Automatic recognition of road distresses has been an important research area since it reduces economic loses before
cracks and potholes become too severe. Existing systems for automated pavement defect detection commonly require
special devices such as lights, lasers, etc, which dramatically increase the cost and limit the system to certain
applications. Therefore, in this paper, a low cost automatic pavement distress evaluation approach is proposed. This
method can provide real-time pavement distress detection as well as evaluation results based on the color images
captured from a camera installed on a survey vehicle. The entire process consists of two main parts: pavement surface
extraction followed by pavement distress detection and classification. In the first part, a novel color segmentation
method based on a feed forward neural network is applied to separate the road surface from the background. In the
second part, a thresholding technique based on probabilistic relaxation is utilized to separate distresses from the road
surface. Then, by inputting the geometrical parameters obtained from the detected distresses into a neural network
based pavement distress classifier, the defects can be classified into different types. Simulation results are given to
show that the proposed method is both effective and reliable on a variety of pavement images.
We consider the robust packetization scheme for set partitioning in hierarchical trees (SPIHT) embedded bit stream over packet erasure channels and propose a fast and robust fixed-length packetization scheme using a two-step process. In the first step, a multiplexer is used to partition the embedded bit stream into multiple variable-length blocks with block sizes close to the fixed packet size. In the second step, a packetized error-resilient entropy code (PEREC) algorithm is proposed to reorganize these variable-length blocks into fixed-size packets to be transmitted over the physical network. Any lost packets have only minor effects on other received packets. Computer simulations show that the proposed method provides both excellent compression performance with fast speed and graceful degradation under serve packet losses.
A wavelet-based robust image coding technique for image transmission over noisy channels is presented here. The system combines classification and optimal bit allocation along with robust quantization to produce an inherent error-resistant bitstream without channel coding. In order to improve coding efficiency, an iterative algorithm is developed for implementing optimal bit allocation involving both rate distortion and channel optimization. Also, a modified channel optimized uniform trellis coded quantization (TCQ) with a low computational complexity, yet an effective performance, is proposed and used to provide robust quantization. The system achieves both efficient image compression and robust image transmission over noisy channels. Computer simulations indicate that the proposed scheme is effective, even under mismatched channel conditions, and the performance is competitive with other robust image compression techniques.
We present a competitive learning vector quantization with evolution strategies for image compression. This technique embeds evolution strategies (ES) into the standard competitive learning vector quantization algorithm (CLVQ). After each iteration during the CLVQ training process, the so-far generated codebook is adjusted by the embedded ES through its recombination, mutation, and selection process. The proposed algorithm can efficiently overcome CLVQ's problems of under-utilization of neurons and initial codebook dependency. The embedding of ES greatly increases the algorithm's capability to avoid local minimums, leading to a global optimization. Experimental results demonstrate that it can obtain significant improvement over CLVQ and other comparable algorithms in image compression applications, especially when it involves larger codebooks.
This paper presents a learning based codebook design algorithm for vector quantization of digital images using evolution strategies (ES). This technique embeds evolution strategies into the standard competitive learning vector quantization algorithm (CLVQ) and efficiently overcomes its problems of under-utilization of neurons and initial codebook dependency. The embedding of ES greatly increases the algorithm’s capability of avoiding the local minimums, leading to global optimization. Experimental results demonstrate that it can obtain significant improvement over CLVQ and other comparable algorithms in image compression applications. In comparison with the FSLVQ and KSOM algorithms, this new technique is computationally more efficient and requires less training time.
A predictive tree structure is presented for classifying the wavelet coefficients, and a new scheme is proposed to construct the trees based on rate distortion function, including both the optimal hierarchical quadtree construction and the predictive spatial orientation tree development. The full search quadtree optimization is applied first to the highest level of high frequency subbands, exploiting the intrasubband correlation of wavelet coefficients. The generated optimal quadtree serves as a predictor to construct the trees for other lower level subbands in which only the leaf nodes are to be analyzed in terms of the associated Lagrange costs for further expansion, taking advantage of the self-similarity across subbands. Constraining the full search quadtree optimization within the highest level subband reduces the computational complexity significantly. Simulation results indicate the proposed scheme is efficient and the performance of the system is comparable to some of the popular image compression techniques.
One of the major disadvantages of the standard iterative image restoration is its linear rate of convergence. In this paper it is shown that for natural scenes and uniform space invariant distortion, we can attain acceleration in the iteration process. It has been shown here that, for standard iterative restoration, if we choose the gain parameter close to but less than its upper limit and then after some iterations reduce it to exactly half of its upper limit, a close estimation of the original image can be obtained in that particular iteration itself. This is due to the fact that the iterative step vectors get closer to the original image vector as the iteration progresses, with a linear rate of convergence. The advantage of using this approach is that the iteration process can be accelerated in any desired iteration. Reducing the gain parameter in an early stage of the iteration process can save processing time at the cost of accuracy. On the other hand, if we choose to reduce the gain parameter after an increased number of iterations, we can obtain a more accurate result using more processing time. This is a result of the fact that the angle between the iterative step vector and the original image vector approaches zero as we increase the number of iterations.
