Rhonda Software: Computer Vision Computer Vision is relatively new but most rapidly growing domain of Rhonda's expertise. Since 2007 Rhonda has been doing research and development in this area. As the mainstream of CV R&D, Rhonda is planning to release two Audience Measurement products. Rhonda also offers CV custom-oriented solutions in other domains like barcodes, tools and pattern recognition. Rhonda leverages modern CV-methods and mathematical approaches: KDE, Mean shift or Running Gaussian Average methods to extract objects from background (depending on background scene).Color based histograms and Mean Shift for object detection and trackingViola and Jones method for faces detectionHidden Markov models and Neural Networks for faces recognitionRTP (MPEG4) or MJPEG over HTTP for streaming meta-data and video. Note: Rhonda does not distribute its CV solutions in form of library or SDK.
RESEARCH APPLICATIONS VENDORS 121VIEW Digital Signage Media Who We Are 121View is a digital media software development company. We provide two way digital signage media networks that interact with customers in the marketplace. We target high traffic areas, customer point of purchase decision and information transfer locations such as shopping malls, mass transit systems, hospitals, retail, education, government and entertainment venues. view details What We Do We are a software development and digital media managment company. Our Vision – A digital media network for every business. Our Mission – To engineer and manage digital media software solutions that enable businesses and customers to communicate with each other. Value Proposition – Providing dedicated web based media networks that are user friendly, cost effective and provide measured results. Join Us – In a world where media delivery and communications are migrating from print to digital delivery we believe every company needs its own digital media network.
Quividi - Automated Audience Measurement of Billboards and Out Of Home Digital Media - OOH - Home TECHNOLOGY How Face Detection Works OpenCV's face detector uses a method that Paul Viola and Michael Jones published in 2001. Usually called simply the Viola-Jones method, or even just Viola-Jones, this approach to detecting objects in images combines four key concepts: Simple rectangular features, called Haar features An Integral Image for rapid feature detection The AdaBoost machine-learning method A cascaded classifier to combine many features efficiently The features that Viola and Jones used are based on Haar wavelets. The actual rectangle combinations used for visual object detection are not true Haar wavlets. The presence of a Haar feature is determined by subtracting the average dark-region pixel value from the average light-region pixel value. To determine the presence or absence of hundreds of Haar features at every image location and at several scales efficiently, Viola and Jones used a technique called an Integral Image.
Face Recognition with OpenCV — OpenCV v2.4.2 documentation Introduction OpenCV (Open Source Computer Vision) is a popular computer vision library started by Intel in 1999. The cross-platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. In 2008 Willow Garage took over support and OpenCV 2.3.1 now comes with a programming interface to C, C++, Python and Android. OpenCV is released under a BSD license so it is used in academic projects and commercial products alike. OpenCV 2.4 now comes with the very new FaceRecognizer class for face recognition, so you can start experimenting with face recognition right away. The currently available algorithms are: You don’t need to copy and paste the source code examples from this page, because they are available in the src folder coming with this documentation. All code in this document is released under the BSD license, so feel free to use it for your projects. Face Recognition Face recognition is an easy task for humans. .
Alexandre Alahi - FREAK - What is the problem? A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: SIFT, SURF, and more recently BRISK to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: we need to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. - What is our solution? We propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). - Why is our solution proposed? Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. Software: Download: FREAK C/C++ source License: BSD Dependency: OpenCV Related publication: A. Some examples:
Accenture Innovation Awards Concept van de Week – ThirdSight: persoonlijke reclame Het klinkt onwerkelijk, maar het kan: reclameboodschappen automatisch afstemmen op interesses en emoties. En wel met de software van AIA-deelnemer ThirdSight. ThirdSight is een samenwerkingsverband van medewerkers van de Universiteit van Amsterdam (UvA) en mediagroep BlueBubbleLab onder leiding van Ben van Dongen. Ben is ook CEO van ThirdSight. ThirdSight is ervan overtuigd dat de producten de potentie bezitten om marketing als vakgebied te transformeren, en elke boodschap voor een consument relevant te maken. Camera’s herkennen mensen Ben: “Onze technologie verschaft toegang tot een bron van informatie over persoonskenmerken, emotie en gedrag. Tot nu toe ongekende informatie Het betekent dat ThirdSight elke boodschap op elk digitaal medium voor elke consument interessant kan maken. Effect van reclame meten Zo is het softwarepakket EmoVision gericht op individuele analyse. Wat wil ThirdSight bereiken? Accenture organiseert in 2012 de Innovation Awards in vijf verschillende industrieën.