Advantech SOM-6820 COM Express module is powered by Qualcomm Snapdragon X Elite Arm SoC

Advantech SOM-6820 is a COM Express Type 6 Compact Computer-on-Module powered by a Qualcomm Snapdragon X Elite SoC with up to twelve 64-bit Arm Oryon cores, instead of an x86 processor from Intel or AMD, more commonly found on COM Express modules. The COM also features up to 64GB LPDDR5 memory, two MIPI CSI camera connectors, an RTL8153B USB 3.0 Gigabit Ethernet controller, and a TPM 2.0 security chip. All I/Os are exposed through two standard 220-pin B2B connectors, including DisplayPort and LVDS/eDP interfaces for up to four 4K displays, up to four SATA III for storage, twelve USB 3.0/2.0 interfaces, multiple PCIe Gen4/3 interfaces, and more. With up to 45 TOPS of AI performance, the SOM-6820 is especially well-suited to medical imaging and machine vision applications as well as mission-critical systems and humanoid robots. Advancec SOM-6820 specifications: Snapdragon X Elite SoC variants (one or the other) X1E-00-1DE 12-core up

IEEE Spectrum: TraffickCam Uses Computer Vision to Counter Human Trafficking | ResearchBuzz: Firehose

Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with Bézier Curves

While Vision-language Models (VLMs) have demonstrated strong semantic capabilities, their ability to interpret the underlying geometric structure of visual information is less explored. Pictographic characters, which combine visual form with symbolic structure, provide an ideal test case for this capability. We formulate this visual recognition challenge in the mathematical domain, where each character is represented by an executable program of geometric primitives. This is framed as a program synthesis task, training a VLM to decompile raster images into programs composed of Bézier curves. Our model, acting as a "visual decompiler", demonstrates performance superior to strong zero-shot baselines, including GPT-4o. The most significant finding is that when trained solely on modern Chinese characters, the model is able to reconstruct ancient Oracle Bone Script in a zero-shot context. This generalization provides strong evidence that the model acquires an abstract and transferable geometric grammar, moving beyond pixel-level pattern recognition to a more structured form of visual understanding.

Nature: Fair human-centric image dataset for ethical AI benchmarking | ResearchBuzz: Firehose

Are Foundation Models Ready for Industrial Defect Recognition? A Reality Check on Real-World Data

Foundation Models (FMs) have shown impressive performance on various text and image processing tasks. They can generalize across domains and datasets in a zero-shot setting. This could make them suitable for automated quality inspection during series manufacturing, where various types of images are being evaluated for many different products. Replacing tedious labeling tasks with a simple text prompt to describe anomalies and utilizing the same models across many products would save significant efforts during model setup and implementation. This is a strong advantage over supervised Artificial Intelligence (AI) models, which are trained for individual applications and require labeled training data. We test multiple recent FMs on both custom real-world industrial image data and public image data. We show that all of those models fail on our real-world data, while the very same models perform well on public benchmark datasets.

General Intuition lands $134M seed to teach agents spatial reasoning using video game clips | TechCrunch

Late last year, OpenAI reportedly tried to buy Medal and its vast trove of video game data for $500M. Today, the company spun out a frontier research lab that's using that data to build AI agents that understand how they move through space and time, a concept called spatial-temporal reasoning.

Sistemas de Visão Industrial - Fundamentos Básicos

Fundamentos Básicos para a Visão Computacional na Indústria.