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Title: HVS-based wavelet color image coding.
Creator: Guo, Linfeng., Florida Atlantic University, Glenn, William E., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This work is an attempt of incorporating the latest advances in vision research and signal processing into the field of image coding. The scope of the dissertation is twofold. Firstly, it sets up a framework of the wavelet color image coder and makes optimizations of its performance. Secondly, it investigates the human vision models and implements human visual properties into the wavelet color image coder. A wavelet image coding framework consisting of image decomposition, coefficients...
Show moreThis work is an attempt of incorporating the latest advances in vision research and signal processing into the field of image coding. The scope of the dissertation is twofold. Firstly, it sets up a framework of the wavelet color image coder and makes optimizations of its performance. Secondly, it investigates the human vision models and implements human visual properties into the wavelet color image coder. A wavelet image coding framework consisting of image decomposition, coefficients quantization, data representation, and entropy coding is first set up, and then a couple of unsolved issues of wavelet image coding are studied and the consequent optimization schemes are presented and applied to the basic framework. These issues include the best wavelet bases selection, quantizer optimization, adaptive probability estimation in arithmetic coding, and the explicit transmission of significant map of wavelet data. Based on the established wavelet image coding framework, a human visual system (HVS) based adaptive color image coding scheme is proposed. Compared with the non-HVS-based coding methods, our method results in a superior performance without any cost of additional side information. As the rudiments of the proposed HVS-based coding scheme, the visual properties of the early stage of human vision are investigated first, especially the contrast sensitivity, the luminance adaptation, and the complicated simultaneous masking and crossed masking effects. To implement these visual properties into the wavelet image coding, the suitable estimation of local background luminance and contrast in the wavelet domain is also re-investigated. Based upon these prerequisite works, the effects of contrast sensitivity weighting and luminance adaptation are incorporated into our coding scheme. Furthermore, the mechanisms of all kinds of masking effects in color image, e.g., the self-masking, the neighbor masking, the crossbands masking, and the luminance-chrominance crossed-masking, are also studied and properly utilized into the coding scheme through an adaptive quantization scheme. Owing to elaborate arrangement and integration of the different parts of the perception based quantization scheme, the coefficient-dependent adaptive quantization step size can be losslessly restored during the decoding without any overhead of side information.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/11941
Subject Headings: Wavelets (Mathematics), Image processing--Digital techniques
Format: Document (PDF)

Title: Selective texture characterization using Gabor filters.
Creator: Boutros, George., Florida Atlantic University, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The objective of this dissertation is to develop effective algorithms for texture characterization, segmentation and labeling that operate selectively to label image textures, using the Gabor representation of signals. These representations are an analog of the spatial frequency tuning characteristics of the visual cortex cells. The Gabor function, of all spatial/spectral signal representations, provides optimal resolution between both domains. A discussion of spatial/spectral representations...
Show moreThe objective of this dissertation is to develop effective algorithms for texture characterization, segmentation and labeling that operate selectively to label image textures, using the Gabor representation of signals. These representations are an analog of the spatial frequency tuning characteristics of the visual cortex cells. The Gabor function, of all spatial/spectral signal representations, provides optimal resolution between both domains. A discussion of spatial/spectral representations focuses on the Gabor function and the biological analog that exists between it and the simple cells of the striate cortex. A simulation generates examples of the use of the Gabor filter as a line detector with synthetic data. Simulations are then presented using Gabor filters for real texture characterization. The Gabor filter spatial and spectral attributes are selectively chosen based on the information from a scale-space image in order to maximize resolution of the characterization process. A variation of probabilistic relaxation that exploits the Gabor filter spatial and spectral attributes is devised, and used to force a consensus of the filter responses for texture characterization. We then perform segmentation of the image using the concept of isolation of low energy states within an image. This iterative smoothing algorithm, operating as a Gabor filter post-processing stage, depends on a line processes discontinuity threshold. Selection of the discontinuity threshold is obtained from the modes of the histogram of the relaxed Gabor filter responses using probabilistic relaxation to detect the significant modes. We test our algorithm on simple synthetic and real textures, then use a more complex natural texture image to test the entire algorithm. Limitations on textural resolution are noted, as well as for the resolution of the image segmentation process.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/12342
Subject Headings: Image processing--Digital techniques, Computer vision
Format: Document (PDF)

