Showing papers by "Houqiang Li published in 2006"

Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy

Abstract: It is important to observe and study cancer cells' cycle progression in order to better understand drug effects on cancer cells. Time-lapse microscopy imaging serves as an important method to measure the cycle progression of individual cells in a large population. Since manual analysis is unreasonably time consuming for the large volumes of time-lapse image data, automated image analysis is proposed. Existing approaches dealing with time-lapse image data are rather limited and often give inaccurate analysis results, especially in segmenting and tracking individual cells in a cell population. In this paper, we present a new approach to segment and track cell nuclei in time-lapse fluorescence image sequence. First, we propose a novel marker-controlled watershed based on mathematical morphology, which can effectively segment clustered cells with less oversegmentation. To further segment undersegmented cells or to merge oversegmented cells, context information among neighboring frames is employed, which is proved to be an effective strategy. Then, we design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed. Finally, we combine mean-shift and Kalman filter to achieve a more robust cell nuclei tracking method than existing ones. Experimental results show that our method can obtain 98.8% segmentation accuracy, 97.4% cell division tracking accuracy, and 97.6% cell tracking accuracy

Error Resilient Mode Decision in Scalable Video Coding

Abstract: Error resilient macroblock mode decision has been extensively investigated in the literature for single-layer video coding, for which error resilient mode decision is also called as intra refresh. In this paper, we present a loss-aware rate-distortion optimized macroblock mode decision algorithm for scalable video coding, wherein more macroblock coding modes than intra and inter are involved. Thanks to the good performance, the proposed method has been adopted into the joint scalable video model by the joint video team.

A Fast Downsizing Video Transcoder for H.264/AVC with Rate-Distortion Optimal Mode Decision

Abstract: This paper focuses on the mode decision and motion selection problem when H.264/AVC video streams are transcoded in spatial resolution. A fast downsizing transcoding scheme is developed in which a new rate-distortion (R-D) optimal mode decision mechanism is presented for high speed transcoding as well as high coding efficiency. A model for estimating relative prediction errors is applied in this paper, which is free from computation of interpolation and SAD/SSD computation. Based on the selected model, a motion refinement within a distance of 1 pixel is performed after mode decision. Experimental results demonstrate that our method can significantly speed up the spatial resolution reduction process, while achieving high coding efficiency.

Error Resilient Video Coding using Redundant Pictures

Abstract: Coding of redundant pictures is supported in the latest international video coding standard H.264 (also known as MPEG-4 part 10 or AVC). This paper proposes a standard-compliant way to encode and decode redundant pictures for improved error resilience. The method is based on a combination of picture-level reference picture selection, reference picture list ordering, and a hierarchical allocation of redundant pictures, which can efficiently prevent temporal error propagation without relying on feedback information. Simulation results show that the method outperforms the optimal loss-aware rate-distortion optimized intra refresh method. The proposed algorithm has been adopted into the H.264 joint model.

An attention based spatial adaptation scheme for h.264 videos on mobiles

Abstract: With the growing popularity of personal digital assistant devices and smart phones, consumers have become increasingly enthusiastic to watching videos from these mobile devices. However, when browsing videos in mobiles, users often feel that the display resolution greatly affects their perceptual experience with the limited screen size. In this paper, an attention based spatial video adaptation scheme is proposed to overcome the display constraints by producing and displaying the region of interest. According to the size of the target display, we automatically detect and crop the informative region in each frame to generate a smooth sequence. To avoid costly full encoding operations, we develop a set of transcoding techniques based on the H.264 standard. Experimental results show that this approach not only improves the perceptual quality but also saves the bandwidth and computation, especially for the videos which have not been well edited.

Sketch-Guided Texture-Based Image Inpainting

Abstract: In this paper, we propose a novel framework for image inpainting, named sketch-guided texture-based image inpainting. Inspired by the well-known primal sketch model, we present a penetrating perspective into the process of image formation, where each image is seen as a variety of texture organized by some underlying structure. Based on this conceptual foundation, our approach of image inpainting integrates two unified stages: it first reconstructs the image structure with the sketch model, and then guided by the structure, it restores the missing region by patch-based texture synthesis. The major superiority of the framework over other ones consists in its capability of simultaneously recovering the structure and texture in the missing regions. Comprehensive experiments are performed to compare our method with other state-of- the-art ones; the encouraging results obtained convincingly demonstrate the effectiveness of our method.

