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This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the...
In order to improve the accuracy of multi-spectra remote sensing image classification, a terrain classification method based on support vector machine is proposed. A remote sensing image classification method based on SVM algorithm of C-SVC type is introduced and emphasis is put on the study of the improved SMO algorithm. In order to improve efficiency of classification, multiple-spectra remote sensing...
Edition of natural images usually asks for considerable user involvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside...
Red tides pose a significant environmental and economic threat in the Gulf of Mexico. Timely detection of red tides is important for understanding this phenomenon. In this paper, learning approaches based on k-nearest neighbors, random forests and support vector machines have been evaluated for red tide detection from MODIS satellite images. Detection results from our algorithms were compared with...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
In this paper, an iterative algorithm, which is based on support vector machine (SVM), is proposed for synthetic aperture radar (SAR) image segmentation. The proposed method considers the SAR image segmentation as the pixel classification. The pixels of the previous segmented image are regarded as the training samples for SVM, which is used to re-segment the image. These iterations are repeated until...
In this paper, a comparison between supervised change detection methods for very high geometrical resolution satellite images is considered. Methods commonly used for high and medium resolution are here confronted to the problem of exploiting very high resolution imagery, which is characterized by strong redundancy, high variances of information composing objects, collinearity and noise. Three supervised...
In this paper we present a novel method for hand gesture recognition based on Gabor filters and support vector machine (SVM). Gabor filters are first convolved with images to acquire desirable hand gesture features. The principal components analysis (PCA) method is then used to reduce the dimensionality of the feature space. With the reduced Gabor features, SVM is trained and exploited to perform...
In this paper we study object learning and recognition on a humanoid robot with foveated vision. The developed approach is view-based and can learn viewpoint-independent representations for object recognition. The training data is collected statistically and in an interactive way where a human instructor freely shows the object from a number of different viewpoints. The proposed system was fully implemented...
Information on the vehicular traffic density in an intelligent transport system (ITS) is presently obtained mainly through loop detectors (LD), traffic radars and surveillance cameras. However, the difficulties and cost of installing loop detectors and traffic radars tend to be significant. Currently, a more advanced method of circumventing this is to develop a sort of virtual loop detector (VLD)...
Maneuvering targets tracking in cluttered environment is a challenging problem in computer vision because of the difficulty of distinguishing the target from the background. In this paper, we treat tracking as a binary classification problem and employ support vector machine to suppress the background. In order to enhance the robustness against illumination changes, we propose to combine color invariance...
We propose a method to identify and localize object classes in images. Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme. To this end, we construct a classifier on the histogram of local features found in each superpixel. We regularize this classifier by aggregating histograms in the neighborhood of each...
Object detection in cluttered, natural scenes has a high complexity since many local observations compete for object hypotheses. Voting methods provide an efficient solution to this problem. When Hough voting is extended to location and scale, votes naturally become lines through scale space due to the local scale-location-ambiguity. In contrast to this, current voting methods stick to the location-only...
Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated...
The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tracking, and some online and semi-supervised algorithms are developed to handle these difficulties. In this paper, we consider tracking as a classification problem and present a novel tracking method based on boosting in a...
In this paper, we introduce a simple approach for detecting enteromorpha based on statistical learning of image features using support vector machines (SVM). The approach first classifies an enteromorpha image into two classes: enteromorpha and background. Then it extracts features from those two classes and uses them for training the SVM model. Finally, the predicting process is carried out in a...
In this paper, we propose a method for fast pedestrian detection in images/videos. Multi-scale orientated (MSO) features are proposed to represent coarse pedestrian contour, on which Adaboost classifiers are trained for pedestrian coarse location. In the fine detection, histogram of oriented gradient (HOG) features and SVM classifiers are employed to precisely classify pedestrians and non-pedestrians...
The support vector machine (SVM) was a new machine learning technique developed on the basis of statistical learning theory. It is the most successful realization of statistical learning theory. To testify the validity of SVM, this study chose the data set of hyperspectral images sensed by AVIRIS, with the band selected by Bhattacharya distance. And it added different scales of texture information...
Remote sensing has become a technique of indispensable importance for us to acquire the information on the ground. In the process of imaging, geometric distortion occurs due to several factors, which causes many difficulties when using those remote images for change detection, information fusion, resolution enhancement and so on. So the image registration is necessary. Aiming at the distortion type...
This paper presents a semantic-based video analysis method and two steps of their classification in the wushu video: Firstly, used of the image frame difference to construct on the basis of background image, and used of threshold settings to determine the body information and background information, This can achieve the elimination of background and exercise the purpose of extracting the human body...
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