Imagine a robot rolling through a building, a car driving through city streets, or a drone flying over a campus. Hours later, it reaches a familiar-looking spot and silently asks a crucial question: “Have I been here before?” This deceptively simple question is at the heart of Visual Place Recognition (VPR). Visual Place Recognition is…
Counting overlapping or touching objects in images is a common challenge in computer vision. Simple thresholding and contour detection often fail when objects are in contact, treating multiple items as a single blob. The Watershed algorithm provides a solution to this problem by treating the image as a topographic surface and “flooding” it to separate…
Imagine capturing the perfect landscape photo on a sunny day, only to find harsh shadows obscuring key details and distorting colors. Similarly, in computer vision projects, shadows can interfere with object detection algorithms, leading to inaccurate results. Shadows are a common nuisance in image processing, introducing uneven illumination that compromises both aesthetic quality and functional…
Imagine uploading an image of a document into your browser and watching it automatically detect page boundaries, correct perspective distortion, extract searchable text, and generate a clean, professional PDF, all without transmitting a single byte to a remote server. This isn’t science fiction; it’s the result of modern, high-performance web technologies running entirely on the…
If you’ve ever used OpenCV to process live video from webcams, IP cameras, or recorded streams, you know the pattern: a loop pulling frames and a growing chain of image-processing calls. It works, but it often feels like assembling IKEA furniture without the right tools, doable, yet increasingly inefficient as complexity grows. What if you…
EgoX introduces a novel framework for translating third-person (exocentric) videos into realistic first-person (egocentric) videos using only a single input video. The work tackles a highly challenging problem of extreme viewpoint transformation with minimal view overlap, leveraging pretrained video diffusion models and explicit geometric reasoning to generate coherent, high-fidelity egocentric videos. Key Highlights Single Exocentric…
Have you ever captured amazing underwater footage, only to discover that your photos were plagued by poor visibility, muted colours, and a bluish-green haze? You’re not by yourself. As depth increases, warmer colours such as red, orange, and yellow are absorbed first, leaving images looking dull and low in contrast. In this post, we are…
Omni-Attribute introduces a new paradigm for fine-grained visual concept personalization, solving a long-standing problem in image generation: how to transfer only the desired attribute (identity, hairstyle, lighting, style, etc.) without leaking irrelevant visual details. Developed by researchers from Snap Inc., UC Merced, and CMU, this work proposes the first open-vocabulary image attribute encoder explicitly designed…
We capture the world with cameras that compress depth, texture, and geometry into flat pixel grids, yet our minds effortlessly reconstruct the 3D structure behind them. What if computers could do the same? Structure-from-Motion (SfM) is the technique that enables this. By analyzing how features shift across multiple images, SfM simultaneously recovers the camera motion…
GeoVista introduces a new frontier in multimodal reasoning by enabling agentic geolocalization, a dynamic process where a model inspects high-resolution images, zooms into regions of interest, retrieves web information in real time, and iteratively reasons toward pinpointing a location. Developed by researchers from Fudan University, Tencent Hunyuan, Tsinghua University, and Shanghai Innovation Institute, GeoVista addresses the long-standing…