algorithms

Algorithms and data structures in many languages.

angularmodules

awesome-deblurring

A curated list of resources for Image and Video Deblurring

background-subtraction---gmm

Gaussian Mixture Model (GMM) is popular method that has been employed to tackle the problem of background subtraction. However, the output of GMM is a rather noisy image which comes from false classification. This situation may arise because several conditions in the video input such as, waving trees, rippling water, and illumination changes. In this paper, an enhanced version of GMM technique which is combined with Hole Filling Algorithm (HF) is proposed to alleviate those problems. The experimental result shows that the proposed method improved the accuracy up to 97.9% and Kappa statistic up to 0.74. This result has outperformed many similar methods that is used for evaluation.

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.

blur-detection

customer-service-analysis---data-analytics

dense-optical-flow

Dense Optical flow computes the optical flow vector for every pixel of the frame which may be responsible for its slow speed but leading to a better accurate result. It can be used for detecting motion in the videos, video segmentation, learning structure from motion. There can be various kinds of implementations of dense optical flow. The example below will follow the Farneback method along with OpenCV. The first step is that the method approximates the windows of image frames by a quadratic polynomial with the help of the polynomial expansion transform. Next, by observing how the polynomial transforms under the state of motion. i.e. to estimate displacement fields. Dense optical flow is computed, after a series of refinements. For OpenCV’s implementation, the magnitude and direction of optical flow from a 2-D channel array of flow vectors are computed for the optical flow problem. The angle (direction) of flow by hue is visualized and the distance (magnitude) of flow by the value of HSV color representation. The strength of HSV is always set to a maximum of 255 for optimal visibility.

deoldify-1

A Deep Learning based project for colorizing and restoring old images (and video!)

detection-of-moving-object

Dectect and track the moving objects with camera.

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.

environmental-hydrology-for-data-science

firstangular

github-profile-readme-generator

🚀 Generate GitHub profile README easily with the latest add-ons like visitors count, GitHub stats, etc using minimal UI.

html-cards

Used - HTML, CSS, JS

interactive-login-page

Used - HTML, CSS, JS

intersection-over-union---object-detection

kavyapriyakp

movie-lenghts-case-study

nasa-ally

Breaking barriers, Coming together as one! 🙌

networkx-supply-chain-simulation

Generate Supply Chain Network with NetworkX package and simulate by using SimPy

nursierator---team-hacsics-hackchennai-

NursieRaTor

optical-flow

Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and a scene. The term optical flow is also used by roboticists, encompassing related techniques from image processing and control of navigation including motion detection, object segmentation, time-to-contact information, focus of expansion calculations, luminance, motion compensated encoding, and stereo disparity measurement. Optical flow was used by robotics researchers in many areas such as: object detection and tracking, image dominant plane extraction, movement detection, robot navigation and visual odometry. The application of optical flow includes the problem of inferring not only the motion of the observer and objects in the scene, but also the structure of objects and the environment. Since awareness of motion and the generation of mental maps of the structure of our environment are critical components of animal (and human) vision, the conversion of this innate ability to a computer capability is similarly crucial in the field of machine vision.

portfolio

program-gui

GUI of a software prototype with latest features developed to aid surveillance systems' monitoring.

python-programs

python-scripts

research-internships-for-ug-students

A comprehensive list of research opportunities for undergraduate students

retail-analysis-with-walmart-data-science-project

sales-performance-analysis---tableau-project