Towards Enhanced Photography in Changing Lighting using 3D Map and Matching


Towards Enhanced Photography in Changing Lighting using 3D Map and Matching – Lightroom is an indispensable step toward the realization of a common vision, but its implementation has been hampered by many issues. Many existing approach may have been tailored for a particular vision. In this paper, we propose a novel lightroom model, namely, 3D Lightroom Model (LMM), which is a fully automatic and flexible approach for improving and improving the quality of vision. The LMM model is based on the following two main objectives: 1) to provide a framework to achieve better performance on the vision task, and 2) to allow researchers to implement the LMM model into their research. In the first part, we address the image classification problem by learning a discriminant model based on a distance metric to learn the mapping of images and their color. We show that LMM can yield better performance in a variety of vision tasks (e.g., image classification) than the conventional LMM framework.

Human action recognition is a fundamental challenge of many computer vision applications. In this paper, we propose a novel technique to learn the human action prediction capability of a machine-learning model. This approach uses a deep learning framework which learns a mapping from human action data. This data is composed of multiple instances representing multiple actions from a sequence of actions. By jointly learning a novel model, the two data instances with the human action data, we can use the feature vectors as a learning mechanism using a deep learning framework. We test the ability of our model to predict human actions using a wide variety of human action datasets. We found that our model outperformed human action recognition systems in accuracy on several datasets. The proposed model was very effective over human actions recognition task.

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Towards Enhanced Photography in Changing Lighting using 3D Map and Matching

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  • Protein Secondary Structure Prediction Based on Mutual and Nuclear Hidden Markov Models

    Comparing human action recognition and recognition from natural image datasetsHuman action recognition is a fundamental challenge of many computer vision applications. In this paper, we propose a novel technique to learn the human action prediction capability of a machine-learning model. This approach uses a deep learning framework which learns a mapping from human action data. This data is composed of multiple instances representing multiple actions from a sequence of actions. By jointly learning a novel model, the two data instances with the human action data, we can use the feature vectors as a learning mechanism using a deep learning framework. We test the ability of our model to predict human actions using a wide variety of human action datasets. We found that our model outperformed human action recognition systems in accuracy on several datasets. The proposed model was very effective over human actions recognition task.


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