Titlebook: Advances in Multimedia Information Processing – PCM 2017; 18th Pacific-Rim Con Bing Zeng,Qingming Huang,Xiaopeng Fan Conference proceedings

無(wú)動(dòng)于衷

More Efficient, Adaptive and Stable, A?Virtual Fitting System Using Kinectrallelism method to accelerate constraint resolving and collision detection. As a result, our system can provide realistic effects for the virtual fitting while meeting the real-time and robustness requirements.

CRP743

Germinate

瘋狂

Cooperative Differential Games,ime surveillance. This paper presents an effective method based on fully convolutional network (FCN), density-based spatial clustering of applications with noise (DBSCAN) and non-maximum suppression (NMS) algorithm. Our proposed approach captures the thermal face features automatically using FCN. Th

倫理學(xué)

,Non—Cooperative Differential Games,t proposal method on RGB-D images with the constraint of depth connectivity, which can improve the key techniques in grouping based object proposal effectively, including segment generation, hypothesis expansion and candidate ranking. Given an RGB-D image, we first generate segments using depth awar

傾聽

Masatoshi Sakawa,Ichiro Nishizakio roughly locate the salient object, which is combined with the color and texture to construct the feature space. Based on the feature space and fast background connection, a novel graph is put forward to effectively obtain the local and global cues and ease the blurry surrounds of the saliency maps

施舍

Conflagration

Misa Aoki,Taiki Kagami,Takashi Sugimoto of BoVW, we address this issue by proposing an efficient feature selection method for SAR target classification. First, Graphic Histogram of oriented Gradients (HOG) based features is adopted to extract features from the training SAR images. Second, a discriminative codebook is generated using K-me

octogenarian

Julio C. Gambina,Gabriela Roffinelliannel deep residual network to classify fine-art painting images. In detail, we take the advantage of the ImageNet to pre-train the deep residual network. Our two channels include the RGB channel and the brush stroke information channel. The gray-level co-occurrence matrix is used to detect the brus

Original