Artificial Intelligence in Fashion, Beauty and related Creative industries
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The document discusses the increasing integration of artificial intelligence (AI) in creative industries, particularly fashion, where AI is used for tasks ranging from design assistance to personalized customer experiences. Innovations like Project Muze and fashion-focused AI tools are transforming how trends are analyzed and products are developed, while robots are being used to improve garment manufacturing efficiency in the US. The document also highlights the need for collaboration between designers and AI technologies to enhance creativity rather than replace human roles.
![Photography Automatic curation
Posted on February 29, 2016 by Appu Shaji
3 Comments Tagged cuDNN, Deep Learning,
Machine Learning, photography, Theano,
Torch
EyeEm is a community and marketplace for passionate photographers. More than 15 million
photographers use EyeEm to share their photos, connect with other photographers,
improve their skills through masterclasses, get recognition through our photography missions and
exhibitions, andearn money by licensing their photos. The following video shows the impact of our
deep-learning-based automatic aesthetic curation on the EyeEm search experience—read on
to learn more about how it is done.
https://vimeo.com/154364175#at=55
At EyeEm we develop technology that helps photographers tell their stories and get
discovered. We believe there are two ingredients that contribute to the success of a
photograph: 1) the story behind the photograph, and 2) the way that story is told.
Automatic image tagging technologies from companies like EyeEm,
Google Cloud Vision, Flickr, and Clarifai are quickly approaching maturity and
helping to tell the stories behind photographs by indexing or tagging them to make
them discoverable.
Visual aesthetics addresses the way each story is told; specifically, how the visual
style and composition create an emotional connection with the viewer by using
structure and cues that draw attention toward (and away) from the constituent story
elements of the photographer’s choice.
Our objective is to compare images and learn the commonalities between well-
crafted photographs, and the differences between well-crafted and mediocre
photos. A loss function that can express this is as
an extension of the hinge loss function, Researchers have explored such loss functions
in image similarity, metric learning and face identification settings with great
success in the past (Chechik2010 , Norouzi2011, Schroff2015). In a larger context,
such energy functions fall into the class of implicit regression (LeCun06), where the
loss function penalizes the constraint that input variables must satisfy.
As a photography-first company, we validate
our assumptions with our internal curators
and reviewers, who spend the major part of
their working day curating photographs. We
track the time they spend on curation tasks
where the image list is prioritized via the
aesthetic rank vs. the default sort order.
Using deep learning, we have been able to
reduce curation time by 80%.
[Chechik 2010] G. Chechik, V. Sharma, U. Shalit, S. Bengio, Large Scale Online Learnings of Image Similarity Through
Ranking, Journal of Machine Learing Research 11, 2010
[Norouzi 2011] M. Norouzi, D. Fleet, Minimal Loss Hashing for Compact Binary Codes, International Conference in
Machine Learning (ICML), 2011.
