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Automatic vegetation coverage detection plays a key role for monitoring and management of land usage, environmental variation, and urban planning. This paper presents a novel vegetation coverage detection technique for very high resolution multi-spectral satellite imagery. The proposed technique consists of two stages including a supervised patch-level scoring stage and an unsupervised pixel-level...
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous...
Human activity recognition is a fundamental problem in computer vision with many applications such as video retrieval, automatic visual surveillance and human computer interaction. Sports represent one of the most viewed content on digital tv and the web. Automatically collected statistics of team sports game play represent actionable information for many end users such as coaches and broadcast speakers...
In order to show the scientific status of Harbin Engineering University (HEU) in recent five years, the scientific papers published by the staff in HEU in the period of 2008–2012 were extracted out from the SCI database. These papers were processed by the CiteSpaceII software, which is a visual software for Bibliometrics. The kernel researchers, kernel fields, and the kernel institutions and countries...
We present a study of a support vector machine (SVM) application to brain-computer interface (BCI) paradigm. Four SVM kernel functions are evaluated in order to maximize classification accuracy of a four classes-based BCI paradigm utilizing a code-modulated visual evoked potential (cVEP) response within the captured EEG signals. Our previously published reports applied only the linear SVM, which already...
In recent yeas, VLAD has been used to represent an image effectively and efficiently by just a few bytes in large-scale image retrieval. In spite of its remarkable performance, a series of modification methods have been presented. In addition, the redundancy between the features corresponding to the same cluster center could be improved. In this paper, a regional PCA Whitening method is proposed to...
Automatic detection of necrosis in histological images is an interesting problem of digital pathology that needs to be addressed. Determination of presence and extent of necrosis can provide useful information for disease diagnosis and prognosis, and the detected necrotic regions can also be excluded before analyzing the remaining living tissue. This paper describes a novel appearance-based method...
Breast cancer has caused more and more attention in recent years since the mortality rate is increasing and age of onset is trend to be younger than before. Using computer vision technology for automatic classifying benign and masses malignant ones could assist doctors in diagnosing condition. However, the margins and shapes of masses are various and which are very similar with surrounding tissues,...
This paper focuses on the combination of StochasticPetri Net (SPN) graphs for event association in the context ofNatural Languages Understanding (NLU). Our general goal isto develop a new NLU methodology. In this paper we presentsome of its components which are: the use of AnaphoraResolution (AR), the extraction of kernel(s) based on thestructure of their parse trees, and the synthesis of SPN graphs...
This paper presents a novel object tracking algorithm. Object appearance and spatial information is learned from a single template using a non-linear subspace projection. A probabilistic search strategy, based on particle filter, is employed to find object region in each frame of the video sequence that best models the target object in the subspace representation. Particle filter estimates the posterior...
The performance of call graph analytics is critical to the development and management of large-scale software systems. Classical call graph visualizers either reuse a plain node-link graph metaphor that does not scale, or adopt the specific aggregation technique like the PivotGraph. In this paper, we first generalize the OLAP analysis framework from multidimensional data to multivariate graphs. Then...
Classification of high-resolution remote-sensing images is a challenging research area. In this paper we proposed a novel decision fusion framework to combine bag of features (BOF) based classifiers. The proposed framework, can also be used in multi category image classification applications. A single voting algorithm is used for decision fusion and an ambiguity detection module is used to determine...
In this paper, we present, first, a new method for color feature extraction based on SURF detectors. Then, we proved its efficiency for flower image classification. Therefore, we described visual content of the flower images using compact and accurate descriptors. These features are combined and the learning process is performed using a multiple kernel framework with a SVM classifier. The proposed...
The Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of-words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose...
This paper surveys the learning algorithms of visual features representation and the computational modelling approaches proposed with the aim of developing better artificial object recognition systems. It turns out that most of the learning theories and schemas have been developed either in the spirit of understanding biological facts of vision or designing machines that provide better or competitive...
As integration of depth sensing into mobile devices is likely forthcoming, we investigate on merging appearance and shape information for mobile visual search. Accordingly, we propose an RGB-D search engine architecture that can attain high recognition rates with peculiarly moderate bandwidth requirements. Our experiments include a comparison to the CDVS (Compact Descriptors for Visual Search) pipeline,...
In this paper, we aim at improving the discriminative jointly dictionaries for large-scale image classification. Sparse representation is a popular tool for image classification. Visual dictionary is very critical to the classification performance. A visual tree is constructed according to the visual similarity, in which the higher layer represents the coarser membership and the lower layer represents...
The research of affine scale space is to create a more general approach to the affine invariant image scale representation by modifying the corresponding Gaussian filters in order to cope with the specific change of view point. It has the purpose to retain a linear relationship with the transiting of the view point. With this linear relationship, the affine scale space could be established as a more...
Robust scale calculation is a challenging problem in visual object tracking. Most state-of-the-art trackers fail to handle large scale variations in complex image sequences. This paper propose a novel approach for robust scale calculation in a tracking-by-detection framework. The proposed approach divides the target into four patches and computes the scale factor by finding the maximum response position...
In this paper, a straightforward and effective method for image upsizing is presented; upsizing is considered to be an essential operation in image processing. The Wiener filtering method is a well-known optimal framework for signal prediction, which provides information of original and degraded signals. In image upsizing, the Wiener filtering framework is not valid because of the missing information...
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