A neural network based image enhancement method is introduced to improve the image resolution from a sequence of low resolution image frames. Most of the existing methods reconstruct a high-resolution image from a multiple of low-resolution image frames by minimizing some established cost function using a mathematical technique. This method, however, uses an integrated recurrent neural network (IRNN) that is particularly designed to be capable of learning an optimal mapping from a multiple of low-resolution image frames to a high-resolution image through training. The IRNN consists of four feed-forward sub-networks working collectively with the ability of having a feedback of information from its output to input. As such, it is capable of both learning and searching the optimal solution in the solution space leading to high resolution images. Simulation results demonstrate that the proposed IRNN has good potential in solving image resolution enhancement problem, as it can adapt itself to the various conditions of the reconstruction problem by learning.
This paper describes the study of wavelet-based methods employed to de-noise a force transducer signal. This signal was extracted during the extensional deformation of a non-Newtonian polymer fluid. The non-Newtonian polymeric fluid was extensionally deformed with an exponentially increasing velocity profile. This velocity profile corresponded to a specific strain rate. Since the motion was stopped quickly (deceleration time was below 50ms for a complete stop), a serious problem of ringing occurred for approximately one second after the motion has ceased. The ringing manifested itself as a damped harmonic oscillation, which overrides the relaxation characteristics of the molecular structure within the boger fluid. In this paper, our goal was to suppress the damped harmonic oscillatory signal while preserving the relaxation characteristics (decaying exponential signal) of the force data. Several wavelet-based techniques provided acceptable noise suppression while preserving the signal of interest.
In this paper, we describe the design and development of a high sensitivity, large dynamic range force transducer capable of measuring transient force changes in tension and compression. Conventional force transducers typically rely on the deformation of strain gauges, or on servo-mechanical load cells. While strain gauge transducers exhibit a rapid response time, they are subject to electrical noise, and typically have a minimum useful limit of approximately 10-5 N. Servo-mechanical transducers have poor response times and exhibit compliance in the axis of deformation that is unacceptable for many applications. The research objective is to develop a novel force transducer based on the change in optical properties with loading of a pre-stressed polymer. The concept utilizes a pre-stressed polymer material as a linkage to which a force would be applied either in compression or tension. The molecular deformation of the polymer linkage will be analyzed using miniature optical components arranged as a phase-modulated polarimeter capable of birefringence measurements on the order of 10-9. Calibration of the measured birefringence with known loads provides the necessary calibration parameters. The instrument is capable of directional force measurements and is extremely accurate for measuring low-level forces. Since the force transducer is based on optical techniques, it would be resistant to electronic noise, and would allow measurement of rapidly changing loads. The best available force transducers capable of measuring transient responses have a lower resolution of approximately 10-5 N. Research with the rheology of fluids, transient flows of pharmaceuticals in combinatorial research, biological tissue response, and biomimetic adhesive research often require force measurements below this range. Although ultra-microbalances exist that have sensitivities well below this range, the averaging techniques employed that allow these measurements make them unsuitable for transient flows, as does the physical size of the systems.
Wavelet transform which provides a multiresolution representation of images has been widely used in image and video compression. An investigation of wavelet decomposition reveals the cross-correlation among subimages at different resolutions. To exploit this cross-correlation, a new scheme using classified vector quantization to encode wavelet coefficients is proposed in this paper. The original image is first decomposed into a hierarchy of three layers containing ten subimages by discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantized since it contains most of the energy of the wavelet coefficients. All high frequency subimages are vector quantized to utilize the cross-correlation among different resolutions. Vectors are constructed by combining the corresponding coefficients of the high frequency subimages of the same orientation at different resolutions. Classified vector quantization is used to reduce edge distortion and computational complexity. Computer simulations are carried out to evaluate the performance of the proposed method.
The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and lattice vector quantization is presented. The input image is first decomposed into a hierarchy of three layers containing 10 subimages by discrete wavelet transform. The lowest resolution low-frequency
subimage is scalar quantized with 8 bits/pixel. High-frequency subimages are encoded by lattice vector quantization. A pyramidal piecewise uniform companding approach is used to design the lattice
quantizer according to a piecewise constant approximation to the probability density function of the input source. Due to the fast algorithm of lattice quantization, computational complexity is greatly reduced as compared to the vector quantizers based on the Linde- Buzo-Gray (LBG) algorithm. Computer simulations show that the proposed coding scheme can achieve a high compression ratio while maintaining good reconstruction image quality (both objectively and subjectively).
