Search

Optical flow has been a promising technique for motion estimation from video. Existing optical flow methods primarily focus on three constraint terms between two frame images, which are brightness constancy, gradient constancy and smoothness constancy for each pair of corresponding pixels. However, such pixelwise constraints are not enough to summarize the local motions for most of objects and thus usually cannot achieve high accuracy in the motion estimation. In this paper, we present a novel framework to optimize the optical flow using patchwise brightness constraint, gradient constraint, as well as smoothness constraint. Since patchwise constraints can describe more local statistics invariant motion features for the objects located within the windows, we can obtain an optical flow technique with more stable and accurate performances. Our method is evaluated on a standard dataset. Experimental results show that our patchwise-constrained optical flow generally outperforms existing pixelwise-constrained optical flow.

As leaf images can be captured more and more conveniently, automatic leaf recognition has been the key to help us identify different kinds of plant. However, fast and robust leaf recognition is still an unsolved problem, because the leaf images can be collected among different growing stages and with different shapes and colors. In this paper, we present a fast and robust method for leaf recognition by identifying leaves based on rotation invariant shape context (RISC) and summed squared differences (SSD) color matching. Unlike the existing shape context, which is only scale and translational invariant, our proposed method can recognize the leaves with different rotational angles, namely rotation invariant. To distinguish plants having the same shape context but with different colors, we use SSD color matching to measure the similarity of different leaves. The combination of RISC and SSD makes our leaf recognition method faster and much more robust than conventional shape context method. In our experiment, we obtained convincing results to demonstrate its effectiveness.

Automatic leaf recognition has been a hot research topic as digital leaf images capturing becomes more and more convenient and popular, which is also essential for plant education. However, fast and robust automatic recognition for leafs remains a challenging problem. In this paper, we present a novel method for leaf recognition based on texture matching. To measure the similarity of two leaves which normally have different color distributions, lighting distributions, and viewing angles, we use binary Gabor pattern (BGP) matching to efficiently extract the texture feature by transforming an image into a pattern histogram. Support vector machine (SVM) classifier is then used to determine the final recognition results. Due to the robustness of combination of BGP and SVM, our method achieves an average recognition rate of up to 95.2 %.

Images and videos usually express perceptual information with meaningful structures. By removing fine details and reserving important structures, video texture smoothing can better display the useful structural information, and thus is significant in the video understandings for both human and computers. Compared with the image texture smoothing, video texture smoothing is much more challenging. This paper proposes a novel video texture smoothing method through combining existing Relative Total Variation (RTV) and optical flow matching. By considering both special relationship and color/gradient similarity between adjacent frames, we build an optimization framework with two novel regularization terms and solve the smoothed video texture via iterations. Convincing experiment results demonstrate the effectiveness of our method.

In this paper, we propose a novel method for automatic hand gesture recognition from images based on shape context. Unlike conventional approaches, our method can robustly detect hand gestures rotated with arbitrary angle. Specifically, we improve the existing shape context to rotational invariant by creating a new log-polar space based on the tangent line of the boundary points. We first align the two hand gestures by solving a correspondence problem. The similarity of two hand gestures are obtained by calculating the shape distance based on our proposed rotational invariant shape context. Finally, the best matched result is identified by retrieving the gesture with the maximal shape similarity. Our method is evaluated using a standard simulated gesture dataset. Experimental results show that our method can accurately identify hand gestures, either with or without rotation. Comparison experiments also suggest that our method outperforms existing hand gesture recognition methods based on conventional shape context.

Textures are ubiquitous and usually have fine details as well as meaningful structures on the surfaces. Many algorithms have been proposed for texture smoothing and structure extraction, but none of them obtained a satisfactory effect because the optimization procedure is very challenging. In this paper, we present a texture smoothing method based on a novel adaptive total variation framework. We propose using absolute variation to separate the important structures and the fine details of a texture. Then, a sharp total variation (STV) based on absolute variation and inherent variation is used to reinforce the structure edges during the smoothing process. Finally, by integrating our proposed STV and the existing relative total variation (RTV), we can not only smooth the fine detail of the textures, but also maintain the salient structures. Experiments show that our method outperforms the existing methods in terms of detail smoothing and salient structures preserving.

The feature extraction is the most critical step in image retrieval. Among various local feature extraction methods, scale-invariant feature transform (SIFT) has been proven to be the most robust local invariant feature descriptor, which is widely used in the field of image matching and retrieval. However, the SIFT algorithm has a disadvantage that the algorithm will produce a large number of feature points and is not suited for widely using in the field of image retrieval. Firstly, a novel significant measure algorithm is proposed in this paper, and the regions of interest in images are obtained. Then, SIFT features are extracted from salient regions, reducing the number of SIFT features. Our algorithm also abstracts color features from salient regions, and this method overcomes SIFT algorithm’s drawback that could not reflect image’s color information. The experiments demonstrate that the integrated visual saliency analysis-based feature selection algorithm provides significant benefits both in retrieval accuracy and in speed.

level set formulation and partial differential equations. The weighted endocardium surface and epicardium surface are then varying according to the local echo sampling density. To handle the noisy and non-uniform regions within the echo data, we used a regularization term to adaptively and continuously following the gradient descent of the topological change. Finally, the optimal the endocardium surface and epicardium surface are obtained by solving the minimal energy for the two weighted surfaces using a brief propagation algorithm. Results: Even for the typically low signal-to-noise and small field-ofview in echo images, our method can successfully segment the left ventricle by robustly extracting the endocardium surface and epicardium surface. The proposed method achieves a mean accuracy of 6.1±1.3% volume differences against the gold standard for MRI segmentation method and a mean accuracy of 14.5±2.1% for segmentation of all four chambers. Conclusion: Using a level set approach to segment left ventricle in 3D echocardiography provides a fully automatic tool for physicians to obtain the anatomical and diagnostic information for cardiac functional analysis, without requiring any user input or any other assistants. The ability of our method to automatically and accurately extract endocardium and epicardium from echo has promising clinical potential in further medical diagnosis and surgery planning.