[Schroff 2015] F. Schroff, D. Kalenichenko, J. Philbin , FaceNet: A Unified Embedding for Face Recognition and
Clustering, ArXiV : 1503.03832, 2015
[LeCun 2006] Y. LeCun, S. Chopra, R. Hadsell, M. Ranazato, F. J. Huang, A Tutorial on Energy-Based Learning,
Predicting Structured Data, 2006](https://image.slidesharecdn.com/creativeai-161022030518/85/Artificial-Intelligence-in-Fashion-Beauty-and-related-Creative-industries-19-320.jpg)
Artificial Intelligence in Fashion, Beauty and related Creative industries
- 3. Artificial Intelligence in Creative Industries Artist and researcher Terence Broad is working on his master's at Goldsmith's computing department; his dissertation involved training neural networks to "autoencode" movies they've been fed. boingboing.net/2016/06/02 Jürgen Schmidhuber, Point Omega, https://youtu.be/KQ35zNlyG-o http://dx.doi.org/10.1016/S0004-3702(98)00055-1 For an excellent review of the current state, see the post by Kyle McDonald who is an artist working with code: medium.com/@kcimc/a-return-to-machine-learning Unsupervised Representation Lea rning with Deep Convolutional G enerative Adversarial Networks Semantic Style Transfer and T urning Two-Bit Doodles into F ine Artworks Deep Dream FBO Glitch By KyleMcDonald , also posted to Twitter. London, United Kingdom, Sept 21 2016 Over the past few months, there has been increasing interest in applying the latest developments in artificial intelligence to creative projects in art, music, film, theatre and beyond. techinsider.io, Magenta group introduced at Moogfest https://vimeo.com/169779284
- 4. Fashion & Artificial Intelligence If artificial intelligence has its way, discounting could disappear, thanks to software that tells retailers exactly what and how many products to buy, and when to put them on sale to sell them at full price. Online shopping could become a conversation, where the shopper describes the dress of their dreams, and, in seconds, an AI-powered search engine tracks down the closest match. Designers, merchandisers and buyers could all work alongside AI, to predict what customers want to wear, before they even know themselves. For fashion, some of the biggest opportunities are in aligning supply and demand, scaling personal customer service, and assisting designers. By analysing large amounts of data — say, the browsing and shopping history of every single one of a fashion brand’s online customers, as well as those of its competitors — AI can tell a retailer how to align product drops to match demand, and even how to display products in a store to sell as many as possible. Machine learning can also enable brands to finely personalise their offerings to each market, or even, each individual customer. IBM's Watson — which is working with over 500 partners in industries including retail — has partnered with The North Face to offer “guided shopping” online. The AI asks shoppers questions on factors such as gender, time of year and technical product details, to deliver tailored recommendations. "There are AI systems today that compose music, write stories, and create artwork that no one can tell is machine-generated. So fashion design is surely not beyond AI's capabilities,” In the same way that the work of architects like Frank Gehry and Zaha Hadid relies on computer modelling, “Fashion designers armed with AIs will be similarly able to come up with radical new ideas: AI will amplify their creativity rather than replace it," reasons Domingos. “AI will absolutely challenge and replace designers,” counters Kenneth Cukier. “Let's get real — lots of design is trial and error or boring, repetitive work. AI can help with both by making more accurate predictions of what designs will work and taking over some of the repetitive tasks.” Others agree that, for the moment, partnering with third party AI specialists is the way forward. “The smartest thing a business can do, is partner with a fashion-focused tech company with AI at its core,” says Geoff Watts of Edited. “Building AI teams from scratch, or acquiring AI start-ups and retrofitting them to have a retail focus, requires a substantial investment of time and money.” businessoffashion.com/articles/fashion-tech www.technologyreview.com Jonathan Zornow, the sole employee of a new startup called Sewbo, thinks the U.S. could bring garment manufacturing a little closer to home by automating the feeding of fabric into sewing machines—a step that to this day is done by hand. Zornow has created a process by which a robotic arm guides chemically stiffened pieces of fabric through a commercial sewing machine. Apparel companies often move their manufacturing to countries where wages are lower in a perpetual quest to cut costs. The Center for American Progress found that in 2011, 15 of the top apparel exporters to the U.S. paid their Chinese garment workers an average monthly real wage of $324.90. Bangladeshi workers earned just $91.45. Meanwhile, U.S. sewing machine operators earn an average monthly wage of $1,922, according to the Bureau of Labor Statistics. Part of its selling point is that Sewbo users can design and begin mass producing a new garment style in a day, as opposed to the months it typically takes to manufacture and ship a new garment design. Such a feat would certainly bring new meaning to the term “fast fashion.”
