Guijin Tang

Multi-Channel Mixed-Pattern Based Frame Rate Up-Conversion Using Spatio-Temporal Motion Vector Refinement and Dual-Weighted Overlapped Block Motion Compensation

In this paper, a novel motion compensated frame rate up-conversion (MC-FRUC) algorithm is proposed to enhance the visual quality of video sequences. First of all, the multi-channel mixed pattern (MCMP) is proposed to design a block matching criterion which uses a few computations to reveal the variances of one luminance channel and two chrominance channels in a video frame. Second, in basis of the forward and backward initial motion vector fields (MVFs) estimated by a variant of 3DRS algorithm, the spatio-temporal motion vector refinement (ST-MVR) algorithm is proposed to obtain the more smoother MVF by implicitly adding the spatio-temporal smooth constraint into the process of motion vector refinement, and then the highly fault-tolerant motion vector smoothing (HFT-MVS) algorithm is proposed to prevent the emergence of outliers in MVF. Finally, in order to reduce the edge blurring and occlusions, the proposed dual-weighted overlapped block motion compensation (DW-OBMC) algorithm uses the forward and backward MVFs to jointly produce the interpolated frame. Experimental results show that the proposed algorithm can significantly improve both objective and subjective quality of the interpolated frame with a low computational complexity, and provide the better performance than the existing algorithms.

Active tracking using color silhouettes for indoor surveillance

Pan-Tilt-Zoom (PTZ) cameras play an important role in surveillance systems. In this paper, we propose an active tracker using color silhouettes with a single camera. We firstly apply dilation and erosion operators of morphology to binary difference image to get color silhouettes. We also record the color silhouette of the target which we are interested in. Secondly, we measure the similarity of color silhouette between the observation and the candidates of silhouettes. We exploit the most similar one to update that of the tracked target. Finally, we control the PTZ camera to move according to the location of the tracked target. The experimental results show that the proposed algorithm can effectively track people even though she/he is fully occluded.

An improved image dehazing algorithm based on dark channel prior

Image dehazing algorithm based on dark channel prior has been proved to be effective, but it cannot still guarantee accurate transmission. To solve this problem, we firstly propose a more reasonable estimation of atmospheric light, because bias in the atmospheric light estimation will cause an inaccurate transmission. Secondly, we improve the estimation of transmission in bright area so as to alleviate color distortion. After image recovery, we carry out denoising to improve the image quality. Finally, we use a blind image quality assessment method based on property of Human Visual System, and the experimental results show that this improved algorithm is more effective.

A novel saliency detection model based on curvelet transform

Visually salient object or region detection in images is an active research field in recent years. Inspired by that curvelets can provide multi-scale sparse representation of objects with edges and textures, in this paper, we propose a novel saliency detection model based on fast discrete curvelet transform (SDCT) to detect more compact salient objects in an image. First, fast discrete curvelet transform is used to acquire multi-scale representation of feature maps in CIELab color space. Then the feature maps are transformed to feature salient maps based on dissimilarity measure between patches in a global manner. Finally, the complementary feature salient maps at each scale and each color channel are merged linearly to obtain unitary saliency map. Experimental results on MSRA saliency benchmark database show that the proposed SDCT model outperforms the most state-of-the-art saliency detection models in spatial and frequency domain with higher overall performance, especially acquires more compact salient object and suppresses background saliency effectively, which is desirable for many computer vision applications.

Simple and effective image quality assessment based on edge enhanced mean square error

Simple and effective image quality assessment (IQA) method is very desirable in many image and video processing applications, such as coding, transmission, restoration and enhancement. Classic pixel absolute error based objective IQA metrics such as mean square error (MSE) and corresponding peak signal to noise ratio (PSNR) are widely used for various applications due to low computation and clear physical meanings, but have also been criticized for poorly correlated with subjective evaluation. Inspired by that human visual system (HVS) is more sensitive to image local edge distortion than flat or texture areas, in this paper, we propose a novel edge enhanced MSE (EE-MSE) to emphasize edge distortion effects on IQA. Experimental results on LIVE database release 2 show that the proposed EE-MSE IQA metric is competitive with state-of-the-art HVS-based IQA metrics, while has lower computational complexity and is more suitable for optimization task.

Robust Face Hallucination via Similarity Selection and Representation

Face image super resolution, also referred to as face hallucination, is aiming to estimate the high-resolution (HR) face image from its low-resolution (LR) version. In this paper, a novel two-layer face hallucination method is proposed. Different from the previous SR methods, by applying global similarity selecting, the proposed approach can narrow the scope of samples and boost the reconstruction speed. And the local similarity representation step make the method have better ability to suppress noise for applications under severe condition. As a general framework, other useful algorithms can also be incorporated into it conveniently. Experiments on commonly used face database demonstrate our scheme has better performance, especially for noise face image.

Intelligent tuna recognition for fisheries monitoring

Integrated video camera systems have been installed on fishing boats to trial for fishery monitoring in some countries. Currently, substantial amount of video footage is manually analyzed off the boats after each trip. Automatic processing of the videos is important for saving time and manpower. In this paper, an intelligent tuna recognition method is proposed. The method includes four steps. Firstly, the video is pre-processed by suppressing fast moving objects such as human. Secondly, the color and texture features are extracted to describe tuna, deck and other objects. Thirdly, support vector machine and statistic shape model are employed to identity and recognize tuna. Finally, a prior-knowledge based post-processing method is used to refine the recognition result. The experiment has showed that the proposed method is accurate and robust in tuna recognition. The method can also be used for other fish recognition applications, benefiting fisheries monitoring by providing efficient and automatic fish recognition.