Global Image Recognition Market report provides in-depth analysis of Top Players, Geography, End users, Applications, Competitor analysis, Revenue, Price, Gross Margin, Market Share, Import-Export data, Trends and Forecast.\nEffect of COVID-19: Image Recognition Market report investigate the effect of Coronavirus (COVID-19) on the Image Recognition industry.\nThe all-inclusive analysis of these data provides an in-depth and detailed insight into the global Image Recognition market.\nAbout Inside Market ReportsInside Market Reports provides the most comprehensive database of market intelligence reports.\nThe latest market research reports on industries, the developments and innovations have all the trends of well-known industries and prospects.
"}}}},{"node":{"postTitle":"What cloud storage options are available for photos in 2021?","slug":"what-cloud-storage-options-are-available-for-photos-in-2021","publishDate":"2021-06-09 19:00:00-04:00","createdLocal":"2021-06-10 14:31:22.012032","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"You might have heard the news recently that Google Photos unlimited storage is going away and photos that you store from this point forward will count towards your total Google storage quota (which includes your Gmail and Google Drive data).\nEven with that change, you probably still want to store your photos in the cloud.\nThere are plenty of options to choose from but some are better than others.\nHere are some options for you to consider for cloud storage, presented in a handy chart.
"}}}},{"node":{"postTitle":"Behind the Lens w/ Google Pixel promo highlights AR search","slug":"behind-the-lens-w-google-pixel-promo-highlights-ar-search","publishDate":"2021-06-09 00:00:00","createdLocal":"2021-06-10 14:31:18.093507","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Alongside Live View directions in Maps and 3D Animals, Lens is one of Google’s key investments in augmented reality.\nGoogle UK is now promoting the visual search tool with a “Behind the Lens with Google Pixel” promo campaign.\nThe feature is being demoed on Pixel phones, and Google specifically touts the integration in the native Camera.\nPrevious Google Lens campaigns involved newspaper ads for season 3 of Stranger Things that “came to life” with AR.\nThis Google Lens promo (and new stat) comes as Apple on Monday announced Live Text and Look Up.
"}}}},{"node":{"postTitle":"Upgrade your TikTok videos with the world’s smallest smartphone video stabilizer with AI tracking","slug":"upgrade-your-tiktok-videos-with-the-worlds-smallest-smartphone-video-stabilizer-with-ai-tracking","publishDate":"2021-06-09 00:00:00","createdLocal":"2021-06-10 14:31:17.611958","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Over the years, the smartphone camera has become one of the most advanced and pop photography and videography tools on the planet… the hand holding the camera, however, hasn’t evolved as much.\nAll that changes with the PowerVision S1, the world’s most compact smartphone gimbal with a built-in 3-axis stabilizer, a tripod, and even AI tracking for those completely hands-free shots.\nDesigned to be the size of a power bank, the PowerVision S1 is quite literally a tiny, AI-powered film crew.\nThe hardware of the PowerVision S1 is perhaps one of the most impressive design and engineering feats I’ve seen.\nExtend your charging options wherever you are so you always have a way to juice your phone and your PowerVision S1.
"}}}},{"node":{"postTitle":"Cortical Pathways or Mechanisms in the Face Inversion Effect","slug":"cortical-pathways-or-mechanisms-in-the-face-inversion-effect","publishDate":"2021-06-10 00:00:00","createdLocal":"2021-06-10 14:31:16.614098","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Within the FSZ or CON group, the upright face associated activation effect was defined using conjunctions of t-contrasts including upright faces > inverted faces, upright faces > scramble faces, and upright faces > objects.\nThe inverted face associated activation effect was defined using conjunctions of t-contrasts including inverted faces > upright faces, inverted faces > scramble faces, and inverted faces > objects.\nThen, we defined CON vs FSZ inter-group associated upright face activation effect (including t-contrast conjunctions of upright faces > inverted faces, upright faces > scramble faces, and upright faces > objects) and inverted face activation effect (including t-contrast conjunctions of inverted faces > upright faces, inverted faces > scramble faces, and inverted faces > objects).\nSimilarly, inverted-face selective areas were extracted from t-contrast maps regarding conjunctions of inverted faces > upright faces, inverted faces > scrambled faces, and inverted faces > objects (p < 0.05, FDR corrected).\nA conjunction analysis using a conjunction-null hypothesis47 was performed to identify common areas of activation across contrasts of upright faces > scrambled faces, upright faces > object, upright faces > inverted faces, and contrasts of inverted faces > scrambled faces, inverted faces > object, inverted faces > upright faces.
"}}}},{"node":{"postTitle":"Multi-class Classification of Breast Digital Pathology Image","slug":"multi-class-classification-of-breast-digital-pathology-image","publishDate":"2021-06-10 00:00:00","createdLocal":"2021-06-10 14:31:15.452184","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"In 2020, almost 1 in 4 of the newly diagnosed female cancer cases were breast cancer, and nearly 700,000 women died of breast cancer worldwide, accounting for 15.5% of female mortality.1 Therefore, early diagnosis of breast cancer plays a significant role in reducing cancer-related deaths.\nIn clinical practice, breast cancer is initially diagnosed by physical examination and medical imaging, including bilateral breast palpation, mammography, and ultrasonography.\nMaterials and MethodsA Deep Learning Framework for Multi-Class ClassificationIn our study, we proposed a deep learning approach designed for the analysis of breast cancer.\nThe classification of breast digital pathology images must rely on morphology-related features such as the density and variability of the nucleus.\nUnderstanding the evolution of the disease is critical for the prevention and treatment of breast cancer, especially for early diagnosis.
"}}}},{"node":{"postTitle":"What is the Best Face Recognition Software to Use","slug":"what-is-the-best-face-recognition-software-to-use","publishDate":"None","createdLocal":"2021-06-10 14:31:14.295205","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"What is the Best Face Recognition Software to UseLots of companies and organizations ask what is the best face recognition software to use for their application.\nBefore your company or enterprise shells out money for face recognition software, you should learn the concept behind it and the various applications it can be used for.\nIn asking what the best face recognition software is, you need to first define the parameters of what function the software will serve.\nElements of Face Recognition SoftwareFace recognition systems consist of two parts: the scanner and the processing software.\nUses of Face Recognition TechnologyWhen a manager asks, what is the best face recognition software for my application?
"}}}},{"node":{"postTitle":"Blocking AI to keep your personal data your own","slug":"blocking-ai-to-keep-your-personal-data-your-own","publishDate":"2021-06-10 15:38:18+10:00","createdLocal":"2021-06-10 14:31:00.992334","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"The change is very small and imperceptible to human eyes, but it introduces enough ‘noise’ into an image to make it useless for training AI.\nA key challenge for training deep neural networks is that the programs usually require a huge volume of data to learn from.\nIf someone uses unlearnable data to train deep learning models, the model will perform so poorly that it will be close to just making random guesses on new data.\nA small change in sound pressure level is indistinguishable for humans, but this change can make the audio data unlearnable for machines.\nOn a larger scale, companies could release protected proprietary data without worrying it will be used for training deep learning models.
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