Displaying 1 to 12 from 12 results

Surface-Defect-Detection - 📈 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance

  •    Python

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. Compared with the clear classification, detection and segmentation tasks in computer vision, the requirements for defect detection are very general. In fact, its requirements can be divided into three different levels: "what is the defect" (classification), "where is the defect" (positioning) And "How many defects are" (split).




Mirror-Glass-Detection - 🍸 Mirror & Glass Detection in Real-world Scenes

  •    Python

In this project, we are developing techniques for mirror and glass detection/segmentation. While a mirror is a reflective surface that reflects the scene in front of it, glass is a transparent surface that transmits the scene from the back side and often also reflects the scene in front of it too. In general, both mirrors and glass do not have their own visual appearances. They only reflect/transmit the appearances of their surroundings. As mirrors and glass do not have their own appearances, it is not straightforward to develop automatic algorithms to detect and segment them. However, as they appear everywhere in our daily life, it can be problematic if we are not able to detect them reliably. For example, a vision-based depth sensor may falsely estimate the depth of a piece of mirror/glass as the depth of the objects inside it, a robot may not be aware of the presence of a mirror/glass wall, and a drone may collide into a high rise (noted that most high rises are covered by glass these days).

PyStegosploit - PoC - Exploit Delivery via Steganography and Polyglots, CVE-2014-0282

  •    HTML

Stegosploit creates a new way to encode "drive-by" browser exploits and deliver them through image files. These payloads are undetectable using current means. This paper discusses two broad underlying techniques used for image based exploit delivery - Steganography and Polyglots. Drive-by browser exploits are steganographically encoded into JPG and PNG images. The resultant image file is fused with HTML and Javascript decoder code, turning it into an HTML+Image polyglot. The polyglot looks and feels like an image, but is decoded and triggered in a victim's browser when loaded. Stegosploit comprises of tools that let a user analyse images, steganographically encode exploit data onto JPG and PNG files, and turn the encoded images into polyglot files that can be rendered as HTML or executed as Javascript.


SNE-RoadSeg2 - 🌱 SNE-RoadSeg in PyTorch, ECCV 2020 by Rui (Ranger) Fan & Hengli Wang, but now we have improved it

  •    Python

This SNE-RoadSeg2 is based on the official pytorch implementation of SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection, accepted by ECCV 2020. This is their project page. In this repo, we provide the training and testing setup for the KITTI Road Dataset. We test our code in Python 3.7, CUDA 10.0, cuDNN 7 and PyTorch 1.1. We provide Dockerfile to build the docker image we use.

VOGUE-Try-On - Personal repository for "VOGUE: Try-On by StyleGAN Interpolation Optimization" (CVPR 2021)

  •    HTML

Figure 1: VOGUE is a StyleGAN interpolation optimization algorithm for photo-realistic try-on. Top: shirt try-on automatically synthesized by our method in two different examples. Bottom: pants try-on synthesized by our method. Note how our method preserves the identity of the person while allowing high detail garment try on. Given an image of a target person and an image of another person wearing a garment, we automatically generate the target person in the given garment. At the core of our method is a pose-conditioned StyleGAN2 latent space interpolation, which seamlessly combines the areas of interest from each image, i.e., body shape, hair, and skin color are derived from the target person, while the garment with its folds, material properties, and shape comes from the garment image. By automatically optimizing for interpolation coefficients per layer in the latent space, we can perform a seamless, yet true to source, merging of the garment and target person. Our algorithm allows for garments to deform according to the given body shape, while preserving pattern and material details. Experiments demonstrate state-of-theart photo-realistic results at high resolution (512 x 512).






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