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A malt is one of intermediate ingredients for a brewing industry. The quality of barley used for malting have essential impact on the final product flavor. An automatic system for a barley grains inspection, utilizing computer vision methods, can provide an objective quality assessment. We present image preprocessing steps of grain inspection system. Main preprocessing steps are: segmentation of grain...
Background Subtraction is the major important step in many image processing applications which can be applied in much of video surveillances. The major result of this method is accuracy as well as processing time. So we mainly focused on these two challenges. We parallelized the Two Layered CodeBook Model on Graphical Processing Unit (GPU) for increasing the processing speed and the accuracy of the...
Recently, Scientists needs to know the behavior of fish populations in underwater. Previously many algorithms are used but they are suffered in complex textures and low detection rate. This paper proposed a multi threading fuzzy c-mean (MFC mean) approach to detect multi-moving fishes in a noisy and dense condition. In this approach, we combines the multi threaded parallel (MTP) approach and kernel...
Bird strikes present a huge risk for air vehicles, especially since traditional airport bird surveillance is mainly dependent on inefficient human observation. Recently, multiple groups have proposed bird monitoring using computer vision to detect birds, determine bird flying trajectories, and predict aircraft takeoff delays. However, the characteristics of bird flight using imagery which should be...
We develop a new paradigm for designing fully streaming, area-efficient FPGA implementations of common building blocks for vision algorithm. By focusing on avoiding redundant computation we achieve a reduction of one to two orders of magnitude reduction in design area utilization as compared to previous implementations. We demonstrate that our design works in practice by building five 325 frames per...
An efficient algorithm for shadow and highlight removal in nonparametric moving object detection strategies is proposed. By the nonparametric modeling of the variations of the brightness and the chromaticity along the sequences, shadows and highlights are identified. In this way, the quality of the detections is significantly improved.
In traffic video surveillance system, target-level tracking and feature-level tracking are two important areas for research. Therefore, the combination between them is an interesting question. Mean-shift is a traditional target-level tracking algorithm with no adaptation to vehicle scale and orientation change. In order to solve the problem, algorithm combine SURF (speed-up robust feature) feature...
In recent years, researchers have paid more and more attention to computer vision, pattern recognition and human-computer interaction. Recognizing barcode in real-time video is an embodiment of these techniques. This paper presents an algorithm which can locate barcode region in complex background through region-based image analysis. Being different from traditional region-based image analysis, the...
The traditional fixed bandwidth Mean-Shift Tracking algorithm can not have an effective tracking for any changes in targets. Based on the current methods, the tracking are particularly effective on gradually increase or decrease in size, there has no Mean-shift Tracking fit the size in any change. On the basic of this, we propose a combination of traditional algorithm bandwidth adaptive algorithm...
Efficient learning from massive amounts of information is a hot topic in computer vision. Available training sets contain many examples with several visual descriptors, a setting in which current batch approaches are typically slow and does not scale well. In this work we introduce a theoretically motivated and efficient online learning algorithm for the Multi Kernel Learning (MKL) problem. For this...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
In this, paper general solutions for nonlinear non-negative component analysis for data representation and recognition are proposed. Motivated by a combination of the non-negative matrix factorization (NMF) algorithm and kernel theory, which has lead to a recently proposed NMF algorithm in a polynomial feature space, we propose a general framework where one can build a nonlinear non-negative component...
In this paper, we explore the key factors in the design and implementation of visual computing (image processing and computer vision) algorithms on the massive parallel GPU (graphics processing units). The goal of the exploration is to provide common perspective and guidelines of using GPU for visual computing applications. We have selected three nontrivial applications (multiview stereo matching,...
The problem of finding a match for an image (`template') within a larger image is known as template matching. It is key to a variety of computer vision applications. Currently known template matching algorithms run in fixed time, or are guaranteed to find the best match. We present a novel algorithm which in many cases can guarantee that the best match is found. In other cases it finds a good approximation...
The mean-shift algorithm is very useful in object tracking for its many advantages, such as good performance in real-time tracking, nonparametric density model, etc. Although the scale of the mean-shift kernel is a crucial parameter, there exists presently still no clear mechanism in choosing or updating the scale when the kernel of changing size is tracked. In this paper, a new method is introduced...
Linear discriminant analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data,...
Robust object tracking is quite important in computer vision. In this paper, a novel tracking approach for single object which combines genetic algorithm and Kalman filter is proposed. Genetic algorithm is introduced and reasonably applied to find the tracked object in a search area. A further step called multi-blocks voting is exploited for obtaining more accurate object localization. Kalman filter...
As a nonparametric statistical method, the mean shift algorithm has recently attracted much attention in the computer vision community due to its efficiency in motion tracking and clustering analysis. Its convergence rate is, however, slow around the convergence point. One way to tackle this problem is to switch the search mechanism to Newtonpsilas method which has a quadratic order of convergence...
We propose an efficient method for complex optimization problems that often arise in computer vision. While our method is general and could be applied to various tasks, it was mainly inspired from problems in computer vision, and it borrows ideas from scale space theory. One of the main motivations for our approach is that searching for the global maximum through the scale space of a function is equivalent...
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