Title: Densely-centered uniform P-search: A fast motion estimation algorithm.
Creator: Greenberg, Joshua H., Florida Atlantic University, Furht, Borko
Abstract/Description: Video compression technology promises to be the key to the transmission of motion video. A number of techniques have been introduced in the past few years, particularly that developed by the Motion Picture Experts Group (MPEG). The MPEG algorithm uses Motion Estimation to reduce the amount of data that is stored for each frame. Motion Estimation uses a reference frame as a codebook for a modified Vector Quantization process. While an exhaustive search for Motion Estimation Vectors is time...
Show moreVideo compression technology promises to be the key to the transmission of motion video. A number of techniques have been introduced in the past few years, particularly that developed by the Motion Picture Experts Group (MPEG). The MPEG algorithm uses Motion Estimation to reduce the amount of data that is stored for each frame. Motion Estimation uses a reference frame as a codebook for a modified Vector Quantization process. While an exhaustive search for Motion Estimation Vectors is time-consuming, various fast search algorithms have been developed. These techniques are surveyed, and the theoretical framework for a new search algorithm is developed: Densely-Centered Uniform P-Search. The time complexity of Densely-Centered Uniform P-Search is comparable to other popular Motion Estimation techniques, and shows superior results on a variety of motion video sources.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15286
Subject Headings: Image processing--Digital techniques, Data compression (Telecommunication)
Format: Document (PDF)

Title: Video and Image Analysis using Statistical and Machine Learning Techniques.
Creator: Luo, Qiming, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Digital videos and images are effective media for capturing spatial and ternporal information in the real world. The rapid growth of digital videos has motivated research aimed at developing effective algorithms, with the objective of obtaining useful information for a variety of application areas, such as security, commerce, medicine, geography, etc. This dissertation presents innovative and practical techniques, based on statistics and machine learning, that address some key research...
Show moreDigital videos and images are effective media for capturing spatial and ternporal information in the real world. The rapid growth of digital videos has motivated research aimed at developing effective algorithms, with the objective of obtaining useful information for a variety of application areas, such as security, commerce, medicine, geography, etc. This dissertation presents innovative and practical techniques, based on statistics and machine learning, that address some key research problems in video and image analysis, including video stabilization, object classification, image segmentation, and video indexing. A novel unsupervised multi-scale color image segmentation algorithm is proposed. The basic idea is to apply mean shift clustering to obtain an over-segmentation, and then merge regions at multiple scales to minimize the MDL criterion. The performance on the Berkeley segmentation benchmark compares favorably with some existing approaches. This algorithm can also operate on one-dimensional feature vectors representing each frame in ocean survey videos, which results in a novel framework for building a hierarchical video index. The advantage is to provide the user with the flexibility of browsing the videos at arbitrary levels of detail, which makes it more efficient for users to browse a long video in order to find interesting information based on the hierarchical index. Also, an empirical study on classification of ships in surveillance videos is presented. A comparative performance study on three classification algorithms is conducted. Based on this study, an effective feature extraction and classification algorithm for classifying ships in coastline surveillance videos is proposed. Finally, an empirical study on video stabilization is presented, which includes a comparative performance study on four motion estimation methods and three motion correction methods. Based on this study, an effective real-time video stabilization algorithm for coastline surveillance is proposed, which involves a novel approach to reduce error accumulation.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012574
Subject Headings: Image processing--Digital techniques, Electronic surveillance, Computational learning theory
Format: Document (PDF)

Title: Low-level and high-level correlation for image registration.
Creator: Mandalia, Anil Dhirajlal., Florida Atlantic University, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The fundamental goal of a machine vision system in the inspection of an assembled printed circuit board is to locate the integrated circuit(IC) components. These components are then checked for their position and orientation with respect to a given position and orientation of the model and to detect deviations. To this end, a method based on a modified two-level correlation scheme is presented in this thesis. In the first level, Low-Level correlation, a modified two-stage template matching...
Show moreThe fundamental goal of a machine vision system in the inspection of an assembled printed circuit board is to locate the integrated circuit(IC) components. These components are then checked for their position and orientation with respect to a given position and orientation of the model and to detect deviations. To this end, a method based on a modified two-level correlation scheme is presented in this thesis. In the first level, Low-Level correlation, a modified two-stage template matching method is proposed. It makes use of the random search techniques, better known as the Monte Carlo method, to speed up the matching process on binarized version of the images. Due to the random search techniques, there is uncertainty involved in the location where the matches are found. In the second level, High-Level correlation, an evidence scheme based on the Dempster-Shafer formalism is presented to resolve the uncertainty. Experiment results performed on a printed circuit board containing mounted integrated components is also presented to demonstrate the validity of the techniques.
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Date Issued: 1990
PURL: http://purl.flvc.org/fcla/dt/14635
Subject Headings: Image processing--Digital techniques, Computer vision, Integrated circuits
Format: Document (PDF)