- 5. Fashion design Deep learning for design Created in partnership with U.K.-based digital design studio Stinkdigital, Project Muze constitutes a specially built engine that uses a neural network trained with the design preferences (color, texture, and style) of more than 600 fashion “trendsetters” and features data from the Google Fashion Trend Report, in addition to styles that have trended on Zalando itself. http://www.stinkdigital.com/work/zalando-project-muze venturebeat.com/2016/09/02 We started Project Creaite last winter to see the capabilities of deep learning and computer vision algorithms in creative work such as fashion design and product design. The first version was focused on creating product pictures for items from fashion vertical such as apparel and accessories. We finally got some time to write about it and share what we did through a blog post here. As we all know, Artificial Intelligence is finally here in its narrow form and ready to be helpful to solve specific problems for which we have readily available data. At Artifacia Research we have spent some time studying in detail about generative models. In phase one of Project Creaite, we used an encoder-decoder scheme to get promising results and built a prototype a few months ago that can come up with new designs for fashion products after being fed with enough number of examples. We at Artifacia Research believe that the further development of this technology can help bring a lot of efficiency in fashion design in particular and product design in general. We are really excited by the possibilities of Deep Learning and AI and so we would be investing our time to take this project to the next level very soon. research.artifacia.com Example results from our generative network“Using data from so many different sources in such a complex algorithm is not going to produce something exact, intricate, or least of all, conforming to design norms. As you can see in the pictures attached below, the machine tends to spit out some seriously strange designs, but with an element of cool and human to them because of their inspirations and origins. An option is presented to answer more questions and further customize a given design, as well; this option matches the user up with a whole new pattern, style, or shape, and shows how that trait would apply to the outfit on show. While some of these are downright impractical and most of them would get you more than a few strange looks in public, the fact that a machine is capable of this sort of thing at all speaks volumes about the hard work that many talented people have put into the field of machine learning down the years.” - androidheadlines.com
- 6. Fashion manufacturing Local customized high fashion? ROBOTIC MANUFACTURING Locally near the customer (let it be Tokyo, London or New York) Make It luxurious and unique No need for child labor in Asia, and only raw textiles need to be shipped REFERENCES Scanning instead of trying-on: Custom-made clothes with 3D Laser Scanning (LMS400 from SICK) Explore Cornell - The 3D Body Scanner - Made-to-Measure Movie: 3D scanning used to create unique fitted clothes - Dezeen The DittoForm – A 3D Body Scan Dressform « bits of thread sewing studio Volumental and Their 3D Scanning Technology is Bringing Custom Fit Shoes to the US
- 7. Fashion shopping Shopping Assistants #1 https://medium.com/machine-intelligence-report/robo-bill-cunningham-shazam-for-fashion-with-deep-neural-networks-7126ea39197b#.k8bdzzxmb Artificial Intelligence | Fashion | Deep Learning
- 8. Fashion shopping Shopping Assistants #2 https://www.visii.com/ https://engineering.pinterest.com/blog/ introducing-new-way-visually-search-pin terest https://www.cortexica.com/ https://www.quora.com/How-can-you-cluster-similar-clothing-brands-with-machine-learning
- 9. Fashion shopping Shopping Assistants #3 http://www.racked.com/2014/3/19/ https://indico.io/blog/fashion-matching-tutorial/ Deep Learning Opportunities in Fashion Deep Learning is the buzz word in Artificial Intelligence these days. But what is it all about? www.picalike.com/blog/2014/02/20 http://multithreaded.stitchfix.com/blog/2015/09/17/deep-style/ https://arxiv.org/abs/1609.07859 http://dx.doi.org/10.1007/978-3-319-10590-1_31 Cited by 40
