分类： 心理学 >> 社会心理学 提交时间： 2023-03-28 合作期刊: 《心理科学进展》
摘要： 用户换位型思维是创业者进行机会识别并应对市场竞争的重要因素, 但已有研究忽视了其模式、形成机理及对机会信念绩效的影响。针对此问题, 基于结构映射理论与注意力参与模型解构用户换位型思维模式, 包括吸收式、归纳式、启发式和分析式; 然后, 从“个体?用户”双元视角探讨与用户有关的先验知识、灵活的角色导向、认知复杂性以及用户需求不确定性和碎片化等因素对用户换位型思维形成的正向影响; 最后, 以机会信念形成速度和创新性为绩效指标, 阐释用户换位型思维模式对机会信念形成绩效的影响, 并考察适应新的信息环境和调用自身知识结构的认知管理策略对用户换位型思维模式与机会信念形成绩效的调节作用。研究结论将丰富用户换位型思维的内涵, 拓展结构映射理论与注意力参与模型的解释范围, 也为指导创业者运用用户换位型思维去识别机会提供参考, 对创业者思考与理解用户有重要意义。
分类： 管理学 >> 管理学其他学科 提交时间： 2022-07-20
摘要： 用户换位型思维是创业者进行机会识别并应对市场竞争的重要因素，但已有研究忽视了其模式、形成机理及对机会信念绩效的影响。针对此问题，基于结构映射理论与注意力参与模型解构用户换位型思维模式，包括吸收式、归纳式、启发式和分析式；然后，从个体-用户双元视角探讨与用户有关的先验知识、灵活的角色导向、认知复杂性以及用户需求不确定性和碎片化等因素对用户换位型思维形成的正向影响；最后，以机会信念形成速度和创新性为绩效指标，阐释用户换位型思维模式对机会信念形成绩效的影响，并考察适应新的信息环境和调用自身知识结构的认知管理策略对用户换位型思维模式与机会信念形成绩效的调节作用。研究结论将丰富用户换位型思维的内涵，拓展结构映射理论与注意力参与模型的解释范围，也为指导创业者运用用户换位型思维去识别机会提供参考，对创业者思考与理解用户有重要意义。
分类： 工程与技术科学 >> 光学工程 提交时间： 2021-10-21
摘要： 针对MEMS激光雷达在研发过程中的内部时间同步问题，提出了一种简单的自标定方法。首先，我们介绍了MEMS激光雷达的内部时间不对齐问题。在此基础上，提出了一种鲁棒的最小垂直梯度（MVG）先验算法，用于标定激光与MEMS反射镜之间的时间差，该时间差可以自动计算，无需任何人工参与或专门设计的合作目标。最后，在MEMS激光雷达上进行了实际实验，验证了该方法的有效性。需要注意的是，校准可以在简单的实验室环境中进行，无需任何测距设备和人工参与，这大大加快了实际应用中的研发进度。
分类： 计算机科学 >> 计算机软件 提交时间： 2021-03-01
摘要： This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed. In the ME-Retinex model, a new constraint on the reflectance image is introduced that the maximum channel of the reflectance image conforms to the maximum channel of the low light image and its entropy should be the largest, which converts the decomposition of reflectance and illumination in Retinex model to a non-ill-conditioned problem and allows the ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net are carefully formulated to separate the noises and details during training, and they are based on the idea that, if noises are independently distributed, after the processing of smoothing filters (\eg mean filter), the gradient of the noise part should be smaller than the gradient of the detail part. It can be proved qualitatively and quantitatively through experiments that the proposed method is efficient.
分类： 计算机科学 >> 计算机应用技术 提交时间： 2020-08-26
摘要： Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time can be used for training, and there is no need to carefully select the supervised images which will save a lot. In addition, it takes less than 20 ms to process a color image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness. (Code will be available at https://github.com/hitzhangyu/image-enhancement-with-denoise)
分类： 计算机科学 >> 计算机科学技术其他学科 提交时间： 2020-03-06
摘要： 本文提出了一种基于深度学习的自监督低照度图像增强方法。受信息熵理论和Retinex模型的启发，我们提出了一种基于信息熵最大的Retinex模型。利用该模型，一个非常简单的网络可以将照度图和反射图分离开来，且仅用低照度图像就可以进行训练。为了实现自监督学习，我们在模型中引入了一个约束条件：反射图的最大值通道与低照度图像的最大值通道一致，且其熵最大。我们的模型非常简单，不依赖任何精心设计的数据集（即使是一张低照度图像也能完成网络的训练），网络仅需进行分钟级的训练即可实现图像增强。实验证明，该方法在处理速度和效果上均达到了当前最新水平。
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2018-12-13 合作期刊: 《计算机应用研究》
摘要： 针对局部二值模式(local binary pattern，LBP)描述信息单一以及对噪声敏感的问题，提出一种多尺度自适应阈值局部三值模式(multi-scale adaptive local ternary pattern，MSALTP)编码算法。MSALTP首先将原始图像放大；其次将图像平均划分成几个区域，并计算像素的均值；然后计算每个区域中心像素与均值的偏差；最后提取ALTP特征，将结果统计特征直方图实现图像分类。实验表明提出的算法识别率比目前较好的抗噪声算法在不同的噪声下识别率有较大提高。
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