Pinned Repositories
Bilateral-Filtering
A bilateral filter is a non-linear, edge-preserving, and noise-reducing smoothing filter for images. It replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. This weight can be based on a Gaussian distribution. Crucially, the weights depend not only on Euclidean distance of pixels, but also on the radiometric differences (e.g., range differences, such as color intensity, depth distance, etc.). This preserves sharp edges.
Edge-Detection---Image-Processing
Edge detection methods for finding object boundaries in images Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Common edge detection algorithms include Sobel, Canny, Prewitt, Roberts, and fuzzy logic methods.
Interactive-Login-Page
Used - HTML, CSS, JS
Movie-Lenghts-Case-Study
Program-GUI
GUI of a software prototype with latest features developed to aid surveillance systems' monitoring.
Research-Internships-for-UG-Students
A comprehensive list of research opportunities for undergraduate students
Telecome-Consumer-Complaints-Data-Analytics-PYTHON
Data Dictionary Ticket #: Ticket number assigned to each complaint Customer Complaint: Description of complaint Date: Date of complaint Time: Time of complaint Received Via: Mode of communication of the complaint City: Customer city State: Customer state Zipcode: Customer zip Status: Status of complaint Filing on behalf of someone Analysis Task To perform these tasks, you can use any of the different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup. - Import data into Python environment. - Provide the trend chart for the number of complaints at monthly and daily granularity levels. - Provide a table with the frequency of complaint types. Which complaint types are maximum i.e., around internet, network issues, or across any other domains. - Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed. - Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on: Which state has the maximum complaints Which state has the highest percentage of unresolved complaints - Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.
Turbulence-Mitigation
DeTurbulence using non-deep methods.
Video-Quality-Metrics
Video quality metrics are algorithms designed to predict how actual viewers would gauge video quality. These metrics are used for a range of activities, from comparing codecs and different encoding configurations, to assisting in production and live quality of experience (QoE) monitoring. Image quality can degrade due to distortions during image acquisition and processing. Examples of distortion include noise, blurring, ringing, and compression artifacts. Efforts have been made to create objective measures of quality. For many applications, a valuable quality metric correlates well with the subjective perception of quality by a human observer. Quality metrics can also track unperceived errors as they propagate through an image processing pipeline, and can be used to compare image processing algorithms. If an image without distortion is available, you can use it as a reference to measure the quality of other images. For example, when evaluating the quality of compressed images, an uncompressed version of the image provides a useful reference. In these cases, you can use full-reference quality metrics to directly compare the target image and the reference image. If a reference image without distortion is not available. you can use a no-reference image quality metric instead. These metrics compute quality scores based on expected image statistics.
Video-to-frames
Basic requirements for video analytics -- frame separation, frame rate, frame resize, Average of the frames
Kavyapriyakp's Repositories
Kavyapriyakp/Research-Internships-for-UG-Students
A comprehensive list of research opportunities for undergraduate students
Kavyapriyakp/Video-Quality-Metrics
Video quality metrics are algorithms designed to predict how actual viewers would gauge video quality. These metrics are used for a range of activities, from comparing codecs and different encoding configurations, to assisting in production and live quality of experience (QoE) monitoring. Image quality can degrade due to distortions during image acquisition and processing. Examples of distortion include noise, blurring, ringing, and compression artifacts. Efforts have been made to create objective measures of quality. For many applications, a valuable quality metric correlates well with the subjective perception of quality by a human observer. Quality metrics can also track unperceived errors as they propagate through an image processing pipeline, and can be used to compare image processing algorithms. If an image without distortion is available, you can use it as a reference to measure the quality of other images. For example, when evaluating the quality of compressed images, an uncompressed version of the image provides a useful reference. In these cases, you can use full-reference quality metrics to directly compare the target image and the reference image. If a reference image without distortion is not available. you can use a no-reference image quality metric instead. These metrics compute quality scores based on expected image statistics.
Kavyapriyakp/Edge-Detection---Image-Processing
Edge detection methods for finding object boundaries in images Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Common edge detection algorithms include Sobel, Canny, Prewitt, Roberts, and fuzzy logic methods.
Kavyapriyakp/Video-to-frames
Basic requirements for video analytics -- frame separation, frame rate, frame resize, Average of the frames
Kavyapriyakp/Bilateral-Filtering
A bilateral filter is a non-linear, edge-preserving, and noise-reducing smoothing filter for images. It replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. This weight can be based on a Gaussian distribution. Crucially, the weights depend not only on Euclidean distance of pixels, but also on the radiometric differences (e.g., range differences, such as color intensity, depth distance, etc.). This preserves sharp edges.
Kavyapriyakp/Interactive-Login-Page
Used - HTML, CSS, JS
Kavyapriyakp/Program-GUI
GUI of a software prototype with latest features developed to aid surveillance systems' monitoring.
Kavyapriyakp/Turbulence-Mitigation
DeTurbulence using non-deep methods.
Kavyapriyakp/Zoho-Analytics-for-Environmental-Science
A dashboard consisting of simple visualizations and analytics of environmental dataset using Zoho Analytics tool.
Kavyapriyakp/algorithms
Algorithms and data structures in many languages.
Kavyapriyakp/Environmental-Hydrology-for-Data-Science
Kavyapriyakp/AngularModules
Kavyapriyakp/Awesome-Deblurring
A curated list of resources for Image and Video Deblurring
Kavyapriyakp/Blur-Detection
Kavyapriyakp/Customer-Service-Analysis---Data-Analytics
Kavyapriyakp/DeOldify-1
A Deep Learning based project for colorizing and restoring old images (and video!)
Kavyapriyakp/Detection-of-Moving-Object
Dectect and track the moving objects with camera.
Kavyapriyakp/FirstAngular
Kavyapriyakp/github-profile-readme-generator
🚀 Generate GitHub profile README easily with the latest add-ons like visitors count, GitHub stats, etc using minimal UI.
Kavyapriyakp/HTML-Cards
Used - HTML, CSS, JS
Kavyapriyakp/Intersection-Over-Union---Object-Detection
Kavyapriyakp/kavyapriyakp
Kavyapriyakp/NASA-Ally
Breaking barriers, Coming together as one! 🙌
Kavyapriyakp/NetworkX-Supply-Chain-Simulation
Generate Supply Chain Network with NetworkX package and simulate by using SimPy
Kavyapriyakp/NursieRaTor---Team-HACSICS-HackChennai-
NursieRaTor
Kavyapriyakp/portfolio
Kavyapriyakp/Python-programs
Kavyapriyakp/PYTHON-SCRIPTS
Kavyapriyakp/Surveillance
Kavyapriyakp/Video-Quality-Metrics-with-Graph-Visualiation