Title: Using color image processing techniques to improve the performance of content-based image retrieval systems.
Creator: Costa, Fabio Morais., Florida Atlantic University, Furht, Borko
Abstract/Description: A Content-Based Image Retrieval (CBIR) system is a mechanism intended to retrieve a particular image from a large image repository without resorting to any additional information about the image. Query-by-example (QBE) is a technique used by CBIR systems where an image is retrieved from the database based on an example given by the user. The effectiveness of a CBIR system can be measured by two main indicators: how close the retrieved results are to the desired image and how fast we got those...
Show moreA Content-Based Image Retrieval (CBIR) system is a mechanism intended to retrieve a particular image from a large image repository without resorting to any additional information about the image. Query-by-example (QBE) is a technique used by CBIR systems where an image is retrieved from the database based on an example given by the user. The effectiveness of a CBIR system can be measured by two main indicators: how close the retrieved results are to the desired image and how fast we got those results. In this thesis, we implement some classical image processing operations in order to improve the average rank of the desired image, and we also implement two object recognition techniques to improve the subjective quality of the best ranked images. Results of experiments show that the proposed system outperforms an equivalent CBIR system in QBE mode, both from the point of view of precision as well as recall.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12870
Subject Headings: Image processing--Digital techniques, Imaging systems--Image quality, Information storage and retrieval systems
Format: Document (PDF)

Title: Subband coding of images using binomial QMF and vector quantization.
Creator: Rajamani, Kannan., Florida Atlantic University, Erdol, Nurgun, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis presents an image coding system using binomial QMF based subband decomposition and vector quantisation. An attempt was made to compress a still image of size 256 x 256 represented at a resolution of 8 bits/pixel to a bit rate of 0.5 bits/pixel using 16 channel subband decomposition with binomial QMFs and coding the subbands using a full search LBG Vector Quantizer (VQ). Simulations were done on SUN work station and the quality of the image was evaluated by computing the Signal to...
Show moreThis thesis presents an image coding system using binomial QMF based subband decomposition and vector quantisation. An attempt was made to compress a still image of size 256 x 256 represented at a resolution of 8 bits/pixel to a bit rate of 0.5 bits/pixel using 16 channel subband decomposition with binomial QMFs and coding the subbands using a full search LBG Vector Quantizer (VQ). Simulations were done on SUN work station and the quality of the image was evaluated by computing the Signal to Noise Ratio (SNR) between the original image and the reconstructed image.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15203
Subject Headings: Image compression--Digital techniques, Image processing--Digital techniques, Image transmission--Digital techniques, Coding theory, Vector fields
Format: Document (PDF)

Title: A novel DSP scheme for image compression and HDTV transmission.
Creator: Dong, Xu., Florida Atlantic University, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The main objective of the research is to develop computationally efficient hybrid coding schemes for the low bit implementations of image frames and image sequences. The basic fractal block coding can compress a relatively low resolution image efficiently without blocky artifacts, but it does not converge well at the high frequency edges. This research proposes a hybrid multi-resolution scheme which combines the advantages of fractal and DCT coding schemes. The fractal coding is applied to...
Show moreThe main objective of the research is to develop computationally efficient hybrid coding schemes for the low bit implementations of image frames and image sequences. The basic fractal block coding can compress a relatively low resolution image efficiently without blocky artifacts, but it does not converge well at the high frequency edges. This research proposes a hybrid multi-resolution scheme which combines the advantages of fractal and DCT coding schemes. The fractal coding is applied to get a lower resolution, quarter size output image and DCT is then used to encode the error residual between original full bandwidth image signal and the fractal decoded image signal. At the decoder side, the full resolution, full size reproduced image is generated by adding decoded error image to the decoded fractal image. Also, the lower resolution, quarter size output image is automatically given by the iteration function scheme without having to spend extra effort. Other advantages of the scheme are that the high resolution layer is generated by error image which covers the bandwidth loss of the lower resolution layer as well as the coding error of the lower resolution layer, and that it does not need a sophisticated classification procedure. A series of computer simulation experiments are conducted and their results are presented to illustrate the merit of the scheme. The hybrid fractal coding method is then extended to process motion sequences as well. A new scheme is proposed for motion vector detection and motion compensation, by judiciously combining the techniques of fractal compression and block matching. The advantage of this scheme is that it improves the performance of the motion compensation, while keeping the overall computational complexity low for each frame. The simulation results on realistic video conference image sequences support the superiority of the proposed method in terms of reproduced picture quality and compression ratio.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/12407
Subject Headings: Hybrid integrated circuits, Image compression, Fractals, Image processing--Digital techniques, High definition television
Format: Document (PDF)

