A shape-embedded dynamic time warping (DTW) algorithm. To the best of our knowledge, shapeDTW beats all other DTW variants on UCR time series datasets.

[ACM Multimedia 2024] Shape-Guided Clothing Warping for Virtual Try-On

Contains tools for generalized Procrustes analysis, active shape models and shape-based image warping

Shape-based clustering of time series using dynamic time warping

Unbounded Shape Dynamic Time Warping

Fit an image to an arbitrary target shape using unstructured interpolation

This repository accompanies “Shape-Based Clustering of Daily Weigh-In Trajectories using Dynamic Time Warping”. The aim of the paper was to use Dynamic Time Warping, a shape-based clustering method, to cluster binary trajectories and evaluate patterns.

Registration aims to decompose amplitude and phase variation of samples of curves. Phase variation is captured by warping functions which monotonically trans- form the domains. Resulting registered curves should then only exhibit amplitude variation. Most existing methods assume that all sample functions exhibit a typical sequence of shape features like peaks or valleys, and registration focuses on aligning these features. In this paper we adopt a more general point of view which goes be- yond feature alignment. We present a registration method where warping functions are defined in such a way that the resulting registered curves span a low dimensional linear function space. The approach may be used as a tool for analyzing any type of functional data satisfying a structural regularity condition called “bounded shape variation”. Problems of identifiability are discussed in detail, and connections to es- tablished registration procedures are analyzed. The method is applied to real and simulated data.

Shape matching using a Dynamic Time Warping (DTW) algorithm

OpenCV Basics: A project covering image and video processing essentials. Includes capturing images/videos, webcam integration, color splitting/detection/change, image blurring, edge detection, resizing, cropping, adding text, drawing shapes, perspective warping, JPEG handling, shape, and face detection.

For training and using shape warping models for 3D shape reconstruction and learning

Part Based Shape Warping webpage

warping shapes for pen plotting

No description available

fabric.js library for warping text into custom shapes

Particle system for shape analysis using mean space warping with Thin Plate Spline or RBK Kernel Warping

Text warping/shaping tool built with fabric.js - inspired by CustomInk

GLSL shader reference library — shaping functions, noise, FBM, domain warping, SDFs, ray marching, and Shadertoy templates

The implementation of Bijak, R: „The lateral buckling of simply supported unrestrained symetric I-shape beams with free warping”

Completed a 70-hour practical course in Computer Vision using Python and OpenCV. Learned image processing, HSV filtering, shape and face detection, perspective warping, and real-time video analysis. Built 3 projects: Virtual Painter, Document Scanner, and License Plate Detector.

Completed a 70-hour practical course in Computer Vision using Python and OpenCV. Learned image processing, HSV filtering, shape and face detection, perspective warping, and real-time video analysis. Built 3 projects: Virtual Painter, Document Scanner, and License Plate Detector.

A Python tool that warps patterns onto flags with realistic folds. Users mark points to define shape, and the tool applies perspective warping, gradient-based displacement, and Lambertian shading for 3D realism. Runs as a script or in a Docker container for easy deployment.

A Python tool that warps patterns onto flags with realistic folds. Users mark points to define shape, and the tool applies perspective warping, gradient-based displacement, and Lambertian shading for 3D realism. Runs as a script or in a Docker container for easy deployment.

Logo identification is more challenging than object or logo recognition owing to the lack of original data. Businesses often use logos to identify themselves and their goods. Shapes, colours, text, and textures are often used. Logo recognition is important for several applications, including online brand management, copyright infringement, context-specific advertising placement, and vehicle recognition. Even while companies do not need to alter their logos often, the context in which they appear varies for each product of the same company. Changing backdrops, perspective distortions, warping, occlusions, colours, and sizes are some of the challenges with precise logo recognition. The growing number of goods (brands) with customised logos further complicates logo recognition. Logo recognition requires significant processing power to enable multi-class classification. The uses of vehicle logo recognition are many. The control unit system at military camps, government buildings, at crossroads and traffic signals, at checkpoints across the city and surrounding regions, and far beyond the nation's territory, are sensitive places. Humans can easily identify and distinguish logos in everyday settings. However, in densely populated cities with high vehicle and human densities, data breaches are common. To recognise and identify car logos, automated methods are needed. This project's main aim is to utilise deep learning models to ensure logo recognition without needing a lot of computing resources

Logo identification is more challenging than object or logo recognition owing to the lack of original data. Businesses often use logos to identify themselves and their goods. Shapes, colours, text, and textures are often used. Logo recognition is important for several applications, including online brand management, copyright infringement, context-specific advertising placement, and vehicle recognition. Even while companies do not need to alter their logos often, the context in which they appear varies for each product of the same company. Changing backdrops, perspective distortions, warping, occlusions, colours, and sizes are some of the challenges with precise logo recognition. The growing number of goods (brands) with customised logos further complicates logo recognition. Logo recognition requires significant processing power to enable multi-class classification. The uses of vehicle logo recognition are many. The control unit system at military camps, government buildings, at crossroads and traffic signals, at checkpoints across the city and surrounding regions, and far beyond the nation's territory, are sensitive places. Humans can easily identify and distinguish logos in everyday settings. However, in densely populated cities with high vehicle and human densities, data breaches are common. To recognise and identify car logos, automated methods are needed. This project's main aim is to utilise deep learning models to ensure logo recognition without needing a lot of computing resources