This paper exploits three correlation patterns that exist in the discrete wavelet transform (DWT) coefficients of an decomposed image. DWT is known as a very useful transform for image compression. Since the correlation patterns are among the DWT coefficients, they are post-DWT redundancy. By reducing this redundancy, quite significant improvement can be obtained as shown in this paper. In the real image world, edges which are discontinuities are very important in presenting an image. DWT on edges is not as efficient as it is on smooth areas, so some correlated DWT residuals around an edge can be observed in our experiments. This is the most important reason why the post-DWT redundancy exists. To make use of this redundancy, two useful techniques are employed in this paper. They are the Magnitude Partition and the Coordinate Splitting. The first one does not increase data entropy while the second one could reduce data entropy. The combination of this two techniques is the key idea to the schemes of this paper. Since this post-DWT redundancy has not been well pointed out in the current literacy, the novelty of this paper is to give an overall examination on it and to provide the useful schemes to reduce it.
A new motion compensated predictive coding method for an image sequence at a low bit rate is presented. Motion compensation is based on the segmentation of the reconstructed image performed in both encoder and decoder. This will eliminate the need to transmit the region shape information. Also, motion vector prediction is implemented to reduce the overhead for motion information. Motion-compensated prediction errors are transformed using the discrete cosine transform, and the coefficients are quantized and entropy coded as recommended by CCITT. Computer simulation shows that the proposed coding algorithm significantly reduces the block distortion, as conventional block matching algorithms do at very low bit rates (e.g., 0.02 bit/pixel).
This paper presents a classified vector quantizer based on Peano scanning. The Peano scanning, which is used to reduce the dimensionality of the data provides a 1D algorithm to classify an image block. The class of the block is determined based on its Peano scanning value from a look up table (LUT) of representative Peano scanning values and their associated classes. The Peano scanning algorithm is easily implemented in hardware and the class can be determined in a logarithmic time proportional to the number of entries in the LUT when using a binary search algorithm on the sorted LUT. Moreover, the class look up table is easily implemented in real time. An effective algorithm to generate all the codebooks of the classes simultaneously in a systematic way based on the greedy tree growing algorithm is also presented. The monochromatic images encoded in the range of 0.625 - 0.813 with a 16 dimensional input vectors are shown to preserve the edge integrity and quality as determined by subjective and objective measures.
A new hybrid coding scheme for video sequences is presented. With the introduction of a fast quadtree motion segmentation algorithm, motion vectors are estimated with variable size block matching which produces better performance considering both overhead motion information and motion compensated prediction error. Small blocks containing high motion activities are intraframe vector quantized, whereas large blocks representing smooth motion areas are first decimated and then interframe vector quantized. Simulation results demonstrate that the proposed scheme performs very well.
KEYWORDS: Image compression, Image quality, Video coding, Image processing, Switching, Video compression, Video, Computer programming, Digital filtering, Navigation systems
In this paper, a new coding method for stereo image pairs is presented. Disparity vectors are estimated with variable size block matching algorithm which requires fewer overhead bits for transmission. The different statistical properties between original images and disparity- compensated images are discussed. The efficiency of DCT compression is fully utilized through adaptively switching between the original image data and the disparity-compensated image data. Simulation results demonstrate that the proposed scheme performs well.
KEYWORDS: Image compression, Image filtering, Cameras, Video processing, Data storage, Computer programming, Computer simulations, Earth sciences, Machine vision, Signal to noise ratio
Stereo images are used in many applications including autonomous vehicle navigation, geoscience, and machine vision. The storage and transmission of the stereo images involve large amounts of data. Therefore, efficient coding techniques are required to encode the two correlated stereo image pairs with low bit rates. This paper presents two techniques to compress stereo image data. The proposed schemes are based on subband coding and disparity compensation (DC). DC, which is similar to motion compensation, exploits the redundancy that exists between two stereo images. In the first method, the two images are decomposed into four subbands using two-tap filters. The lowest band of the left image is coded using block DPCM and DC is applied to code the lowest band of the right image. The high bands are directly quantized and PCM coded. The coding is adaptive in the sense that different quantizers are selected based on the activity of each block. In the second method, disparity compensated interpolative predictive coding, DC is applied to the original images and proceeds in a different way. To find the differential signals, the pixels in each block of the right image are divided into two groups, P1 and P2. P1 pixels are predicted by the corresponding pixels in its matching block and P2 pixels are predicted interpolatively by the decoded P1 pixels. Finally, a method for the lossless coding of the disparity information is presented. The proposed lossless scheme reduces the overhead information and the search efforts.
This paper reports a method to recognize partially occluded objects using the B-spline representation of the boundary. Curve sgements are represented using B-splines which are piecewise polynomial curves guided by a sequence of points. The B-spline control points found from the boundary points is then used to extract local features of the curve. A Hough transform like method is applied to normalize the two curve boundaries using extracted local features. The merit of a match is evaluated using the normalized B-spline control points. The ability of the technique to handle partial boundary information is also demonstrated.
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