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As an important complexity feature of signal, Lempel‐Ziv complexity (LZC) has the advantage of simple‐to‐calculate, but it ignores amplitude information, and has low sensitivity at low amplitude. Dispersion Lempel‐Ziv complexity (DLZC) is a recently proposed nonlinear dynamic method, it has the advantage of immunity to noise even at relatively large proportion of noise and has been used to describe...
Hormone-binding proteins (HBPs) are important soluble carriers for growth hormones, and correct recognition of HBPs is crucial to understanding their functions. Therefore, we aimed to construct an efficient and reliable classifier to identify HBPs accurately. At first, 246 proteins were collected from UniProt database and considered as the objective benchmark dataset. We employed the 8000-dimensional...
Links between issue reports and corresponding fix commits are widely used in software maintenance. The quality of links directly affects maintenance costs. Currently, such links are mainly maintained by error-prone manual efforts, which may result in missing links. To tackle this problem, automatic link recovery approaches have been proposed by building traditional classifiers with positive and negative...
Over the last decade, object-based image classification (OBIC) has become a mainstream method in remote sensing land-use/land-cover applications. Many supervised classification methods have been proposed in the OBIC framework. However, most did not use deep learning methods. In this paper, a new deep-learning-based OBIC framework is introduced. First, we segment the original image into objects by...
Cataract is one of the most prevalent causes of blindness in the industrialized world, accounting for more than 50% of blindness. Early detection and treatment can reduce the suffering of cataract patients and prevent visual impairment from turning into blindness. But the expertise of trained eye specialists is necessary for clinical cataract detection and grading, which may cause difficulties to...
In crowdsourced testing, it is beneficial to automatically classify the test reports that actually reveal a fault – a true fault, from the large number of test reports submitted by crowd workers. Most of the existing approaches toward this task simply leverage historical data to train a machine learning classifier and classify the new incoming reports. However, our observation on real industrial data...
Gait recognition has been proved useful in human identification at a distance. But view variance of gait feature is always a great challenge because of the difference in appearance. If the view of the probe is different from that of the gallery, one view transformation model can be employed to convert the gait feature from one view to another. But most existing models need to estimate the view angle...
In this study, we investigate on the learning behaviors of DNN by explicit feature transformations. As a demonstration, linear and logarithm transformations, corresponding to the amplitude spectra and log-power spectra, are compared with the same minimum mean squared error (MMSE) objective function for optimizing DNN parameters. Based on the experimental analysis of the DNN learning behaviors, we...
In crowdsourced testing, an important task is to identify the test reports that actually reveal fault — true fault, from the large number of test reports submitted by crowd workers. Most existing approaches towards this problem utilized supervised machine learning techniques, which often require users to manually label a large amount of training data. Such process is time-consuming and labor-intensive...
In this paper, an improved scale-invariant feature transform (SIFT) algorithm for synthetic aperture radar (SAR) image matching is proposed. Initially, feature descriptors based on gradient ratio (GR) are constructed by utilizing traditional SIFT method. In order to measure the matching degree between images, the similarities of the descriptors are then calculated via the symmetry kullback-leibler...
Most of the time, some traces of digital tampering may be left in image manipulation, resulting in the inconsistency of natural images and providing some clues for image forgeries. This paper proposed a novel probability model based on the first digit statistics of DCT coefficients, to express the changes of statistical properties after manipulation. And we combined Bayes' theorem to detect and locate...
On the basis of online boosting algorithm, this paper presents an optimized target tracking algorithm. Now, the commonly-used tracking algorithm usually tracks the targets as a whole, thus facing great difficulty in realizing effective tracking while the target is severely shielded. In order to surmount the difficulties resulting from shielding, this paper puts forward block-based target tracking...
This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre-training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references....
In seas security supervision, we should conduct feature extraction and match for consecutive frames in order to effectively reduce the jitter caused by wind-induced vibration. Harris corner point detection algorithm is widely used in feature extraction for images. An improved algorithm for Harris corner detection is proposed in this paper since the feature points of images with large size, high pixel...
With the emergence of mobile Internet, Internet of things and cloud computing, the domain of information security is in a rapid development. As a result, a constant stream of compound-words describing new concepts and new technologies has arisen. However, the existing dictionary does not collect those new compound-words in time, so it cannot identify them correctly. In order to solve this problem,...
In many vision problems, rotation-invariant analysis is necessary or preferred. Popular solutions are mainly based on pose normalization or brute-force learning, neglecting the intrinsic properties of rotations. In this paper, we present a rotation invariant detection approach built on the equivariant filter framework, with a new model for learning the filtering behavior. The special properties of...
Human abnormal action is an active issue in the computer vision domain. Most of the current approaches rely on spatio-temporal feature. A great deal of work has been done to show the feature with good performance, but expensive computation. There are some applications with low requirements for human abnormal action detection. We propose a new simple but low computational human abnormal action detection...
In this paper, the processing of ear identification is introduced in brief, and after making an improvement of moment invariants algorithm, six feature vectors are distilled by the method of high-order moment invariants. By using two kinds of algorithm respectively, ear image is processed to identification. In ear identification, we can learn that the high-order moment invariants in the improved algorithm...
This paper presents a novel depth recovery technique of bifocus imaging system which may produce two focus images by independent focal lengths. Depth information can be extracted from the disparity between two focus images. This paper will propose the depth recovery formula and related algorithms such as feature extraction, sub-pixel matching, and center estimation. Experiments on real scene images...
Handling numerous unordered images for scene reconstruction and categorization attracts increasing interests for commercial and scientific efforts. In this paper, we address the issue of efficient organization of content-related images from plenty of input images on several scenes with contaminated ones. First a robust view-similarity measure is proposed and the images can be categorized effectively...
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