Browsing by Subject "Image Processing"

Now showing 1 - 8 of 8

Open Access
3-dimensional median filters for image sequence processing
(IEEE, 1991-04) Alp, M. Bilge,; Neuvo, Y.
Two 3-D median-based filtering algorithms have been developed that preserve the motion in the image sequence while attenuating noise effectively. Some observations are made on the root signals in binary domain based on the positive Boolean functions corresponding to the filters. From the Boolean expressions the output distribution functions are derived. The performance of both filters under various noise types is examined theoretically and experimentally. The structures are simulated on a video sequencer (DVSR 100) on real image sequences. Comparisons are made with other 2- and 3-D algorithms from the literature based on mean square error, mean absolute error, and subjective criteria.
Open Access
Automatic determination of navigable areas, pedestrian detection, and augmentation of virtual agents in real crowd videos
(2018-12) Doğan, Yalım
Crowd simulations imitate the behavior of crowds and individual agents in the crowd with personality and appearance, which determines the overall model of a multi-agent system. In such studies, the models are often compared with real-life scenarios for assessment. Yet apart from side-by-side comparison and trajectory analysis, there are no practical, out-of-the-box tools to test how a given arbitrary model simulate the scenario that takes place in the real world. We propose a framework for augmenting virtual agents in real-life crowd videos. The framework locates the navigable areas on the ground plane using the automaticallyextracted detection data of the pedestrians in the crowd video. Then it places the three-dimensional (3D) models of real pedestrians in the 3D model of the scene. An interactive user interface is provided for users to add and control virtual agents, which are simulated together with detected real pedestrians using collision avoidance algorithms.
Open Access
Cepstrum based feature extraction method for fungus detection
(SPIE, 2011) Yorulmaz, Onur; Pearson, T.C.; Çetin, A. Enis
In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Open Access
A general purpose VLSI median filter and its applications for image processing
(IEEE, 1989) Karaman, Mustafa; Onural, Levent; Atalar, Abdullah
A general-purpose median filter configuration consisting of two single-chip median filters is proposed. One of the chips is designed for applications requiring variable word-length and variable window size, whereas the other is for real-time applications. The architectures of the chips are based on odd/even transposition sorting. The chips are implemented in 3-μm M2CMOS using full-custom VLSI design techniques. The chips together with a reasonable external hardware can be used for the realizations of many median filtering techniques. The VLSI design procedure of the chips and their applications to different median filtering techniques for image processing are presented.
Open Access
Generating connected textured fractal patterns using Markov random fields
(Institute of Electrical and Electronics Engineers, 1991) Onural, L.
An algorithm that yields textured and connected binary fractals is presented. The texture is imposed by modeling the fractal as a Markov random field (MRF) at every resolution level. The model size and the parameters specify the texture. The generation starts at a coarser level and continues at finer levels. Connectivity, which is a global property, is maintained by restricting the flow of the sample generating Markov chain within a limited subset of all possible outcomes of the Markov random field. The texture is controlled by the parameters of the MRF model being used. Sample patterns are shown.
Open Access
New radix-2-based algorithm for fast median filtering
(IEEE, 1989) Karaman, M.; Onural, L.
A fast radi-2-based median filtering algorithm is proposed. The median is determined bit-by-bit successively by eliminating the samples whose previous bits are different to that of the median. The intermediate computations of the algorithm do not involve any array computation, nor any memory. The worst-case computational complexity of the algorithm is O(w) for w samples.
Open Access
A parallel scaled conjugate-gradient algorithm for the solution phase of gathering radiosity on hypercubes
(Springer, 1997) Kurç, T. M.; Aykanat, Cevdet; Özgüç, B.
Gathering radiosity is a popular method for investigating lighting effects in a closed environment. In lighting simulations, with fixed locations of objects and light sources, the intensity and color and/or reflectivity vary. After the form-factor values are computed, the linear system of equations is solved repeatedly to visualize these changes. The scaled conjugate-gradient method is a powerful technique for solving large sparse linear systems of equations with symmetric positive definite matrices. We investigate this method for the solution phase. The nonsymmetric form-factor matrix is transformed into a symmetric matrix. We propose an efficient data redistribution scheme to achieve almost perfect load balance. We also present several parallel algorithms for form-factor computation.
Open Access
Visual object tracking using co-difference features
(2017-08) Demir, Hüseyin Seçkin
Visual object tracking has been one of the widely studied computer vision tasks which has a broad range of applications in various areas from surveillance to medical studies. There are different approaches proposed for the problem in the literature. While some of them use generative methods where an appearance model is built and used for localizing the object on the image, others use discriminative approaches that models the object and background as two different classes and turns the tracking task into a binary classification problem. In this study, we propose a novel object tracking algorithm based on co-difference matrix and compare its performance with the recent state-of-the-art tracking algorithms on two specific applications. Experiments on a large class of datasets show that the proposed co-difference based object tracking algorithm has successful results in terms of track maintenance, success rate and localization accuracy. The proposed algorithm uses co-difference matrix as the image descriptor. Extraction of co-difference features is similar to the well known covariance method. However the vector product operator is redefined in a multiplication-free manner. The new operator yields a computationally efficient implementation for real time object tracking applications. For our experiments, we prepared a comparison framework that contains over 70000 annotated images for visual object tracking task. We conducted experiments for two different application areas seperately. The first one is infrared surveillance sytems. For this application, we used a thermal image dataset that contains various objects such as humans, cars and military vehicles. The second application area is cell tracking on time-lapse microscopy images. Image sequences for the second application contain cells of different shapes and sizes. For both applications, datasets include a considerable amount of rotation and background clutter. Performance of the tracking algorithms are evaluated quantitatively based on three different metrics. These metrics measure the track maintenance score, success rate and localization accuracy of an algorithm. Experiments indicate that the proposed co-difference based tracking algorithm is among the best performing methods by having the highest localization accuracy and success rate for the surveillance dataset, and the highest track maintenance score for the cell motility dataset.