- 10. Fashion stylist Augmenting the human? … me sharing not only obvious information like my size, desired price range and “daringness” (with “daring” defined as wearing floral shirts or shorts with blazers), but also helping her work out my actual style preferences by telling her brands I like and flicking through endless pictures of well-dressed men to highlight the looks I want. This is no AI horror story, though. My stylist Sophie Bailey-Hine is very real, and her and her colleagues at Thread, a British startup that was founded in 2012, are currently helping 480,000 men find a new image, dress well, or simply sort out their clothes shopping. “All through, my goal with this business is not really to build a niche quirky retail thing. The majority of men want to dress decently, and don’t particularly love shopping that much. So if you can find a way to spend way less time on it, with a much much better result, I think it’s what the majority of men would use. And so I see this as building a new default for how the majority of men buy clothes.” The startup won a position in the prestigious Y Combinator accelerator in San Francisco, a sort of three-month boot-camp for startups, which led to one of its first paying customers being Instagram CEO Kevin Systrom. But after graduating, it moved from San Francisco back to London, and after a stint in trendy Shoreditch is now further east in a decidedly less cool part of Whitechapel. From day one, the stylists were working as much to teach the algorithm how to aid them as they were to get the best clothes to customers. …Now, the algorithm works better, letting a stylist lay out the skeleton of an outfit while filling in the specifics and targeting who actually receives it as part of their picks. It also allows for the stylist’s own taste to filter through. “I’m obsessed with simple Scandinavian clothes,” Bailey-Hine tells me, “while my fellow stylist Sam is big into street style and Freddie is our very own suiting expert – everyone has their own thing.” In a world where fears of robots “taking jobs” is rife, Thread offers some hope. Yes, eight stylists are doing the jobs of hundreds or thousands in a pre-AI age; but those eight are working with AI, not for it. And besides, Sophie says, “one of the best things I can do as a stylist is to look at photos of a guy and within a few seconds think, ‘OK, he’d look better if he was wearing a slimmer jean, he could make that navy suit look more interesting with a knit tie, and he’d look really good in a light Private White V.C jacket for a sharper take on his utilitarian vibe.’https://www.thread.com/ theguardian.com/technology/2016/jul/19/
- 11. Makeup 3D print your pigments and makeup Picture this: you snap a photo of a tropical purple flower and in less than two minutes you can print a lipstick in that exact color. And you’ll do it at home with Mink, your personal 3D printer. https://makeuphacker.myshopify.com/ forbes.com https://www.foreo.com/institute/moda/ While waiting for Mink, women may have yet another incredibly innovate option when it comes to makeup, and yes it also uses 3D printing technology. Stockholm, Sweden- based skincare company, Foreo, has just unveiled MODA, ‘the world’s first digital makeup artist’. Digital makeup artistry
- 12. Makeup Algorithmic analysis and generation https://arxiv.org/abs/1604.07102 Our system has two functions. I: recommend the most suitable makeup for each before- makeup face II: transfer the foundation, eye shadow and lip gloss from the reference to the before-makeup face. The lightness of the makeup can be tuned. Pay special attention to eye shadow, lip gloss and foundation transfer. Publication number: WO2015127394 A1 Publication type: Application Application number: PCT/US2015/017155 Publication date: Aug 27, 2015 Filing date: Feb 23, 2015 Priority date: Feb 23, 2014 Inventors: Yun Fu, Shuyang Wang Applicant: Northeastern University Export Citation: BiBTeX, EndNote, RefMan Patent Citations (5), Non-Patent Citations (3), Classifications (7),Legal Events (2) External Links: Patentscope, Espacenet https://www.google.com/patents/WO2015127394A1?cl=en