Title: HEVC optimization in mobile environments.
Creator: Garcia, Ray, Kalva, Hari, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Recently, multimedia applications and their use have grown dramatically in popularity in strong part due to mobile device adoption by the consumer market. Applications, such as video conferencing, have gained popularity. These applications and others have a strong video component that uses the mobile device’s resources. These resources include processing time, network bandwidth, memory use, and battery life. The goal is to reduce the need of these resources by reducing the complexity of the...
Show moreRecently, multimedia applications and their use have grown dramatically in popularity in strong part due to mobile device adoption by the consumer market. Applications, such as video conferencing, have gained popularity. These applications and others have a strong video component that uses the mobile device’s resources. These resources include processing time, network bandwidth, memory use, and battery life. The goal is to reduce the need of these resources by reducing the complexity of the coding process. Mobile devices offer unique characteristics that can be exploited for optimizing video codecs. The combination of small display size, video resolution, and human vision factors, such as acuity, allow encoder optimizations that will not (or minimally) impact subjective quality. The focus of this dissertation is optimizing video services in mobile environments. Industry has begun migrating from H.264 video coding to a more resource intensive but compression efficient High Efficiency Video Coding (HEVC). However, there has been no proper evaluation and optimization of HEVC for mobile environments. Subjective quality evaluations were performed to assess relative quality between H.264 and HEVC. This will allow for better use of device resources and migration to new codecs where it is most useful. Complexity of HEVC is a significant barrier to adoption on mobile devices and complexity reduction methods are necessary. Optimal use of encoding options is needed to maximize quality and compression while minimizing encoding time. Methods for optimizing coding mode selection for HEVC were developed. Complexity of HEVC encoding can be further reduced by exploiting the mismatch between the resolution of the video, resolution of the mobile display, and the ability of the human eyes to acquire and process video under these conditions. The perceptual optimizations developed in this dissertation use the properties of spatial (visual acuity) and temporal information processing (motion perception) to reduce the complexity of HEVC encoding. A unique feature of the proposed methods is that they reduce encoding complexity and encoding time. The proposed HEVC encoder optimization methods reduced encoding time by 21.7% and bitrate by 13.4% with insignificant impact on subjective quality evaluations. These methods can easily be implemented today within HEVC.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004112
Subject Headings: Coding theory, Digital coding -- Data processing, Image processing -- Digital techniques, Multimedia systems, Video compression
Format: Document (PDF)

Title: A Novel Method for Human Face Enhancement for Video Images.
Creator: Salas, Ernesto Anel, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The focus of this research is on images extracted from surveillance videos that have a low resolution and are taken under low illumination. In recent years, great advances have been made in face recognition and many studies mention results of 80% and 90% of recognition efficiency, however, most of these studies reported results using face images under controlled conditions. Current surveillance systems are equipped with low resolution cameras and are located in places with changing...
Show moreThe focus of this research is on images extracted from surveillance videos that have a low resolution and are taken under low illumination. In recent years, great advances have been made in face recognition and many studies mention results of 80% and 90% of recognition efficiency, however, most of these studies reported results using face images under controlled conditions. Current surveillance systems are equipped with low resolution cameras and are located in places with changing illumination, as opposed to a controlled environment. To be used in face recognition, images extracted from videos need to be normalized, enlarged and preprocessed. There is a multitude of processing algorithms for image enhancement, and each algorithm faces its advantages and disadvantages. This thesis presents a novel method for image enlargement of human faces applied to low quality video recordings. Results and comparison to traditional methods are also presented.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012547
Subject Headings: Human face recognition (Computer science), Biometric identification, Image processing--Digital techniques, Pattern recognition systems
Format: Document (PDF)