Transcoding to FGS Streams from H.264/AVC Hierarchical B-Pictures

Abstract: This paper presents a transcoder which transcodes to FGS streams from H.264/AVC hierarchical B-pictures. First, the DCT-domain architecture is designed for fast FGS transcoding. Then, we propose a mode decision method in DCT domain to achieve a trade-off between the performances at low bit-rate and high bit-rate. Experimental results demonstrated that our method can improve the coding performance up to 1 dB at low rate and only lose at worst 0.5 dB at high rate.

An attention based spatial adaptation scheme for H.264 videos on mobiles

Abstract: When browsing videos in mobile devices, people often feel that resolution greatly affects their perceptual experience in the limited screen size. In this paper, an attention based spatial video adaptation scheme is proposed to overcome display constraints by producing the region of interest. According to the size of the target display, we automatically detect and crop the informative region in each frame to generate a smooth sequence. To avoid costly fully encoding operations, we employ a set of transcoding techniques based on the H.264 standard. Experimental results show that this approach not only improves the perceptual quality but also saves the bandwidth and computation, especially for the videos which are not well edited.

Off-Line Motion Description for Fast Video Stream Generation in MPEG-4 AVC/H.264

Abstract: The rate-distortion optimal mode decision as well as motion estimation adopted in H.264 brings a big challenge to real-time encoding and transcoding due to the high computation complexity. In this paper, we propose a hierarchical motion description model to present the motion data of each macroblock (MB) from coarsely to finely. A preprocessing approach is developed to estimate the motion data for each MB at each quality level with regard to its reference quality, its adjacent MBs and the target bit-rate. The resulting motion data can be coded and stored as metadata in a media file or a stream. Moreover, we propose a method to readily extract the specific motion data from the model for each MB at given bit-rates. Experimental results have shown the effectiveness of our proposed motion description model in terms of coding efficiency as well as fast bit-rate adaptation in comparison with that of H.264

Attention information based spatial adaptation framework for browsing videos via mobile devices

Abstract: The limited display size of the mobile devices has been imposing significant barriers for mobile device users to enjoy browsing high-resolution videos. In this paper, we present a novel video adaptation scheme based on attention area detection for users to enrich browsing experience on mobile devices. During video compression, the attention information which refers to as attention objects in frames will be detected and embedded into bitstreams using the supplement enhanced information (SEI) tool. In this research, we design a special SEI structure for embedding the attention information. Furthermore, we also develop a scheme to adjust adaptive quantization parameters in order to improve the quality on encoding the attention areas. When the high-resolution bitstream is transmitted to mobile users, a fast transcoding algorithm we developed earlier will be applied to generate a new bitstream for attention areas in frames. The new low-resolution bitstream containing mostly attention information, instead of the high-resolution one, will be sent to users for display on the mobile devices. Experimental results show that the proposed spatial adaptation scheme is able to improve both subjective and objective video qualities.

A novel fast inter-prediction mode decision for H.264/AVC

Abstract: Video coding in wireless environment requires lower computational complexity and lower energy consumption than that used in storage oriented or network oriented application. Although H.264/AVC standard provides considerable higher compression efficiency as compared to the previous standards, its complexity is significantly increased at the same time. In a H.264/AVC encoder, the most time-consuming components are variable block sizes motion estimation and mode decision using rate-distortion optimization (RDO). In this paper, we propose a novel fast inter-prediction mode decision by exploiting high correlation between rate-distortion costs (RD cost) of macroblocks in the current inter frame and their co-located macroblocks in the previous inter frame. Using this new algorithm, we can reduce a number of inter mode candidates and skip motion estimation for these modes. In addition, our algorithm can also decrease a number of tested intra modes. Simulation results show that our approach can save 20% to 50% encoding time, with a negligible PSNR loss less than 0.1 dB and bit rate increase no more than 2% for almost all the test sequences.

Identification of cell-cycle phases using neural network and steerable filter features

Abstract: In this paper, we aim to address the cell phase identification problem, and two important aspects, the feature extraction methods and the classifier design, are discussed. In our study, we first propose extracting high frequency information of different orientations using Steerable filters. Next, we employ a multi-layer neural network using the back-propagation algorithm to replace K-Nearest Neighbor (KNN) classifier which has been implemented in the Cellular Image Quantitator (CELLIQ) system [3]. Experimental results provide a comparison between the proposed steerable filter features and existing regular features which have been used in published papers [3, 5]. From the comparison, it can be concluded that Steerable filter features can effectively represent the cells in different phases and improve the classification accuracy. Neural network also has a better performance than KNN currently deployed in CELLIQ system [3].