- 13. Physical Product Design Parametric AI augmentation http://www.getlittlebird.com/blog/3d_printing_plus_artificial_intelligence Part of a 30 day series on the intersection of AI, and a wide variety of other exponential technologies as explored by cross- over influencers and experts.3D printing has the potential to massively democratize access to manufacturing - but what if it went even further than puting fabrication into the hands of all people? Imagine 3D printing in the hands of machine intelligence. Artificial intelligence plus 3D printing could yield some really transformative experiences. Who’s paying the most attention to that intersection? architectmagazine.com Daedalus Pavilion is Ai Build's latest construction, 3D printed in 3 weeks by industrial robots, using the latest technology in artificial intelligence, deep learning, computer vision and robotics. Ai Build teamed up with partners NVIDIA, Arup, KUKA Robotics and Formfutura to create Daedalus Pavilion as part of the GPU Technology Conference in Amsterdam. pinterest.com #next_top_architects #roboticfabrication #newbaby #fibrousthread #arduino nexttoparchitects.orgdezeen.com/2015/08/26 Designer and researcher Neri Oxman and her Mediated Matter group atMIT Media Lab PARAMETRIC 'PORN' Iris Van Herpen, Neri Oxman, Ana Rajcevic Studio
- 14. Jewelry Design Easy target for AI optimization Ontic Design developed 3D-printed silver and gold bracelets that consumers can design for themselves. You can go onto the website, choose the design and create a piece of art that's coloured onto a glass plate sliding onto the bracelet. It's one of the world's first print-on-demand jewellery companies and the products are beautiful. Artificial intelligence techniques are reviewed in intentions of developing algorithmic designs, and assisting hard computations and parameter optimizations in designing and casting. With those computer advanced technologies, major challenges and solutions are systematically discussed and commented to address new developments for jewelry industry. In general, product design process begins with identifying customer needs, concept generation, concept selection, evaluation, and prototyping. Several AI techniques have been applied to assist designers in almost all stages of product design. For instances, customer needs was identified using the concepts of Quality Function Deployment (QFD) and fuzzy set theory and fuzzy inference. Product form was automatically created using EA, fuzzy logic, hybrid of fuzzy, neural network and genetic algorithms (GA), and hybrid of neural network and kansei expert system. Fuzzy logic technique was also applied to evaluate and select design concepts. Affective user satisfaction was analyzed and modeled using a fuzzy rule-based method. In production, several researches had been applied AI techniques for optimizing process parameters or predicting some consideration properties, which can be applied the concepts to jewelry casting such as the attempt that used GA and ANN in porosity minimization of aluminum casting, the application of GA in shape and process parameter optimization, the optimization of product quality of casts using heuristic search techniques and by using GA and knowledge base. https://onticdesign.com/en/about http://dx.doi.org/10.1145/2807442.2807448 http://web2.eng.nu.ac.th/nuej/file/journal/NUEJ_Vol6_1_2011_paper06.pdf
- 15. Web site design And beyond Adobe Creative Suite TheGrid.io the first artificially intelligent website- design tool Upload photographs that capture the mood and feeling of your brand and The Grid matches them. The Grid tries to understand your website design needs through algorithms and the results are promising. Tell it you want an eCommerce store, blog and testimonials and a unique website is put together with fonts and colours based on uploaded photographs and more. How can designers adapt? 1) Join the AI party Change your skillset and learn to code. Develop your own The Grid and become a platform enabling consumers to design things for themselves. 2) Diversify your design skills to survive. Digital design which has strong structure-based frameworks will be the first to succumb to AI's computer might: website design, digital publishing, app design... We're even seeing AI being applied to TV show openings. Diversify towards work that requires human empathy – the one weakness of computers – and creative storytelling to survive. Fashion films shine a light here. In case these changes to design sound familiar to you, you're right, they are. Before Adobe released its creative suite in 2003 digital artists had to programme their own effects, filters and templates. Motion graphics stem from the demoscene of the eighties where groups of hackers would crack software then put a little graphical intro at the beginning to show it was cracked. These graphics led to groups of mostly males – FullScream included – competing to see who could make the best graphics using the limited computing power available; these were called Demos. And when Adobe started bringing out its