Title: Adaptive two-level watermarking for binary document images.
Creator: Muharemagic, Edin., Florida Atlantic University, Furht, Borko, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In our society, large volumes of documents are exchanged on a daily basis. Since documents can easily be scanned, modified and reproduced without any loss in quality, unauthorized use and modification of documents is of major concern. An authentication watermark embedded into a document as an invisible, fragile mark can be used to detect illegal document modification. However, the authentication watermark can only be used to determine whether documents have been tampered with, and additional...
Show moreIn our society, large volumes of documents are exchanged on a daily basis. Since documents can easily be scanned, modified and reproduced without any loss in quality, unauthorized use and modification of documents is of major concern. An authentication watermark embedded into a document as an invisible, fragile mark can be used to detect illegal document modification. However, the authentication watermark can only be used to determine whether documents have been tampered with, and additional protection may be needed to prevent unauthorized use and distribution of those documents. A solution to this problem is a two-level, multipurpose watermark. The first level watermark is an authentication mark used to detect document tampering, while the second level watermark is a robust mark, which identifies the legitimate owner and/or user of specific document. This dissertation introduces a new adaptive two-level multipurpose watermarking scheme suitable for binary document images, such as scanned text, figures, engineering and road maps, architectural drawings, music scores, and handwritten text and sketches. This watermarking scheme uses uniform quantization and overlapped embedding to add two watermarks, one robust and the other fragile, into a binary document image. The two embedded watermarks serve different purposes. The robust watermark carries document owner or document user identification, and the fragile watermark confirms document authenticity and helps detect document tampering. Both watermarks can be extracted without accessing the original document image. The proposed watermarking scheme adaptively selects an image partitioning block size to optimize the embedding capacity, the image permutation key to minimize watermark detection error, and the size of local neighborhood in which modification candidate pixels are scored to minimize visible distortion of watermarked documents. Modification candidate pixels are scored using a novel, objective metric called the Structural Neighborhood Distortion Measure (SNDM). Experimental results confirm that this watermarking scheme, which embeds watermarks by modifying image pixels based on their SNDM scores, creates smaller visible document distortion than watermarking schemes which base watermark embedding on any other published pixel scoring method. Document tampering is detected successfully and the robust watermark can be detected even after document tampering renders the fragile watermark undetectable.
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Date Issued: 2004
PURL: http://purl.flvc.org/fau/fd/FADT12113
Subject Headings: Data encryption (Computer science), Computer security, Digital watermarking, Data protection, Image processing--Digital techniques, Watermarks
Format: Document (PDF)

Title: Sparse Coding and Compressed Sensing: Locally Competitive Algorithms and Random Projections.
Creator: Hahn, William E., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
Abstract/Description: For an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse...
Show moreFor an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse modeling and L1 optimization have allowed for extraordinary applications such as the single pixel camera, as well as computer vision systems that can exceed human performance. Here we present a novel neural network architecture combining a sparse filter model and locally competitive algorithms (LCAs), and demonstrate the networks ability to classify human actions from video. Sparse filtering is an unsupervised feature learning algorithm designed to optimize the sparsity of the feature distribution directly without having the need to model the data distribution. LCAs are defined by a system of di↵erential equations where the initial conditions define an optimization problem and the dynamics converge to a sparse decomposition of the input vector. We applied this architecture to train a classifier on categories of motion in human action videos. Inputs to the network were small 3D patches taken from frame di↵erences in the videos. Dictionaries were derived for each action class and then activation levels for each dictionary were assessed during reconstruction of a novel test patch. We discuss how this sparse modeling approach provides a natural framework for multi-sensory and multimodal data processing including RGB video, RGBD video, hyper-spectral video, and stereo audio/video streams.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004713, http://purl.flvc.org/fau/fd/FA00004713
Subject Headings: Artificial intelligence, Expert systems (Computer science), Image processing -- Digital techniques -- Mathematics, Sparse matrices
Format: Document (PDF)

Title: Sparse and low rank constraints on optical flow and trajectories.
Creator: Gibson, Joel, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this dissertation we apply sparse constraints to improve optical flow and trajectories. We apply sparsity in two ways. First, with 2-frame optical flow, we enforce a sparse representation of flow patches using a learned overcomplete dictionary. Second, we apply a low rank constraint to trajectories via robust coupling. We begin with a review of optical flow fundamentals. We discuss the commonly used flow estimation strategies and the advantages and shortcomings of each. We introduce the...
Show moreIn this dissertation we apply sparse constraints to improve optical flow and trajectories. We apply sparsity in two ways. First, with 2-frame optical flow, we enforce a sparse representation of flow patches using a learned overcomplete dictionary. Second, we apply a low rank constraint to trajectories via robust coupling. We begin with a review of optical flow fundamentals. We discuss the commonly used flow estimation strategies and the advantages and shortcomings of each. We introduce the concepts associated with sparsity including dictionaries and low rank matrices.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004286, http://purl.flvc.org/fau/fd/FA00004286
Subject Headings: Approximation theory -- Mathematical models, Computer vision, Image processing -- Digital techniques, Information visualization
Format: Document (PDF)

