Towards a Unified Framework for 3D Model Refinement


Towards a Unified Framework for 3D Model Refinement – We propose an approach for the automated extraction and classification of high dimensional multiview frames from videos from a remote camera system. A video classification system is composed of multiple video datasets. The data of different frames is collected by a single camera. Video classification is performed by calculating the mean relative motion of each video instance. Given the frames of videos as high dimensional vectors with some specific feature vectors, a low-dimensional coordinate system of image features is proposed to classify frames, to estimate the motion of each video instance. The system is capable of performing video classification by utilizing a single video dataset. The proposed method can also be applied to other different video classification methods, either in one camera or multiple cameras. Experimental results demonstrate that the proposed method can be easily implemented and trained in a single image.

This paper proposes a deep neural network for the object detection and object classification tasks. Our proposed model is trained with a novel approach, where two deep neural networks are learned to deal with various object classes. The training set used for object class learning is the same as the one used in object classification. Two network-based approach is then implemented to solve the object detection and classification problems. The proposed approach is evaluated using the publicly available dataset of the 2016 COCO 2015 Object Detection & Classification Task. The classification results showed a significant improvement in recognition performance in the two-class setting over similar approach when the object classification dataset. The proposed method outperforms other state-of-the-art methods in the object detection and classification datasets.

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Towards a Unified Framework for 3D Model Refinement

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    Constrained Deep Network-Based Hierarchical Decision Making for Learning Item Levels and Reward OrientationThis paper proposes a deep neural network for the object detection and object classification tasks. Our proposed model is trained with a novel approach, where two deep neural networks are learned to deal with various object classes. The training set used for object class learning is the same as the one used in object classification. Two network-based approach is then implemented to solve the object detection and classification problems. The proposed approach is evaluated using the publicly available dataset of the 2016 COCO 2015 Object Detection & Classification Task. The classification results showed a significant improvement in recognition performance in the two-class setting over similar approach when the object classification dataset. The proposed method outperforms other state-of-the-art methods in the object detection and classification datasets.


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