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The objective of the proposed work is object position estimation, in which the system, after training with examples of images including objects such as cars, should be capable of indicating accurately by coordinates. The method is different from simple object detection, since it uses the context, i.e. the whole image. The key idea is to take an approach with Relevance Vector Machine (RVM) since it...
In batch learning all the training examples have to be available at once to train the model, which often leads to slow performance and large memory requirements. Little work has been done in developing incremental object learners. In this paper, we present an incremental method that finds corresponding points of similar object instances, appearing in natural grayscale images with arbitrary location,...
A semi-supervised graph-based approach to target detection is presented. The proposed method improves the Kernel Orthogonal Subspace Projection (KOSP) by deforming the kernel through the approximation of the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a hyperspectral image target detection application for thermal hot spot detection...
Multiple-extremum issue including the well-known ??singularity?? problem is one of the major defects in kernel-based object tracking. This paper studies this important problem and presents a novel approach called section-based tracking (SBT) that is based on the section information provided by the division of the object's weight image. This approach serves to eliminate fake extremal points and make...
Improving the precision of shot boundary detection is very important. This paper presents an algorithm for shot boundary detection based on SVM (support vector machine) in compressed domain. It uses the features, such as the type of macroblock, the difference between DC coefficients of two co-located blocks in successive frames and the type of frame, to segment a video into the shots by classifying...
Object detection is an important function for intelligent multimedia processing, but its computational complexity prevented its pervasive uses in consumer electronics. Cost-effective & energy-efficient computations are now available with various innovative multicore architectures proposed for embedded systems. However, extensive software optimizations are needed to unravel the inherent parallelisms...
The strategies for the preservation of historical documents can include their digitization, which is an effective way to make them publicly available while preventing degradation of the original sources. The Arquivo Publico Mineiro (APM), the Archives of the State of Minas Gerais, has a collection of historical photographs from Brazil, and some of them have been digitized. The availability of digital...
The paper presents a method for efficient text detection in unconstrained environments, based on image features derived from connected components and on a classification architecture implementing a focus of attention approach.The main application motivating the work is container code detection with the final goal of checking freight trains composition. Although the method is strongly influenced by...
In this paper, a new integrated particle filter is proposed for video object tracking. After particles are generated by importance sampling, each particle is regressed on the transformation space where the mapping function is learned offline by regression on pose manifold using Lie algebra, leading to a more effective allocation of particles. Experimental results on synthetic and real sequences clearly...
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...
SSD-based object tracking has shown its improved performance compared with mean-shift and many people have made further improvements based on it. However, how kernels should be designed to better cooperate with SSD metric and Newton-style iteration remains unsolved. Our work is to find out the underlying principles for SSD kernel design, which can help make the tracker more sensitive to the object...
We present a method to detect characters on signboards in natural scene images. For many applications, both classifier with small computational cost and the efficient feature set, which gives rise to accurate recognition are required. Texture based features are often used for target detection. It has been also shown that the shape of the intensity distribution is often useful for character extraction...
Kernel-based tracker shows robust performances in various object tracking technologies. Due to its robustness and accuracy, kernel-based tracker using mean-shift algorithm is regarded as one of the best ways to apply in object tracking technology in computer vision fields. However, it fails tracking when faced with a speedy object moving beyond its window size within one image frame interval time...
Mass in mammogram can be an indicator of breast cancer. In this work we propose a new approach using twin support vector machine (TWSVM) for automated detection of mass in digital mammograms. This algorithm finds two hyperplanes to classify data points into different classes according to the relevance between a given point and either plane. It works much faster than original SVM classifier. The proposed...
Content-based multimedia database indexing and retrieval tasks require automatic extraction of descriptive features that are relevant to the subject materials i.e., images, video etc. The typical low-level features that are extracted in images and video include measures of color, texture, or shape. Although these features can easily be obtained, they do not give a precise idea of the image content...
An improved, intelligent pedestrian counting system, using images obtained from a single video camera, is described in this paper. This system is capable of detecting and counting a group of pedestrians in the region of interest. Groups can be extracted by using the image processing method, and a kernel-induced probabilistic neural network (KPNN) employed to perform the classification, and estimate...
Traditional background subtraction methods model only temporal variation of each pixel. However, there is also spatial variation in real word due to dynamic background such as waving trees, spouting fountain and camera jitters, which causes the significant performance degradation of traditional methods. In this paper, a novel spatial-temporal nonparametric background subtraction approach (STNBS) is...
Traditional mean shift tracking algorithm set weight value of pixels according to the distance between pixel and center of model. But it is obviously unreasonable during the tracking of asymmetric or non-rigid object, such as human. In this paper, a novel adaptive weight values updating mean shift tracking algorithm is proposed, weight value of every pixel is updated according to variation of motion...
In this paper we present a method for learning class-specific features for recognition. Recently a greedy layer-wise procedure was proposed to initialize weights of deep belief networks, by viewing each layer as a separate restricted Boltzmann machine (RBM). We develop the convolutional RBM (C-RBM), a variant of the RBM model in which weights are shared to respect the spatial structure of images....
There has been significant research into the development of visual feature detectors and descriptors that are robust to a number of image deformations. Some of these methods have emphasized the need to improve on computational speed and compact representations so that they can enable a range of real-time applications with reduced computational requirements. In this paper we present modified detectors...
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