Geometric morphometric analysis of leaf venation in four Shorea species for identification using Digital Image Processing

2020 ◽  
Vol 21 (7) ◽  
Author(s):  
Ishak Ariawan ◽  
YENI HERDIYENI ◽  
ISKANDAR ZULKARNAEN SIREGAR
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Morphometric Analysis ◽  
Short Communication ◽  
Length Ratio ◽  
Support Vector ◽  
Ratio Scale ◽  
Geometric Morphometric ◽  
Geometric Morphometric Analysis

Abstract. Ariawan I, Herdiyeni Y, Siregar IZ. 2020. Short Communication: Geometric morphometric analysis of leaves venation in Shorea spp. for identification using Digital Image Processing. Biodiversitas 21: 3303-3309. Shorea is one of the genera of the Dipterocarpaceae family which consists of more than 190 species. Massive exploitation of forests has threatened the sustainability of Shorea in nature. A total of 156 species has been listed on the IUCN (International Union for Conservation of Nature) red list. From the 156 species, 59.6% are in the critically endangered category, so urgent conservation is needed. However, during collection of Shorea at the seedling phase for conservation  purposes, it is often difficult to distinguish among them that can cause errors in their collection process. To avoid these errors, identification needs to be done, usually based on plant  leaf and flower  morphology. Leaves are easier because they have the main features that distinguish each plant species, one of which is the venation structure. Geometric morphometric techniques are a modern approach recognized as useful for the identification of species in many plants. Geometric morphometrics analyzes the position of the venation point using coordinate geometry values. This research was aimed to extract venation features of Shorea leaves using a geometric morphometric approach. The extraction process result in some features, such as straightness, different angle, length ratio, scale projection, and secondary nerves. On extracted features, an analysis was then performed to find out the best features in classifying species of Shorea spp. The results of this study indicated that the geometric morphometric approach could extract the value of the features of straightness, different angle, length ratio, scale projection, and secondary nerves. The secondary nerve feature is the best feature because it can distinguish between fourcommonly planted species of Shorea spp. (S. acuminata, S. leprosula, S. ovalis, and S. selanica). By using the support vector machine classification technique to identify species of Shorea spp., the classification results obtained an average accuracy of 84.46%.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

Complementary Z-Contrast Imaging and Digital Image Processing

1985 ◽  
Vol 43 ◽  
pp. 304-305
Author(s):  
K. N. Colonna ◽  
G. Oliphant
Keyword(s):  
Image Processing ◽  
Heavy Metal ◽  
Digital Image Processing ◽  
Digital Image ◽  
Biological Tissue ◽  
Biological Imaging ◽  
Tissue Preparation ◽  
Contrast Imaging ◽  
Imaging Tool ◽  
Z Contrast

Harmonious use of Z-contrast imaging and digital image processing as an analytical imaging tool was developed and demonstrated in studying the elemental constitution of human and maturing rabbit spermatozoa. Due to its analog origin (Fig. 1), the Z-contrast image offers information unique to the science of biological imaging. Despite the information and distinct advantages it offers, the potential of Z-contrast imaging is extremely limited without the application of techniques of digital image processing. For the first time in biological imaging, this study demonstrates the tremendous potential involved in the complementary use of Z-contrast imaging and digital image processing.Imaging in the Z-contrast mode is powerful for three distinct reasons, the first of which involves tissue preparation. It affords biologists the opportunity to visualize biological tissue without the use of heavy metal fixatives and stains. For years biologists have used heavy metal components to compensate for the limited electron scattering properties of biological tissue.

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