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Inter prediction based on block matching motion estimation is important for video coding. But this method suffers from the additional overhead in data rate representing the motion information that needs to be transmitted to the decoder. To solve this problem, we present an improved implicit motion information inter prediction algorithm for P slice in H.264/AVC based on the spatio-temporal adaptive...
We proposed fuzzy inference schemes to address the changes of the lighting environment problems: the illumination of the images captured from camera installed on a moving vehicle also varies from frame to frame. First, the input image is checked with a fuzzy inference method to evaluate the illumination conditions in order to apply appropriate preprocessing operations to get a better result. To overcome...
In this paper we implement a vision based moving Object Tracking system with Wireless Surveillance Camera which uses a color image segmentation and color histogram with background subtraction for tracking any objects in non-ideal environment. The implementation of the moving video objects can be based on any one of the tracking algorithms such as Template matching, Continuously Adaptive Mean Shift...
The design of traffic sign recognition (TSR) system, one important subsystem of Advanced Driver Assistance System (ADAS), has been a challenge practical problem for many years due to the complex issues like road environments, lighting conditions, occlusion, and so on. In this paper, we introduce a new TSR system, whose effectiveness has been tested through extensive experiments. The established TSR...
In this paper we propose a framework of topic modeling ensembles, a novel solution to combine the models learned by topic modeling over each partition of the whole corpus. It has the potentials for applications such as distributed topic modeling for large corpora, and incremental topic modeling for rapidly growing corpora. Since only the base models, not the original documents, are required in the...
Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results...
This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored...
Shadow detection is a critical issue for most applications of video surveillance. In this study, we present an object-wise online learning method to detect casting shadows without providing any priori scene information or threshold parameters. Hue, saturation, and intensity- difference histograms of moving objects are collected to learn a cumulative distribution separately. The accumulating strategy...
In this work we present a new approach for learning a layered stacked graphical model for the problem of visual object detection and segmentation. It is obvious that visual objects can be represented by multiple feature cues, such as color, texture, shape. The idea is to treat different feature types in different processes for learning classifiers and then integrate them into a unified model. We employ...
The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn...
In the presented research on codebook optimization for vector quantization, an associative memory architecture is applied, which searches the most similar data among previously stored reference data. For realizing the learning function of new codebook data, a learning algorithm is implemented, which is based on this associative memory and which imitates the concept of the human short/long-term memory...
The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises...
We propose a full-text search technique for image-scanned documents that does not recognize individual characters. The system is as fast as a full-text search of machine-readable documents. Such a system is important when working with historical handwritten manuscripts. The proposed method works independently of differences in language and font because it uses a new pseudo-coding scheme based on the...
Nonnegative Matrix Factorization (NMF) has been widely used in dimensionality reduction, machine learning, and data mining, etc. It aims to find two nonnegative matrices whose product can well approximate the nonnegative data matrix, which naturally lead to parts-based representation. In this paper, we present a family of projective nonnegative matrix factorization algorithm, PNMF with Bregman divergence...
Many consumer digital cameras support dual shooting mode of both low-resolution (LR) video and high-resolution (HR) image. By periodically switching between the video and image modes, this type of cameras make it possible to super-resolve the LR video with the assistance of neighboring HR still images. We propose a model-based video super-resolution (VSR) technique for the above dual-mode cameras...
Recent studies have shown that convolutional networks can achieve a great deal of success in high-level vision problems such as objection recognition. In this paper, convolutional networks are used to solve a typical low-level image processing task, image segmentation. Here, the convolutional networks are trained using gradient descent techniques to solve the problem of segmenting the cell nuclei...
Wu and coworkers introduced an active basis model (ABM) for detecting generic objects in static images. A grey-value local power spectrum was utilized to find a common template and deformable templates from a set of training images and to detect an object in unknown images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short) which includes color...
In this paper, we propose a manifold-based methodology for color constancy. It is observed that the center surround information of an image creates a manifold in color space. The relationship between the points in the manifold is modeled as a line. The human visual system is capable of learning these relationships. This is the basis of color constancy. In illumination correction, the image in the...
We present a shape-first approach to finding automobiles and trucks in overhead images and include results from our analysis of an image from the Overhead Imaging Research Dataset [1]. For the OIRDS, our shape-first approach traces candidate vehicle outlines by exploiting knowledge about an overhead image of a vehicle: a vehicle's outline fits into a rectangle, this rectangle is sized to allow vehicles...
In this paper, we proposed AS3C-N algorithm, a method of adaptive semi-supervised spectral clustering based on Nyström approximation, and apply it to color image classification. Firstly, Introduction and analysis of spectral grouping using the Nyström method are given. compared with NJW spectral clustering, Nyström approximation can reduce the requirements for computer time and space; Secondly,...
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