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In recent years, violence has considerably increased in the world. In a certain state of Brazil, for example, the homicide rate grew from 16 homicides per 100,000 inhabitants in 2000, to 48 homicides per 100,000 inhabitants in 2014. Police departments worldwide use various types of crime maps, which are generated with diverse techniques, in order to analyze and fight crime. Those types of maps enable...
Artificial fish-swarm algorithm is a novel method to search global optimum, which is typical application of behaviorism in artificial intelligence. Compared with traditional method it is more portability and stability. This paper, based on the loss function to increase the enterprise benefit, brings forward an optimized criterion of setting threshold to relieve the security officer work in the chemical...
Action recognition has been one of the most popular fields of computer vision. This paper presents a novel approach to action recognition problem using the dimension reduction method, local fisher discriminant analysis, to reduce the dimension of feature descriptors as the preprocessing step after feature extraction. We propose to use sparse matrix and randomized kd-tree to modify and accelerate the...
Human neonates show a natural predisposition towards biological motion: despite the limited visual information available, they can distinguish the movement of other living agents from object motion. This ability has been suggested to be the basis for identifying conspecifics from birth, hence representing a fundamental skill for the development of social interaction. Inspired by this, we propose a...
We study the problem of scene classification for RGB-D images in this paper. Firstly we analyze the difference between the RGB and depth images. And then based on the difference, an efficient method is implemented to make use of the RGB and depth images and make a well fusion for the RGB and depth features. Focusing on the difference of modality between the RGB and depth images, we propose a method...
We proposed a novel model to predict human's visual attention when free-viewing webpages. Compared with natural images, webpages are usually full of salient regions such as logos, text, and faces, while few of them attract human's attention in a short sight. Moreover, webpages perform distinct viewing patterns which are quite different from the natural images. In this paper, we introduced multi-features...
Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation...
The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded...
This paper presents a fast algorithm for deriving the defocus map from a single image. Existing methods of defocus map estimation often include a pixel-level propagation step to spread the measured sparse defocus cues over the whole image. Since the pixel-level propagation step is time-consuming, we develop an effective method to obtain the whole-image defocus blur using oversegmentation and transductive...
Most existing person re-identification (ReID) methods assume the availability of extensively labelled cross-view person pairs and a closed-set scenario (i.e. all the probe people exist in the gallery set). These two assumptions significantly limit their usefulness and scalability in real-world applications, particularly with large scale camera networks. To overcome the limitations, we introduce a...
Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the “right” exemplars defined and identified? This...
In complex visual recognition systems, feature fusion has become crucial to discriminate between a large number of classes. In particular, fusing high-level context information with image appearance models can be effective in object/scene recognition. To this end, we develop an auto-context modeling approach under the RKHS (Reproducing Kernel Hilbert Space) setting, wherein a series of supervised...
The disadvantages of BOW (Bag of words model) for image classification include the large amount of data in generating a codebook by clustering, redundant code words that may affect the classification results and so on. The process of BOW for the classification can be improved through the Laplace weights to improved fuzzy C means algorithm, and obtaining codebook with more ability to distinguish between...
There is growing interest in social image classification because of its importance in web-based image application. Though there are many approaches on image classification, it is a great problem to integrate multi-modal content of social images simultaneously for social image classification, since the textual content and visual content are represented in two heterogeneous feature spaces. In this study,...
Single image super-resolution (SR) generates a high-resolution (HR) image by estimating the mapping function between image patches of different resolutions. By leveraging the notion of regression, the mapping function estimation task is often transformed into predicting mapping function's derivatives. Although higher-orders of derivative lead to a more accurate mapping function, current algorithms...
With the development of stone processing and sales, effective stone surface texture image recognition methods are needed. We proposed a new stone surface texture image recognition method based on texture and colour. We combine the following visual features: Gabor features which can well simulate the single cell sensing profile of mammalian visual neurons, The Grey-level Co-occurrence Matrices(GLCM)...
Programming FPGAs has been an arduous task that requires extensive knowledge of hardware design languages (HDLs), such as Verilog or VHDL, and low-level hardware details. With OpenCL support for FPGAs, the design, prototyping and implementation of an FPGA is increasingly moving towards a much higher level of abstraction, when compared to the intrinsically low-level nature of HDLs. On the other hand,...
Blog is becoming an increasingly popular media for information publishing. Besides the main content, most of blog pages nowadays also contain noisy information such as advertisements etc. Removing these unrelated elements can improves user experience, but also can better adapt the content to various devices such as mobile phones. Though template-based extractors are highly accurate, they may incur...
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD...
Although the graph-based machine learning has received considerable attention in the remote sensing area and it has been widely used for terrain classification, the construction of graph in most existing algorithms still takes large memory and plenty of computational time especially for large Polarimetric Synthetic Aperture Radar (PolSAR) data. Addressing these issues, we propose a fast semi-supervised...
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