Previousworkseitheruse zeroth-order optimization, which is slow when the set of parameters increases, or rely on a differentiable proxy of the target finishing pipeline, which is hard to train.

Small- and medium-sized manufacturers need innovative data tools but, because of competition and privacy concerns, often do not want to share their proprietary data with researchers who might be interested in helping. This paper introduces a privacy-preserving platform by which manufacturers may safely share their data with researchers through secure methods, so that those researchers then create innovative tools to solve the manufacturers' real-world problems, and then provide tools that execute solutions back onto the platform for others to use with privacy and confidentiality guarantees. We illustrate this problem through a particular use case which addresses an important problem in the large-scale manufacturing of food crystals, which is that quality control relies on image analysis tools. Previous to our research, food crystals in the images were manually counted, which required substantial and time-consuming human efforts, but we have developed and deployed a crystal analysis tool which makes this process both more rapid and accurate. The tool enables automatic characterization of the crystal size distribution and numbers from microscope images while the natural imperfections from the sample preparation are automatically removed; a machine learning model to count high resolution translucent crystals and agglomeration of crystals was also developed to aid in these efforts. The resulting algorithm was then packaged for real-world use on the factory floor via a web-based app secured through the originating privacy-preserving platform, allowing manufacturers to use it while keeping their proprietary data secure. After demonstrating this full process, future directions are also explored.

CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors. Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement.

Imaging under extremely low-light conditions presents a significant challenge and is an ill-posed problem due to the low signal-to-noise ratio (SNR) caused by minimal photon capture. Previously, diffusion models have been used for multiple kinds of generative tasks and image-to-image tasks, however, these models work as a post-processing step. These diffusion models are trained on processed images and learn on processed images. However, such approaches are often not well-suited for extremely low-light tasks. Unlike the task of low-light image enhancement or image-to-image enhancement, we tackle the task of learning the entire image-processing pipeline, from the RAW image to a processed image. For this task, a traditional image processing pipeline often consists of multiple specialized parts that are overly reliant on the downstream tasks. Unlike these, we develop a new generative ISP that relies on fine-tuning latent diffusion models on RAW images and generating processed long-exposure images which allows for the apt use of the priors from large text-to-image generation models. We evaluate our approach on popular end-to-end low-light datasets for which we see promising results and set a new SoTA on the See-in-Dark (SID) dataset. Furthermore, with this work, we hope to pave the way for more generative and diffusion-based image processing and other problems on RAW data.

In our previous tutorial, you learned how to improve the accuracy of Tesseract OCR by supplying the appropriate page segmentation mode (PSM). The PSM allows you to select a segmentation method dependent on your particular image and the environment in which it was captured. However, there are times when changing the PSM is not sufficient, and you instead need to use a bit of computer vision and image processing to clean up the image before you pass it through the Tesseract OCR engine. To learn how to improve OCR results using basic image processing, just keep reading. Exactly which image processing algorithms or techniques you utilize is heavily dependent on your exact situation, project requirements, and input images; however, with that said, it's still important to gain experience applying image processing to clean up images before OCR'ing them.

To learn how to OCR a passport using OpenCV and Tesseract, just keep reading. So far in this course, we've relied on the Tesseract OCR engine to detect the text in an input image. However, as we discovered in a previous tutorial, sometimes Tesseract needs a bit of help before we can actually OCR the text. This tutorial will explore this idea more, demonstrating that computer vision and image processing techniques can localize text regions in a complex input image. Once the text is localized, we can extract the text ROI from the input image and then OCR it using Tesseract.

Next, we define thelist_images function (line 22–34) which will help us to walk through the input directory structure to grab the image paths. For face detection, we use the Viola-Jones algorithm known as Haar cascade (line 40). It was published in 2001 by Paul Viola and Michael Jones. You can learn more about the algorithm from their original paper Rapid Object Detection using a Boosted Cascade of Simple Features or Wikipedia page. It is quite an old algorithm in the era of deep learning and false-positives prone (reporting a face in the place of the image where no face is present). Even though it is quite easy to use for our demonstrative case. The main processing loop goes as follow: we walk through image files (line 49), read them (line 51) one by one, detect the faces (line 55), save them to the prepared directory (line 59–72) and saving a summary report (line 78–82) with faces coordinates.

To intelligently connect many pieces of legacy infrastructure to the internet, you'll need to do it in such a way insights can be gleaned from the all data it generated. The premise sounds simple enough, but in practice the project would require deep knowledge of a wide range of technologies. By breaking the problem down into discrete, logical pieces, we were able to prove that a working solution was possible. Take for example, an application involving an IoT device, computer vision, and machine learning. The first step required is determining the best approach to retrofitting the legacy infrastructure.

Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.