An Approach to Improvise Canny Edge Detection using Morphological Filters

Nayak, Ankitha. A.; Venugopala, P. S.; Sarojadevi, H.; Chiplunkar, Niranjan N.

doi:10.5120/20368-2575

Cited by 9 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To measure the length of the branches we used the Measurement Microscope Tools Pluging (http://fiji.sc/Microscope_Measurement_Tools). The Canny Edge Detector Pluging [24] was used to find the initial segments of the most marked borders/edge. All plugins were obtained from GitHub source repository (https://imagej.nih.gov/ij/plugins).…”

Section: Methodsmentioning

confidence: 99%

Cell survival and differentiation with nanocrystalline glass-like carbon using substantia nigra dopaminergic cells derived from transgenic mouse embryos

et al. 2017

View full text Add to dashboard Cite

Regenerative medicine requires, in many cases, physical supports to facilitate appropriate cellular architecture, cell polarization and the improvement of the correct differentiation processes of embryonic stem cells, induced pluripotent cells or adult cells. Because the interest in carbon nanomaterials has grown within the last decade in light of a wide variety of applications, the aim of this study was to test and evaluate the suitability and cytocompatibility of a particular nanometer-thin nanocrystalline glass-like carbon film (NGLC) composed of curved graphene flakes joined by an amorphous carbon matrix. This material is a disordered structure with high transparency and electrical conductivity. For this purpose, we used a cell line (SN4741) from substantia nigra dopaminergic cells derived from transgenic mouse embryos. Cells were cultured either in a powder of increasing concentrations of NGLC microflakes (82±37μm) in the medium or on top of nanometer-thin films bathed in the same culture medium. The metabolism activity of SN4741 cells in presence of NGLC was assessed using methylthiazolyldiphenyl-tetrazolium (MTT) and apoptosis/necrosis flow cytometry assay respectively. Growth and proliferation as well as senescence were demonstrated by western blot (WB) of proliferating cell nuclear antigen (PCNA), monoclonal phosphorylate Histone 3 (serine 10) (PH3) and SMP30 marker. Specific dopaminergic differentiation was confirmed by the WB analysis of tyrosine hydroxylase (TH). Cell maturation and neural capability were characterized using specific markers (SYP: synaptophysin and GIRK2: G-protein-regulated inward-rectifier potassium channel 2 protein) via immunofluorescence and coexistence measurements. The results demonstrated cell positive biocompatibility with different concentrations of NGLC. The cells underwent a process of adaptation of SN4741 cells to NGLC where their metabolism decreases. This process is related to a decrease of PH3 expression and significant increase SMP30 related to senescence processes. After 7 days, the cells increased the expression of TH and PCNA that is related to processes of DNA replication.On the other hand, cells cultured on top of the film showed axonal-like alignment, edge orientation, and network-like images after 7 days. Neuronal capability was demonstrated to a certain extent through the analysis of significant coexistence between SYP and GIRK2. Furthermore, we found a direct relationship between the thickness of the films and cell maturation. Although these findings share certain similarities to our previous findings with graphene oxide and its derivatives, this particular nanomaterial possesses the advantages of high conductivity and transparency. In conclusion, NGLC could represent a new platform for biomedical applications, such as for use in neural tissue engineering and biocompatible devices.

show abstract

Section: Methodsmentioning

confidence: 99%

Cell survival and differentiation with nanocrystalline glass-like carbon using substantia nigra dopaminergic cells derived from transgenic mouse embryos

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Additionally, [8]. the quality of the photos for remote sensing can be improved with typical brightness, contrast, and sharpening [9] [10]. A finite collection of regions, X1, X2, X3, etc., are present in the image X after brightness thresholding and full segmentation.…”

Section: A Image Enhancementmentioning

confidence: 99%

“…In this study, the proposed methods were used to the image to discover the edges in the remote sensing data, and the PSNR rate value was used to determine which PSNR rate value was optimum for the IRS P6 satellite image. [9] Enhancing the edge extraction of quantum images with the Sobel operator to keep the key characteristics. With this filter, the difficulty in terms of time and space to calculate the grey gradient with greater accuracy increases.…”

Section: B Edge Detectionmentioning

confidence: 99%

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

Kumar,

Uma

et al. 2023

IJRITCC

View full text Add to dashboard Cite

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%.

show abstract

“…Erosion replaces the current pixel value with the minimum value found in a defined set of pixels. Dilation replaces the current pixel value with the maximum value found in a defined set of pixels [30]…”

mentioning

confidence: 99%

Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ

Yin

et al. 2021

Agriculture

View full text Add to dashboard Cite

Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, apart from the region of interest, contain unwanted parts of plants, soil, rocks, and/or human body parts. Segmentation helps isolate the target region and a deep convolutional neural network classifies images precisely. Therefore, we combined edge and morphological based segmentation, background subtraction, and the convolutional neural network to help improve accuracy on image sets with images containing clean and cluttered backgrounds. In the proposed system, segmentation was applied to first extract leaf images in the foreground. Several images contained a leaf of interest interposed between unfavorable foregrounds and backgrounds. Background subtraction was implemented to remove the foreground image followed by segmentation to obtain the region of interest. Finally, the images were classified by a pre-trained classification network. The experimental results on two, four, and eight classes of datasets show that the proposed method achieves 98.7%, 96.7%, and 93.57% accuracy by fine-tuned DenseNet121, InceptionV3, and DenseNet121 models, respectively, on a clean dataset. For two class datasets, the accuracy obtained was about 12% higher for a dataset with images taken in the homogeneous background compared to that of a dataset with testing images with a cluttered background. Results also suggest that image sets with clean backgrounds tend to start training with higher accuracy and converge faster.

show abstract

An Approach to Improvise Canny Edge Detection using Morphological Filters

Cited by 9 publications

References 10 publications

Cell survival and differentiation with nanocrystalline glass-like carbon using substantia nigra dopaminergic cells derived from transgenic mouse embryos

Cell survival and differentiation with nanocrystalline glass-like carbon using substantia nigra dopaminergic cells derived from transgenic mouse embryos

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ

Contact Info

Product

Resources

About