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Elanangai, V.
- Identification and recognition of Leaf Disease Using Enhanced Segmentation Techniques
Abstract Views :91 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Siddhant College of Engineering, IN
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, IN
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
1 Department of Information Technology, Siddhant College of Engineering, IN
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, IN
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2825-2830Abstract
Segmenting refers to the technique of breaking up an image into its component parts one by one. When it comes to the process of segmenting photos, there is a plethora of choice available at current point in time. These options range from the easy thresholding approach to the complicated color image segmentation techniques. The bulk of the time, the parts that go into making up these sub-assemblies are items that individuals are able to easily identify and categorize as being distinct from one another. As a result of the limitation of computer lack of intelligence to differentiate between distinct items, a wide variety of techniques have been devised and utilized in the process of segmenting photographs. In order to complete its tasks, the image segmentation algorithm requires a wide range of image characteristics to be provided as input. This could be referring to the colors that are contained within an image, the borders that are included within the image, or a particular region that is contained within the image. In order to break down color images into their component elements, we make use of an algorithm that is inspired by natural selection. The research uses enhanced segmentation techniques to identify and recognize the leaf disease in plants. The study conducts extensive simulation to test the efficacy of the model. The results show that the proposed method achieves higher segmentation accuracy than other methods.Keywords
No Keywords.
References
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- An Improvised Video Enhancement Using Machine Learning
Abstract Views :90 |
PDF Views:1
Authors
Affiliations
1 Government B.Ed. Training College, Kalinga, IN
2 Department of Computer Science and Engineering, K.S.K College of Engineering and Technology, IN
3 Department of Electrical and Electronics Engineering, St. Peter Institute of Higher Education and Research, IN
4 Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, IN
1 Government B.Ed. Training College, Kalinga, IN
2 Department of Computer Science and Engineering, K.S.K College of Engineering and Technology, IN
3 Department of Electrical and Electronics Engineering, St. Peter Institute of Higher Education and Research, IN
4 Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2844-2848Abstract
The visual quality of underexposed videos can be increased with the help of a process known as video enhancement. Within the scope of this research, we present a novel approach to enhancing the exposure of movies that are underexposed. Because it has a low barrier to entry theoretically and can reliably give perceptually pleasant outcomes without adding artifacts, it is ideal for a wide variety of practical applications. This is because it is useful for a wide variety of practical applications. We demonstrate the usefulness of the method by displaying improved films of good quality that were made from a variety of different sorts of underexposed videos. A novel approach to the enhancement and editing of video is presented by our method. Rather than relying on a single heuristic transform function, it makes use of human visual perception to adaptively customize the overall visual appearance of the finished product. We believe that our work has the potential to make a big influence in the field of perception-aware video editing and the applications that are related to it, and that it is an important contribution to the community that works on improving videos. The challenge of video enhancement can be formulated as follows: given a video of low quality as the input, produce a video of high quality as the output, but only for specific uses. Whether it be in terms of the video objective clarity or its more subjective qualities, we are interested in learning how we might improve its overall quality.Keywords
Visual Appearance, Video Enhancement, Machine Learning.References
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