In this paper we describe a low complexity image orientation detection algorithm which can be implemented in real-time
on embedded devices such as low-cost digital cameras, mobile phone cameras and video surveillance cameras. Providing
orientation information to tamper detection algorithm in surveillance cameras, color enhancement algorithm and various
scene classifiers can help improve their performances. Various image orientation detection algorithms have been developed
in the last few years for image management systems, as a post processing tool. But, these techniques use certain high-level
features and object classification to detect the orientation, thus they are not suitable for implementation on a capturing
device in real-time. Our algorithm uses low-level features such as texture, lines and source of illumination to detect
orientation. We implemented the algorithm on a mobile phone camera device with a 180 MHz, ARM926 processor. The
orientation detection takes 10 ms for each frame which makes it suitable to use in image capture as well as video mode. It
can be used efficiently in parallel with the other processes in the imaging pipeline of the device. On hardware, the algorithm
achieved an accuracy of 92% with a rejection rate of 4% and a false detection rate of 8% on outdoor images.
KEYWORDS: Multiplexing, Statistical analysis, Statistical multiplexing, Video, Data modeling, Expectation maximization algorithms, Data analysis, Local area networks, Video processing, Superposition
This paper examines the problem of determining the degree of mixing of two independent and different types of traffic streams from observations of their statistically multiplexed stream. A common example of a pair of such different stream types in networks would be one conforming to the conventional Poisson model and the other obeying long-range dependence characterized by a heavy-tailed distribution. We provide an expression for the probability density function of the inter-arrival time of the mixed stream in terms of those of the input streams for the general case. An approach is provided to estimate input parameters from the first and second order statistics of the output traffic for the specific case of multiplexing Poisson and heavy-tailed processes.
We present a neural network based approach to key frame extraction in the compressed domain. The proposed method is an amalgamation of both the MPEG-7 descriptors namely motion intensity descriptor and spatial activity descriptor. Shot boundary detection and block motion estimation techniques are employed prior to the extraction of the
descriptors. The motion intensity (“pace of action”) is obtained using a fuzzy system that classifies the motion intensity into five categories proportional to the intensity. The spatial activity matrix determines the spatial distribution of activity (“active regions”) in a frame. A neural network is used to pick those frames as key frames which have high intensity and maximum spatial activity at the center of the frame. Results are compared against two well-known key frame extraction techniques to demonstrate the advantage and robustness of the proposed approach. Results show that the neural network
approach performs much better than selecting first frame of the shot as a key frame and selecting middle frame of the shot as a key frame methods.
KEYWORDS: Systems modeling, Local area networks, Stochastic processes, Signal processing, Correlation function, Data modeling, Fourier transforms, Control systems, Information technology, Multimedia
The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.
KEYWORDS: Video, Systems modeling, Signal processing, Performance modeling, Data modeling, Computer programming, Scene classification, Statistical modeling, Stochastic processes, Linear filtering
Although VBR video has been characterized as self-similar by various researchers, models based on self-similarity considerations have not been previously studied. This paper investigates the application of discrete-time scale invariant systems to modeling variable-bit rate (VBR) video traces. The motivation for this study lies in the fact that the model discussed here evolves out of self-similarity considerations. Potential application of this system to classifying content-based scenes in VBR video is explored. This paper also demonstrates that using heavy-tailed stable inputs these models can match both the scene time-series correlations as well as scene density functions.
KEYWORDS: Systems modeling, Video, Data modeling, Statistical modeling, Performance modeling, Mathematical modeling, Wavelets, Analog electronics, Video processing, Video compression
It has been shown that Variable Bit Rate (VBR) video exhibits long-range dependence characteristics. Several models have been proposed to synthesize traces whose autocorrelation match that of the video traffic traces. Given the relationship between self-similarity and long- range dependence, we investigate the application of discrete-time linear scale invariant systems provided by Zhao and Rao to modeling VBR video traffic. This formulation, called a DLSI system, was derived using a continuous dilation operator in discrete-time as a direct analog of the continuous-time linear scale-invariant system formulation of Wornell and differs from other approaches such as wavelet based construction. While simulations had shown that DLSI systems were capable of generating self- similar data such as those found in network traffic, questions regarding their long-range behavior remained. Answers to these questions are dealt in this paper. We present an alternative modeling technique to model VBR video traffic using DLSI system. The proposed model has a fractional pole-zero structure and provides a good model for long-range dependent and self-similar time series. The behavioral pattern of the fractional pole-zero filter for a range of the Hurst parameter is also shown. The time-domain characteristics of the filter are analyzed using power series approximations. Comparison of output autocorrelation function (ACF) to those of the video trace ACF is made. We conclude that some samples of video data are indeed better modeled by the LSI models than the conventional models such as Markovian and Long-range dependent models.
Discrete-time linear systems that possess scale-invariance properties even in the presence of continuous dilation were proposed by Zhao and Rao. The paper presents results of subsequent investigation characterizing self-similarity properties of discrete-time signals synthesized by these systems. It is shown that white noise inputs to these linear scale invariant systems produce self-similar outputs regardless of the marginal distribution of the noise. We investigate this with different types of inputs and in most instances the outputs are fractional Gaussian and self-similar. This is confirmed by generating the fractional Gaussian noise from the fractional Brownian motion and comparing its characteristics with the system output. For heavy tailed input distributions, the output is also heavy-tailed and self-similar. It is also shown that it is possible to synthesize statistically self-similar signals whose self-similarity parameters are consistent with those observed in network traffic.
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