A KD-Tree-Based Nearest Neighbor Search for Large Quantities of Data

[[abstract]]The discovery of nearest neighbors, without training in advance, has many applications, such as the formation of mosaic images, image matching, image retrieval and image stitching. When the quantity of data is huge and the number of dimensions is high, the efficient identification of a nearest neighbor (NN) is very important. This study proposes a variation of the KD-tree - the arbitrary KD-tree (KDA) - which is constructed without the need to evaluate variances. Multiple KDAs can be constructed efficiently and possess independent tree structures, when the amount of data is large. Upon testing, using extended synthetic databases and real-world SIFT data, this study concludes that the KDA method increases computational efficiency and produces satisfactory accuracy, when solving NN problems.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]KO

An Improved CAMSHIFT Tracking Algorithm Applying on Surveillance Videos

[[abstract]]In this paper, we present an improved version of CAMSHIFT algorithm applying on surveillance videos. A 2D, hue and brightness, histogram is used to describe the color feature of the target. In this way, videos with poor quality or achromatic points can be characterized better. The flooding process and contribution evaluation are executed to obtain a precise target histogram which reflects true color information and enhances discrimination ability. The proposed method is compared with existing methods and shows steady and satisfactory results.[[sponsorship]]Information Engineering Research Institute[[conferencedate]]20130303~20130304[[iscallforpapers]]Y[[conferencelocation]]Phuket, Thailan

Precise Tracking and Initial Segmentation of Abdominal Aortic Aneurysm

[[abstract]]In this paper we propose a mean-shift based technique for a precise tracking and segmentation of abdominal aortic aneurysm (AAA) from computed tomography (CT) angiography images. The proposed method applies median filter on the gradient of ray-length and linear interpolation for denoising. The segmentation result can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important for selection of appropriate stent graft device for treatment of AAA. Comparing to conventional approaches, our method is very efficient and can save a lot of manual labors.[[conferencetype]]國際[[conferencedate]]20131102~20131104[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Aizu-Wakamatsu, Japa

A Comparison of Feature-Combination for Example-Based Super Resolution

[[abstract]]Super resolution (SR) in computer vision is an important task. In this paper, we compared several common used features in image super resolution of example-based algorithms. To combine features, we develop a cascade framework to both solve the problem of deciding weights among features and to improve computation efficiency. Finally, we modify the framework to have an adaptive threshold such that not only the computation load is much reduced but the modified framework is suitable to any query image as well as various image databases.[[sponsorship]]the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)[[conferencetype]]國際[[conferencedate]]2014, 5/13 - 5/16[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

Robust News Video Text Detection Based on Edges and Line-deletion

[[abstract]]This paper presents a robust and efficient text detection algorithm for news video. The proposed algorithm uses the temporal information of video and logical AND operation to remove most of irrelevant background. Then a window-based method by counting the black-and-white transitions is applied on the resulted edge map to obtain rough text blobs. Line deletion technique is used twice to refine the text blocks. The proposed algorithm is applicable to multiple languages (English, Japanese and Chinese), robust to text polarities (positive or negative), various character sizes (from 4×7 to 30×30), and text alignments (horizontal or vertical). Three metrics, recall (R), precision (P), and quality of bounding preciseness (Q), are adopted to measure the efficacy of text detection algorithms. According to the experimental results on various multilingual video sequences, the proposed algorithm has a 96% and above performance in all three metrics. Comparing to existing methods, our method has better performance especially in the quality of bounding preciseness that is crucial to later binarization process.[[incitationindex]]EI[[booktype]]紙

UM-Based Image Enhancement in Low-Light Situations

[[conferencetype]]國際[[conferencedate]]20110714~20110716[[iscallforpapers]]Y[[conferencelocation]]Corfu Island, Greec

Efficient Wavelet-Based Scale Invariant Features Matching

[[abstract]]Feature points’ matching is a popular method in dealing with object recognition and image matching problems. However, variations of images, such as shift, rotation, and scaling, influence the matching correctness. Therefore, a feature point matching system with a distinctive and invariant feature point detector as well as robust description mechanism becomes the main challenge of this issue. We use discrete wavelet transform (DWT) and accumulated map to detect feature points which are local maximum points on the accumulated map. DWT calculation is efficient compared to that of Harris corner detection or Difference of Gaussian (DoG) proposed by Lowe. Besides, feature points detected by DWT are located more evenly on texture area unlike those detected by Harris’ which are clustered on corners. To be scale invariant, the dominate scale (DS) is determined for each feature point. According to the DS of a feature point, an appropriate size of region centered at this feature point is transformed to log-polar coordinate system to improve the rotation and scale invariance. To enhance time efficiency and illumination robustness, we modify the contrast-based descriptors (CCH) proposed by Huang et al. Finally, in matching stage, a geometry constraint is used to improve the matching accuracy. Compared with existing methods, the proposed algorithm has better performance especially in scale invariance and blurring robustness.[[incitationindex]]EI[[booktype]]紙