Title: DIGITAL IMAGE PROCESSING APPLIED TO CHARACTER RECOGNITION.
Creator: BEGUN, RALPH MURRAY., Florida Atlantic University, Erdol, Nurgun
Abstract/Description: Surveys are made of both character recognition and image processing. The need to apply image processing techniques to character recognition is pointed out. The fields are then combined and tested in sample programs. Simulations are made of recognition systems with and without image preprocessing. Processing techniques applied utilize Walsh-Hadamard transforms and l ocal window operators. Results indicate that image prepro c ess i ng improves recognition rates when noise degrades input images....
Show moreSurveys are made of both character recognition and image processing. The need to apply image processing techniques to character recognition is pointed out. The fields are then combined and tested in sample programs. Simulations are made of recognition systems with and without image preprocessing. Processing techniques applied utilize Walsh-Hadamard transforms and l ocal window operators. Results indicate that image prepro c ess i ng improves recognition rates when noise degrades input images. A system architecture is proposed for a hardware based video speed image processor operating on local image windows. The possible implementation of this processor is outlined.
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Date Issued: 1982
PURL: http://purl.flvc.org/fcla/dt/14120
Subject Headings: Image processing--Digital techniques, Optical character recognition devices, Pattern recognition systems
Format: Document (PDF)

Title: Content-based image retrieval using relevance feedback.
Creator: Marques, Oge, Florida Atlantic University, Furht, Borko, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation presents the results of research that led to the development of a complete, fully functional, image search and retrieval system with relevance feedback capabilities, called MUSE (MUltimedia SEarch and Retrieval Using Relevance Feedback). Two different models for searching for a target image using relevance feedback have been proposed, implemented, and tested. The first model uses a color-based feature vector and employs a Bayesian learning algorithm that updates the...
Show moreThis dissertation presents the results of research that led to the development of a complete, fully functional, image search and retrieval system with relevance feedback capabilities, called MUSE (MUltimedia SEarch and Retrieval Using Relevance Feedback). Two different models for searching for a target image using relevance feedback have been proposed, implemented, and tested. The first model uses a color-based feature vector and employs a Bayesian learning algorithm that updates the probability of each image in the database being the target based on the user's actions. The second model uses cluster analysis techniques, a combination of color-, texture-, and edge(shape)-based features, and a novel approach to learning the user's goals and the relevance of each feature for a particular search. Both models follow a purely content-based image retrieval paradigm. The search process is based exclusively on image contents automatically extracted during the (off-line) feature extraction stage. Moreover, they minimize the number and complexity of required user's actions, in contrast with the complexity of the underlying search and retrieval engine. Results of experiments show that both models exhibit good performance for moderate-size, unconstrained databases and that a combination of the two outperforms any of them individually, which is encouraging. In the process of developing this dissertation, we also implemented and tested several image features and similarity measurement combinations. The result of these tests---performed under the query-by-example (QBE) paradigm---served as a reference in the choice of which features to use in the relevance feedback mode and confirmed the difficulty in encoding the understanding of image similarity into a combination of features and distances without human assistance. Most of the code written during the development of this dissertation has been encapsulated into a multifunctional prototype that combines image searching (with or without an example), browsing, and viewing capabilities and serves as a framework for future research in the subject.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/11954
Subject Headings: Information storage and retrieval systems, Image processing--Digital techniques, Feedback control systems
Format: Document (PDF)

Title: Methods and Algorithms for Human Detection in Video Sequences.
Creator: Pertuz, Carlos, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Lower prices of video sensors, security concerns and the need for better and faster algorithms to extract high level information from video sequences are all factors which have stimulated research in the area of automated video surveillance systems. In the context of security the analysis of human interrelations and their environment provides hints to proactively identify anomalous behavior. However, human detection is a necessary component in systems where the automatic extraction of higher...
Show moreLower prices of video sensors, security concerns and the need for better and faster algorithms to extract high level information from video sequences are all factors which have stimulated research in the area of automated video surveillance systems. In the context of security the analysis of human interrelations and their environment provides hints to proactively identify anomalous behavior. However, human detection is a necessary component in systems where the automatic extraction of higher level information, such as recognizing individuals' activities, is required. The human detection problem is one of classification. In general, motion, appearance and shape are the classification approaches a system can employ to perform human detection. Techniques representative of these approaches, such us periodic motion detection, skin color detection and MPEG-7 shape descriptors are implemented in this work. An infrastructure that allows data collection for such techniques was also implemented.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012538
Subject Headings: MPEG (Video coding standard), Image processing--Digital techniques, Form perception, Computer algorithms, Video compression
Format: Document (PDF)