software, oh boy, did people moan. Can you imagine it, people complaining about the advent of Illustrator, Photoshop and After Effects? People wouldn't have to learn to code anymore – the core skill then of digital creativity – and so bland artwork that all looks the same was feared. But what happened instead? A huge explosion in the quality, quantity and diversity of design. Millions more people were given the toolset to make their ideas reality. It created a new industry and tens of thousands of new jobs. The same will happen with the AI revolution. An industry will be created around developing AI applications and digital designers will have to re-learn how to code. The movement will also spawn pseudo-AI digital art with the decisions made being a combination of computer and human thoughts. For example, a computer program could analyse the millions of photos on Instagram and understand what trends and styles of imagery are gaining traction. It could choose colours, filters and effects that have the most interaction within target markets. A draft picture could be created and then you, humble human designer, could use it as a starting point for your own work. www.digitalartsonline.co.uk https://www.youtube.com/watch?v=OXA4-5x31V0 https://www.youtube.com/watch?v=tdWs_ZJL2c8 https://www.youtube.com/watch?v=Tda7jCwvSzg http://prisma-ai.com/ https://www.youtube.com/watch?v=9c4z6YsBGQ0 DEMOSCENE PHOTOSHOPDEMOSCENE AI FILTER AI TOOL
- 16. Videography Generative models everywhere Magic Pony Technology, created by graduates of Imperial College London with expertise in statistics, computer vision, and neuroscience, trains large neural networks to process visual information. technologyreview.com techcrunch.com/2016/06/20/ Everyone can shoot video but few can record or afford a legal soundtrack. Until now. With Jukedeck’s new artificial intelligence music composition technology, creators can get a cheap, royalty free soundtrack custom-made for their video. Jukedeck users don’t even need musical talent. They just select the mood, style, tempo and length, and Jukedeck returns a unique song to match their short film, YouTube series or 6-second Vine. http://web.mit.edu/vondrick/tinyvideo/
- 17. Virtual and Augmented Reality Low-hanging fruit Although it looks like a real woman, the ultra-lifelike figure in this music video is actually a digital model created using high-resolution 3D scans. London- based multimedia studios Marshmallow Laser Feast and Analog teamed up to create the video, called Memex, which is currently on show as part of the Istanbul Design Biennial 2016. https://www.youtube.com/watch?v=ALc4xoy3-Yk variety.com/2016 dezeen.com/2016/10/21 dailymail.co.uk/sciencetech Image Courtesy:- http://www.vrguru.com thebimhub.com/2015/07/28, BIM, AEC, Autodesk augment.com/augmented-reality-architecture Image-guided medical procedures spl.harvard.edufortune.com/2016/04/12
- 18. Photography & Visual Arts Massive opportunities http://www.slideshare.net/PetteriTeikariPhD/p rediction-of-art-market https://dl.dropboxusercontent.com/u/6757026/slideShare/visua lArtsPredictionSystem.pdf
- 19. Photography Automatic curation Posted on February 29, 2016 by Appu Shaji 3 Comments Tagged cuDNN, Deep Learning, Machine Learning, photography, Theano, Torch EyeEm is a community and marketplace for passionate photographers. More than 15 million photographers use EyeEm to share their photos, connect with other photographers, improve their skills through masterclasses, get recognition through our photography missions and exhibitions, andearn money by licensing their photos. The following video shows the impact of our deep-learning-based automatic aesthetic curation on the EyeEm search experience—read on to learn more about how it is done. https://vimeo.com/154364175#at=55 At EyeEm we develop technology that helps photographers tell their stories and get discovered. We believe there are two ingredients that contribute to the success of a photograph: 1) the story behind the photograph, and 2) the way that story is told. Automatic image tagging technologies from companies like EyeEm, Google Cloud Vision, Flickr, and Clarifai are quickly approaching maturity and helping to tell the stories behind photographs by indexing or tagging them to make them discoverable. Visual aesthetics addresses the way each story is told; specifically, how the visual style and composition create an emotional connection with the viewer by using structure and cues that draw attention toward (and away) from the constituent story elements of the photographer’s choice. Our objective is to compare images and learn the commonalities between well- crafted photographs, and the differences between well-crafted and mediocre photos. A loss function that can express this is as an extension of the hinge loss function, Researchers have explored such loss functions in image similarity, metric learning and face identification settings with great success in the past (Chechik2010 , Norouzi2011, Schroff2015). In a larger context, such energy functions fall into the class of implicit regression (LeCun06), where the loss function penalizes the constraint that input variables must satisfy. As a photography-first company, we validate our assumptions with our internal curators and reviewers, who spend the major part of their working day curating photographs. We track the time they spend on curation tasks where the image list is prioritized via the aesthetic rank vs. the default sort order. Using deep learning, we have been able to reduce curation time by 80%. [Chechik 2010] G. Chechik, V. Sharma, U. Shalit, S. Bengio, Large Scale Online Learnings of Image Similarity Through Ranking, Journal of Machine Learing Research 11, 2010 [Norouzi 2011] M. Norouzi, D. Fleet, Minimal Loss Hashing for Compact Binary Codes, International Conference in Machine Learning (ICML), 2011. [Schroff 2015] F. Schroff, D. Kalenichenko, J. Philbin , FaceNet: A Unified Embedding for Face Recognition and Clustering, ArXiV : 1503.03832, 2015 [LeCun 2006] Y. LeCun, S. Chopra, R. Hadsell, M. Ranazato, F. J. Huang, A Tutorial on Energy-Based Learning, Predicting Structured Data, 2006
- 20. Photography/Imaging → Generative Photorealistic CGI https://www.youtube.com/watch?v=R1-Ef54uTeU theverge.com/2014/8/29 http://www.cgsociety.org/index.php/cgsfeatures/cgsfeaturespecial/building_3d_with_ikea So where are the deep learning / algorithms putting CG artists out of business? These approaches still require manual work https://blogs.nvidia.com/blog/2015/08/11/photorealistic/ This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power. “From physical model to beautiful render” https://arxiv.org/abs/1605.02029
- 21. Photography Smart imaging (of course) AI Enabled vs Phase Detect Autofocusing by Jafaar Almusaad http://cognitionx.com/ai-better-photography/ The Frankencamera F3, an experimental computational photography camera platform. The camera runs Linux, and its metering, focusing, demosaicing, denoising, white balancing, and other processing is programmable. https://graphics.stanford.edu/projects/camera-2.0/ Non-AI based insipration in 'smart cameras' Frankencamera F2 http://prolost.com/blog/lightl16 https://light.co/ Manuscripts are solicited to address a wide range of topics on computer vision techniques and applications focusing on computational photography tasks, including but not limited to the following: ● Advanced image processing ● Computational cameras ● Computational illumination ● Computational optics ● High-performance imaging ● Multiple images and camera arrays ● Sensor and illumination hardware ● Scientific imaging and videography ● Organizing and exploiting photo/video collections ● Vision for graphics ● Graphics for vision wikicfp.com www.ximea.com Smart cameras with GPU enhancement artefactgroup.com
- 22. Floral Business Disruptive business models http://time.com/4117334/valentines-day-flower-delivery-startups/ http://fortune.com/2016/02/03/bloomthat-nationwide-delivery/ For a few years, flower delivery startups have emerged to try and grab a share of the $4 billion online flower industry. No generative approaches to design floral bouquets or something similar imho. Only image recognition mainly for AgTech purposes. They packed up and moved to San Francisco, and were accepted into the startup bootcamp Y Combinator's 2013 class. They named the company they created BloomThat, and set out to create bouquets for same-day delivery that are more lovely and seasonal than even local florists could pull off. Along with several other startups in major metros around the country, Farmgirl Flowers is set on reinventing the roughly $10 billion business of buying flowers. “Everyone says the flower industry is a dying industry because flower shops don’t work with the overhead, and the flower companies don’t offer what people want,” says Stembel, the company’s sole founder. “I just saw this and thought this is absurd. How has nobody done anything in this industry?”