Robust Text Binarization Based on Line-Traversing for News Video

[[abstract]]This study presents a robust approach to binarize the detected rectangular text regions (text boxes) on news videos. The binarization problem can be traced back to 1970s but it is still challenging in news video today since the background is complicated and unpredictable. The proposed algorithm adopts the line traversing method integrating the edge information and the intensity statistics to accomplish the binarization task. First, Canny edge detector is applied on a text box. Next, the vertical line scanning from left to right of the text box is performed twice. The vertical line traverses downwards until it hits an edge pixel or reaches the bottom of the box. Similarly, the vertical line traverses upwards until it hits an edge pixel or reaches the top of the box. These traversed pixels are classified as background pixels. From the histogram of those non-background pixels, the peak intensity p and the standard deviation σ are evaluated. The threshold for text in news video is set to be T = (0, p+kσ) or T = (p-kσ, 255), depending on text polarity. In the case that the range of background intensity covers the entire intensity range of the image, the algorithm uses the temporal information of news video to remove most of the background. Moreover, the intensities of those background pixels, whose intensity is similar to the text pixels, are replaced by 255 or 0, depending on the text polarity. Finally, a binarization is performed in this modified text box. Notice that the proposed method is parameter-free, has no limitation on the text polarity and can handle the case of similar intensity in background and text for news video. The method has been extensively experimented on text boxes from various news videos, historical archive documents and other different documents. The proposed algorithm outperforms the well-known methods such as Otsu, Niblack, Sauvola, etc., in speed, precision and quality.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]PA

Image stylisation

[[abstract]]We propose a flexible image stylisation system for reforming a source image into a stained-glass style or some other style desired by users. It consists of three stages including image segmentation, colour or texture allocation and synthesis of calmes. Each stage provides some options for users to choose from. The system provides three options of image segmentation for the users to choose from, including automatic, semi-automatic and manual image segmentation. The automatic option randomly generates seeds in the image region, calculates the distance between each seed and each pixel and then assigns each pixel to the nearest seed region to accomplish image segmentation. The semi-automatic one segments the image according to the seeds provided by the user. The user then repeatedly adjusts the result by deleting or adding some seeds. The last version involves manual image segmentation performed by the users. The next step in segmentation is colour or texture allocation. Each segmented region is allocated with the colour or texture of reference images chosen from a pre-built database. The proposed system provides many options for this allocation, allowing the users to select from a variety of methods of texture or colour allocation. A unique feature of stained glass is that it is held together with lead, zinc, brass or copper strips called calmes. We use a back-propagation neural network to train a calme generator using an image of line drawing as the input together with an image of the corresponding calme of the line drawing as the output. The users can choose whether or not to put the synthesised calmes on the resulting images. The proposed system is easy to use and very flexible. It provides many options for users to stylise an image into a variety of stylised results. Our experiments show that the proposed system can generate a wide range of satisfactory results.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]電子版[[booktype]]紙

An Anomaly Detection by Whitening HOSF

[[abstract]]In this paper an improvement over our previous work is proposed to handle short-medium range surveillance videos. The features of histogram of oriented social force (HOSF) are the primitive building blocks to capture the interactions among people. To reduce the correlation among data, whitening procedure is applied on features. We use Bag-of-Feature (BoF) to pool HOSF in a given frame. Since our goal is to classify whether a given frame is normal and BoF, a histogram of visual words in a frame, can better represent patterns in term of frame. In the phase of building the dictionary, training BoFs are clustered and the center means are so called code words corresponding to "normal" patterns observed during the training process. A Gaussian model is constructed for distances between data and the codeword in each cluster. To decide whether a given frame is normal, the BoF feature is evaluated and the Z-score which measuring the deviation to the closest codeword is calculated. If such BoF is an outlier (i.e. High Z-score) comparing to the closest codeword, then the frame is classified "abnormal". The method is testified by the subway dataset with promising results.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]2015 3/24 - 3/27[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Gwangju, South Korea[[countrycodes]]KO