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As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality. 相似文献
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Trung Kien Dang Marcel Worring The Duy Bui 《Computer Vision and Image Understanding》2011,115(11):1516-1524
We present a semi-interactive method for 3D reconstruction specialized for indoor scenes which combines computer vision techniques with efficient interaction. We use panoramas, popularly used for visualization of indoor scenes, but clearly not able to show depth, for their great field of view, as the starting point. Exploiting user defined knowledge, in term of a rough sketch of orthogonality and parallelism in scenes, we design smart interaction techniques to semi-automatically reconstruct a scene from coarse to fine level. The framework is flexible and efficient. Users can build a coarse walls-and-floor textured model in five mouse clicks, or a detailed model showing all furniture in a couple of minutes interaction. We show results of reconstruction on four different scenes. The accuracy of the reconstructed models is quite high, around 1% error at full room scale. Thus, our framework is a good choice for applications requiring accuracy as well as application requiring a 3D impression of the scene. 相似文献
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针对传统互信息图像配准拼接算法计算量大、效率低等问题,本文结合模板匹配,提出基于模板与互信息的全景图拼接技术。首先将误差法和二次匹配误差法相结合,对待拼接图像进行初次模板匹配,划定大致重叠区域;接着从互信息量的角度比较相邻重叠的两幅图像的相似性,通过建立两幅图像之间的互信息量,计算最大互信息,获得匹配区域;然后再次利用模板匹配,设定最佳匹配区域,最终实现图像配准拼接。在VS2010+Opencv环境中编程实现重叠图像的拼接,并验证了算法的正确性。实验表明,本文算法具有计算量相对小,自动化程度高,配准拼接精度高等优点。 相似文献
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根据外在表现形式的差异将全景分为四种类别并进行分析和比较,针对目前国内外管理信息系统的全景功能大多都是以独立软件的方式进行开发应用,并且多数只能完成柱型全景的制作这两个问题,选用球型全景技术,以插件的形式在管理信息系统中设计和实现数字化全景浏览。 相似文献
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提出了一种从光照变化的序列图象中拼接全景图的方法 .该方法首先将待拼接图象的重叠部分分解成水平集表示 ,并且定义一个形态学距离用于测量水平集之间的相似度 ,然后根据这个形态学距离 ,对其中一幅图象的每一个水平集都在另一幅图象的水平集中找到对应 ,从而得到一个单调转换函数 ,用于表示两幅待拼接图象水平集之间的映射 ,用这个转换函数调整其中一幅图象的对比度与另一幅图象相对应 ;最后 ,用基于灰度匹配的方法将两幅图象拼接 ,图象两两拼接后经全局误差校正即可得到一幅正确拼接的全景图 .该方法可以广泛应用于基于图象的绘制、图象处理等领域 相似文献
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近年来,球形全景的虚拟现实方法开始发展起来.在已有的球形全景图像拼接方法中,基本上都是"半自动"的拼接方法,需要很多人工操作,效率低下、成本高昂.提出一种球面自寻匹配的拼接算法.该算法通过把多幅图像映射到一个合适的球面上,自动调整每幅图像的插入点,使得重合位置的差值图像灰度累积平均值最小,然后球面的图像反映射成平面形式,最终得到拼合图像.该算法可使程序实现完全自动化,使人工操作降至最低,从而降低全景图像的制作成本. 相似文献
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根据对航拍视频进行实时全景图拼接的实际需求,提出一种两阶段的关键帧提取方法。第一阶段利用飞行时间与重叠率的线性关系,以较高重叠率提取准关键帧,第二阶段对准关键帧进行逐帧检测,进一步降低冗余,最后确定关键帧,为全景图拼接做好准备。经实验检验.该方法能够提取出有效关键帧,具有可实用性。 相似文献
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基于图象的绘制(Image Based Rendering)作为一种全新的图形绘制方式,以其相对于传统几何绘制而言,具有高效实用的优点,近年来得到了研究人员越来越多的关注,但IBR技术仍存在一些主要难点,如图象的无缝拼接和实时漫游。针对此问题,开发了一个基于部分球面模型的室内虚拟漫游系统。该系统采用自动匹配和人机交互相结合的方法,可以无缝地将多幅照片拼接成一张全景图,同时采用一种改进的基于查找表的算法,实现了固定视点的实时漫游。 相似文献
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