Title: Image detection for a customizable user interface.
Creator: Polimeni, Joseph C., Florida Atlantic University, Mahgoub, Imad, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A user interface that has objects familiar to the user will be easier to use. In this thesis, a user interface that is customizable to any color bitmap is proposed. The most significant problem with this approach is the problem of finding objects in a color bitmap. A solution to the problem is proposed and evaluated using an analysis tool, developed for this thesis, called Workbench. Current image detection methods are evaluated and compared to the solution proposed using Workbench. The...
Show moreA user interface that has objects familiar to the user will be easier to use. In this thesis, a user interface that is customizable to any color bitmap is proposed. The most significant problem with this approach is the problem of finding objects in a color bitmap. A solution to the problem is proposed and evaluated using an analysis tool, developed for this thesis, called Workbench. Current image detection methods are evaluated and compared to the solution proposed using Workbench. The proposed solution is then evaluated for the YIQ and HSI color mappings. The results of this investigation and recommendations for future work is proposed.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15207
Subject Headings: User interfaces (Computer systems), Human-computer interaction, Image processing--Digital techniques
Format: Document (PDF)

Title: Image rectification/registration from a project management perspective: A review of various software.
Creator: Gammack-Clark, James Peter, Florida Atlantic University, Roberts, Charles
Abstract/Description: The project manager has much to deliberate when choosing a software package for image rectification/registration. He/she must be able to perform a cost analysis evaluation of the packages in question, and determine which package will provide the highest level of positional accuracy. Objective and subjective analysis of six software packages, ArcView Image Analysis, GeoMedia Pro, Arc/Info 8.1, ERMAPPER, ENVI and Idrisi 3.2, and their multiple products (polynomials and triangulations) provide...
Show moreThe project manager has much to deliberate when choosing a software package for image rectification/registration. He/she must be able to perform a cost analysis evaluation of the packages in question, and determine which package will provide the highest level of positional accuracy. Objective and subjective analysis of six software packages, ArcView Image Analysis, GeoMedia Pro, Arc/Info 8.1, ERMAPPER, ENVI and Idrisi 3.2, and their multiple products (polynomials and triangulations) provide the basis with which the project manager may attain this goal. He/she is familiarized with the user interface of each package, through detailed step-by-step methodology. Positional accuracy of each product is compared to Ground Control Points (GCPs) derived from a Differential Global Positioning System (DGPS). The accuracy of each product is also compared to the industry standard USGS DOQQ, and it is discovered that while simple rectification procedures may produce mean errors acceptable to the specifications of NMAS, the strictest application of these standards reveal that these products are not accurate enough to satisfy the USGS standards.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12829
Subject Headings: Computer software--Evaluation, Image processing--Digital techniques, Remote sensing
Format: Document (PDF)

Title: Realization and implementation of separable-in-denominator two-dimensional digital filter.
Creator: Huang, Ziqiang., Florida Atlantic University, Zilouchian, Ali
Abstract/Description: In this thesis, a partial fraction expansion of a separable-in-denominator 2-D transfer function is given. Based on this expansion, several novel realizations of separable-in-denominator 2-D filter are provide. These realizations have the properties of highly parallel structure and improved throughput delay. The performance figures are given in the tables. A method of evaluation of quantization error of separable-in-denominator 2-D filter is also derived by using the residue method. Formulas...
Show moreIn this thesis, a partial fraction expansion of a separable-in-denominator 2-D transfer function is given. Based on this expansion, several novel realizations of separable-in-denominator 2-D filter are provide. These realizations have the properties of highly parallel structure and improved throughput delay. The performance figures are given in the tables. A method of evaluation of quantization error of separable-in-denominator 2-D filter is also derived by using the residue method. Formulas for calculation of roundoff noise of proposed structures are provided. Two programs which can be used to calculate the roundoff noise of proposed structure are listed in the Appendix. To run the programs, we need only to input the constant coefficients of expanded transfer function. At last, an optimal block realization of separable-in-denominator 2-D filter is discussed and the criterion for the absence of limit cycles for a second-order 2-D block is given.
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Date Issued: 1992
PURL: http://purl.flvc.org/fcla/dt/14879
Subject Headings: Real-time data processing, Image processing--Digital techniques, Electric filters, Digital--Computer programs
Format: Document (PDF)

