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With the quad-tree based coding structure and more flexible intra-modes, the coding efficiency provided by intra-technique in inter-frames of HEVC is much higher than the preceding standard H.264/AVC. However, the computing complexity is also significantly increased. Although only a few CUs are encoded as intra-mode in inter-frames finally, almost all CUs need to be checked all the intra-options to obtain the optimal mode, which may be in fact unnecessary.

Atrial fibrillation (AF) patients need long-term electrocardiography (ECG) monitoring to detect occurrence of AF. We can acquire ECG signals under low power by compressive sensing based sensor and detect AF by existing algorithms. However, the compression ratio of AF signal is low when DWT basis is applied for CS reconstruction. On the other hand the complexity of AF detection algorithms is high. In this paper, we propose a CS-based ECG monitoring system with effective AF detection. We exploit dictionary learning to improve 2.5x better compression ratio than existing works.

We propose a real-time plane detection method for projection-based Augmented Reality (AR) system in an unknown environment. While previous works usually designate space, the plane detection method automatically detects multiple planes based on the proposed constrained sampling strategy in RAndom SAmpleing Concensus (RANSAC). For each plane, an area for projection is selected for contents while considering occlusions by other objects.

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way.

Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.

Deep hierarchical models for feature learning have emerged as an effective technique for object representation and classification in recent years. Though the features learnt using deep models have shown lot of promise towards achieving invariance to data transformations, this primarily comes at the expense of using much larger training data and model size. In the proposed work we devise a novel technique to incorporate rotation invariance, while training the deep model parameters.

Edited film alignment is the post-production process of finding small parts of unedited footage that temporally and spatially match an edited film. The huge amount of data to be processed makes significant downsampling of the videos essential in real-life applications. Simultaneously, professional users demand that the task be achieved with frame and pixel-level accuracy. We propose a novel selective Hough transform (SHT) and an accurate template matching method to address the difficult trade-off between accuracy and scalability.