Effective Morphological Transformation and Sub-pixel Classification of Clustered Images

The main aim of this research work is to perform the morphological operations with reduced time complexity and area complexity. Morphological operation is the key element in any image processing. Finding the maximum and minimum using a window of defined size will imply to the morphological dilation and erosion respectively. So the proposed algorithm should be fast in the comparison and sorting, this way the time complexity could be reduced. It’s believed that the anchor concept will fetch this cause. The idea behind this is it fixes a pixel and setting it as the center pixel all the surrounding pixels will be processed. Moreover this is now been implemented for rectangular structuring element. This paper attempts the same for flat and 3D structuring elements. Hyper-spectral Imaging is a developing zone of remote detecting applications. Hyper-spectral pictures incorporate more extravagant and better otherworldly data than the multi-spectral pictures got previously. Hyper-otherworldly pictures are described by an exchange off between the unearthly and spatial resolution. The principle issue of the hyper-ghostly information is the generally low spatial goal. For arrangement, the serious issue brought about by low spatial goal is the blended pixels. Blended pixels alluded to the pixels which are involved by more than one land spread class. In the proposed procedure another strategy is utilized to address the issue of blended pixels and to get a better spatial goal of the land spread characterization maps. The strategy misuses the upsides of both picture bunching methods and phantom dimming calculations, so as to decide the fragmentary plenitudes of the classes at a sub-pixel scale. Spatial regularization by Flank planning method is at last performed to spatially find the got classes at sub-pixel level.

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PROBLEMS OF CLASSIFICATION OF CULTURAL HERITAGE OBJECTS

Sociopolitical sciences ◽

10.33693/2223-0092-2020-10-2-158-168 ◽

2020 ◽

Vol 10 (2) ◽

pp. 158-168

Author(s):

SVETLANA IVANOVA ◽

Keyword(s):

Cultural Heritage ◽

Cultural Values ◽

Research Work ◽

Cultural Property ◽

Cultural Inheritance ◽

Federal Laws ◽

Cultural Heritage Objects ◽

Cultural Monuments

The purpose of the research work is to analyze the norms of Federal laws, as well as the laws of the Russian Federation's constituent entities, devoted to the definitions and classification of the concepts “cultural heritage”, “historical and cultural monuments”, “cultural values”. Conclusions obtained in the course of the research: based on the study of current legislation, it is concluded that the definitions of “cultural values”, “cultural property”, “objects of cultural inheritance” contained in various normative legal acts differ in content. Based on the research, the author proposes the concept of “cultural values”.

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Updated Efficient Area -Carry Select Adder for Low Complexity D LATCH Configuration by disease identification in brain tumor hyper spectral image

Current Signal Transduction Therapy ◽

10.2174/1574362414666191202100807 ◽

2019 ◽

Vol 14 ◽

Author(s):

Teresa V.V ◽

Anand. B

Keyword(s):

Research Work ◽

Low Complexity ◽

Propagation Delay ◽

Maximum Speed ◽

Hyper Spectral ◽

Ripple Carry Adder ◽

Minimum Power Consumption ◽

Carry Select Adder ◽

The Look ◽

Propagation Result

Objective: In this research work presents an efficient way Carry Select Adder (CSLA) performance and estimation. The CSLA is utilized in several system to mitigate the issue of carry propagation delay that is happens by severally generating various carries and to get the sum, select a carry because of the uses of various pairs of RCA to provide the sum of the partial section also carry by consisting carry input but the CSLA isn't time economical, then by the multiplexers extreme total and carry is chosen in the selected section. Methodology: The fundamental plan of this work is to attain maximum speed and minimum power consumption by using Binary to Excess-1. Convertor rather than RCA within the regular CSLA. Here RCA denotes the Ripple Carry Adder section. At the span to more cut back the facility consumption, a method of CSLA with D LATCH is implemented during this research work. The look of Updated Efficient Area -Carry Select Adder (UEA-CSLA) is evaluated and intended in XILINX ISE design suite 14. 5 tools. This VLSI arrangement is utilized in picture preparing application by concluding the cerebrum tumor discovery. Conclusion: In this study, medicinal pictures estimation, investigation districts in the multi phantom picture isn't that much proficient to defeat this disadvantage here utilized hyper spectral picture method is presented a sifting procedure in VLSI innovation restriction of cerebrum tumor is performed Updated Efficient Area - Carry Select Adder propagation result dependent on Matrix Laboratory in the adaptation of R2018b.

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An enhanced binarization framework for degraded historical document images

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00556-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Wei Xiong ◽

Lei Zhou ◽

Ling Yue ◽

Lirong Li ◽

Song Wang

Keyword(s):

Document Image ◽

Morphological Operations ◽

Document Images ◽

Minimum Entropy ◽

Stroke Width ◽

Background Estimation ◽

Structuring Element ◽

Document Image Binarization ◽

Benchmark Datasets ◽

Stroke Width Transform

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

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NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026806001757 ◽

2006 ◽

Vol 06 (01) ◽

pp. 61-75 ◽

Cited By ~ 15

Author(s):

R. PANCHAL ◽

B. VERMA

Keyword(s):

Breast Cancer ◽

Research Work ◽

Breast Cancer Diagnosis ◽

Classification Rate ◽

Test Dataset ◽

Benchmark Database ◽

Different Types ◽

Breast Tissues ◽

Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

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GUI-CAD Tool for Segmentation and Classification of Abnormalities in Lung CT Image

International Journal of Biomedical and Clinical Engineering ◽

10.4018/ijbce.2019010102 ◽

2019 ◽

Vol 8 (1) ◽

pp. 9-31

Author(s):

V. Vijaya Kishore ◽

R.V.S. Satyanarayana

Keyword(s):

Region Of Interest ◽

Performance Measure ◽

Morphological Operations ◽

Interactive 3D ◽

And Performance ◽

Aided Diagnosis ◽

Lung Ct ◽

Clinical Expert ◽

Clinical Determination

A vital necessity for clinical determination and treatment is an opportunity to prepare a procedure that is universally adaptable. Computer aided diagnosis (CAD) of various medical conditions has seen a tremendous growth in recent years. The frameworks combined with expanding capacity, the coliseum of CAD is touching new spaces. The goal of proposed work is to build an easy to understand multifunctional GUI Device for CAD that performs intelligent preparing of lung CT images. Functions implemented are to achieve region of interest (ROI) segmentation for nodule detection. The nodule extraction from ROI is implemented by morphological operations, reducing the complexity and making the system suitable for real-time applications. In addition, an interactive 3D viewer and performance measure tool that quantifies and measures the nodules is integrated. The results are validated through clinical expert. This serves as a foundation to determine, the decision of treatment and the prospect of recovery.

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