Title: XYZ Video Compression: An algorithm for real-time compression of motion video based upon the three-dimensional discrete cosine transform.
Creator: Westwater, Raymond John., Florida Atlantic University, Furht, Borko, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: XYZ Video Compression denotes a video compression algorithm that operates in three dimensions, without the overhead of motion estimation. The smaller overhead of this algorithm as compared to MPEG and other "standards-based" compression algorithms using motion estimation suggests the suitability of this algorithm to real-time applications. The demonstrated results of compression of standard motion video benchmarks suggest that XYZ Video Compression is not only a faster algorithm, but develops...
Show moreXYZ Video Compression denotes a video compression algorithm that operates in three dimensions, without the overhead of motion estimation. The smaller overhead of this algorithm as compared to MPEG and other "standards-based" compression algorithms using motion estimation suggests the suitability of this algorithm to real-time applications. The demonstrated results of compression of standard motion video benchmarks suggest that XYZ Video Compression is not only a faster algorithm, but develops superior compression ratios as well. The algorithm is based upon the three-dimensional Discrete Cosine Transform (DCT). Pixels are organized as 8 x 8 x 8 cubes by taking 8 x 8 squares out of 8 consecutive frames. A fast three-dimensional transform is applied to each cube, generating 512 DCT coefficients. The energy-packing property of the DCT concentrates the energy in the cube into few coefficients. The DCT coefficients are quantized to maximize the energy concentration at the expense of introduction of a user-determined level of error. A method of adaptive quantization that generates optimal quantizers based upon statistics gathered for the 8 consecutive frames is described. The sensitivity of the human eye to various DCT coefficients is used to modify the quantizers to create a "visually equivalent" cube with still greater energy concentration. Experiments are described that justify choice of Human Visual System factors to be folded into the quantization step. The quantized coefficients are then encoded into a data stream using a method of entropy coding based upon the statistics of the quantized coefficients. The bitstream generated by entropy coding represents the compressed data of the 8 motion video frames, and typically will be compressed at 50:1 at 5% error. The decoding process is the reverse of the encoding process: the bitstream is decoded to generate blocks of quantized DCT coefficients, the DCT coefficients are dequantized, and the Inverse Discrete Cosine Transform is performed on the cube to recover pixel data suitable for display. The elegance of this technique lies in its simplicity, which lends itself to inexpensive implementation of both encoder and decoder. Finally, real-time implementation of the XYZ Compressor/Decompressor is discussed. Experiments are run to determine the effectiveness of the implementation.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/12450
Subject Headings: Digital video, Data compression (Telecommunication), Image processing--Digital techniques, Coding theory
Format: Document (PDF)

Image processing--Digital techniques (21)	+ -
Coding theory (3)	+ -
Computer vision (3)	+ -
Computer algorithms (2)	+ -
Data compression (Telecommunication) (2)	+ -

Human face recognition (Computer science) (2)	+ -
Image processing -- Digital techniques (2)	+ -
Information storage and retrieval systems (2)	+ -
Optical character recognition devices (2)	+ -
Pattern recognition systems (2)	+ -
Video compression (2)	+ -
Approximation theory -- Mathematical models (1)	+ -
Artificial intelligence (1)	+ -
Artificial intelligence. (1)	+ -
Biometric identification (1)	+ -
Computational learning theory (1)	+ -
Computer graphics (1)	+ -
Computer security (1)	+ -
Computer software--Evaluation (1)	+ -
Computer vision. (1)	+ -
Computer-aided design (1)	+ -
Computervision (1)	+ -
Data compression (Computer science) (1)	+ -
Data encryption (Computer science) (1)	+ -
Data protection (1)	+ -
Digital coding -- Data processing (1)	+ -
Digital video (1)	+ -
Digital watermarking (1)	+ -
Electric filters, Digital--Computer programs (1)	+ -
Electronic surveillance (1)	+ -

Florida Atlantic University (25)	+ -
College of Engineering and Computer Science (19)	+ -
Department of Computer and Electrical Engineering and Computer Science (18)	+ -
Furht, Borko (5)	+ -
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BEGUN, RALPH MURRAY. (1)	+ -
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Boutros, George. (1)	+ -
Center for Complex Systems and Brain Sciences (1)	+ -
Charles E. Schmidt College of Science (1)	+ -
Costa, Fabio Morais. (1)	+ -
Department of Ocean and Mechanical Engineering (1)	+ -
Dong, Xu. (1)	+ -
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