Variational Methods in Machine Vision
نویسندگان
چکیده
منابع مشابه
"Determining optical flow": A Retrospective
Our work on estimating optical flow [8] was important not because it solved (in some limited way) the problem of estimating optical flow, but because it represented the start of the variational approach to machine vision. The variational approach to machine vision problems was applied to shape-from-shading soon afterwards (Ikeuchi and Horn [10] ), and has since found its way into many areas of ...
متن کاملارزیابی یک سیستم بینایی ماشین از راه اندازهگیری و تخمین شماری از ویژگیهای فیزیکی پسته
In order to increase the role of machine vision in agricultural research in Iran, especially for measuring physical attributes of seeds, a machine vision system was developed using a computer, a capture card, a video camera and a light box. All equipment was purchased from domestic markets. Computer programs were developed for hardware setup and for image processing applications. The programs p...
متن کاملارزیابی یک سیستم بینایی ماشین از راه اندازهگیری و تخمین شماری از ویژگیهای فیزیکی پسته
In order to increase the role of machine vision in agricultural research in Iran, especially for measuring physical attributes of seeds, a machine vision system was developed using a computer, a capture card, a video camera and a light box. All equipment was purchased from domestic markets. Computer programs were developed for hardware setup and for image processing applications. The programs p...
متن کاملStructured Variational Distributions in VIBES
Variational methods are becoming increasingly popular for the approximate solution of complex probabilistic models in machine learning, computer vision, information retrieval and many other fields. Unfortunately, for every new application it is necessary first to derive the specific forms of the variational update equations for the particular probabilistic model being used, and then to implemen...
متن کاملVariational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Clustering is among the most fundamental tasks in computer vision and machine learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM pick...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011