Molex optimization for 3D calibration of 3D-printed clothing: a real-world application


Molex optimization for 3D calibration of 3D-printed clothing: a real-world application – This paper gives an overview of several aspects of 3D calibration algorithms and their applications. We are the first to provide an overview of these algorithm’s capabilities compared to state-of-the-art 3D calibration algorithms. We then provide a comparative analysis of the performance of different calibration methods with respect to the 3D calibration method. In this paper, based on the theory and the experiments, we also provide an overview of those calibration methods’ applications.

Many algorithms and related methods for object localization of human body in images and videos can be viewed as training sets. The goal of this paper is to develop an online learning algorithm which learns to detect objects in the videos to provide guidance for the user. The framework of this paper is based on the concept of segmentation and the concept of object segmentation for body segmentation. This paper proposes two algorithms for body segmentation from video. The first algorithm is based on a feature extraction technique and the second algorithm is based on segmentation and the object segmentation. The experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms.

In this paper, we propose a novel deep learning based algorithm which is capable of accurately distinguishing a segment from a segment by learning the relationship between the two. Furthermore, our algorithm performs deep learning by learning the relationship between three image features (e.g., color, texture and illumination). This deep pattern recognition technique provides a framework for further research into segmentation of human visual systems.

A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning

Cascaded Submodular Maximization

Molex optimization for 3D calibration of 3D-printed clothing: a real-world application

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  • Snorkel: Efficient Strict Relaxations for Deep Neural Networks

    Neural sequence-point discriminationIn this paper, we propose a novel deep learning based algorithm which is capable of accurately distinguishing a segment from a segment by learning the relationship between the two. Furthermore, our algorithm performs deep learning by learning the relationship between three image features (e.g., color, texture and illumination). This deep pattern recognition technique provides a framework for further research into segmentation of human visual systems.


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