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About 6,337 papers for “binary image processing”

Image Processing Architectures for Binary Morphology and Labeling

Hugo Hedberg · Lund University Publications (Lund University) · 2008

Conventional surveillance systems are omnipresent and most are still based on analog techniques. Migrating to the digital domain grants access to the world of digital image processing enabling automation of such systems, which means extracting information from the image stream without human interaction. The resolution, frame rates, and functionality in these systems are continuously increasing alongside the number of video streams to be processed. The sum of all these parameters imposes high data rates and memory bandwidths which are impossible to handle in pure software solutions. Therefore, accelerating key operations and complex repetitive calculations in dedicated hardware architectures is crucial to sustain real-time performance in future advanced high resolution and frame rate systems. To achieve this goal, this thesis presents four architectures of hardware accelerators to be used in real-time embedded image processing systems, implemented as an FPGA or ASIC. Two morphological architectures performing binary erosion or dilation, with low complexity and low memory requirement, have been developed. One supports static, and the other locally adaptive flat rectangular structuring elements of arbitrary size. Furthermore, a high-throughput architecture calculating the distance transform has also been developed. This architecture supports either the city-block or chessboard distance metric and is based on adding the result of parallel erosions. The fourth architecture performs connected component labeling based on contour tracing and supports feature extraction. A modified version of the morphological architecture supporting static structuring elements, as well as the labeling architecture, has been successfully integrated into a prototype of an automated digital surveillance system for which implementation aspects are presented. The system has been implemented and is running on an FPGA based development board using a CMOS sensor for image acquisition. The prototype currently has segmentation, filtering, and labeling accelerated in hardware, and additional image processing performed in software running on an embedded processor.

Cited by 4

Hyperspectral Image Representation and Processing With Binary Partition Trees

Silvia Valero, Philippe Salembier, J. Chanussot · IEEE Transactions on Image Processing · 2012

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

Cited by 99Open access

Binary Detection using Multi-Hypothesis Log-Likelihood, Image Processing

Brent H. Gessel · 2014

Abstract : One of the United States Air Force missions is to track space objects. Finding planets, stars, and other natural and synthetic objects are all impacted by how well the tools of measurement can distinguish between these objects when they are in close proximity. In astronomy, the term binary commonly refers to two closely spaced objects. Splitting a binary occurs when two objects are successfully detected. The physics of light, atmospheric distortion, and measurement imperfections can make binary detection a challenge. Binary detection using various post processing techniques can significantly increase the probability of detection. This paper explores the potential of using a multi-hypothesis approach. Each hypothesis assumes one two or no points exists in a given image. The log-likelihood of each hypothesis are compared to obtain detection results. Both simulated and measured data are used to demonstrate performance with various amounts of atmosphere, and signal to noise ratios. Initial results show a significant improvement when compared to a detection via imaging by correlation. More work exists to compare this technique to other binary detection algorithms and to explore cluster detection.

Cited by 1

A REVIEW ON RECONFIGURABLE PROCESSOR FOR BINARY IMAGE PROCESSING.

Kishan Shivhare, Gaurav Bhardwaj · International Journal of Advanced Research · 2016

An important tool which is used in image as well as video analysis is Binary image processing. Among various types of binary image processors, reconfigurable binary image processors are the most useful

Cited by 0Open access

Image Binary Matrix Processing to Encrypt-Decrypt Digital Images

Mohamad Al-Laham, Firas Omar, Ziad Alqadi · International Journal of Advanced Computer Science and Applications · 2024

This research study presents a simple cryptographic solution for protecting grayscale and colored digital images, which are commonly used in computer applications. Due to their widespread use, protecting these photos is crucial to preventing unauthorized access. This article’s methodology manipulates an image’s binary matrix using basic operations. These specified actions include increasing the 8-column matrix to 64 columns, reorganizing it into 64 columns, separating it into four blocks, and shuffle the columns using secret index keys. These keys are produced using four sets of common chaotic logistic parameters. Each set executes a chaotic logistic map model to generate a chaotic key, which is then translated into an index key. This index key shuffles columns during encryption and reverses during decryption. The cryptographic approach promises a large key space that can withstand hacking. The encrypted image is secure since the decryption procedure is sensitive to the precise private key values. Private keys are frequently chaotic logistic parameters, making encryption resilient. This method is convenient since it supports images of any size and kind without modifying the encryption or decryption techniques. Shuffling replaces difficult logical procedures in typical data encryption methods, simplifying the cryptographic process. Experiments with several photos will evaluate the proposed strategy. The encrypted and decrypted photos will be examined to ensure the method meets cryptographic standards. Speed tests will also compare the proposed method to existing cryptographic methods to show its potential to speed up picture cryptography by lowering encryption and decryption times.

Cited by 1Open access

Hyperspectral image representation and processing with binary partition trees

Silvia Valero Valbuena · 2012

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation

Cited by 8Open access

Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique

A. Sathesh, Edriss Eisa Babikir Adam · Journal of Artificial Intelligence and Capsule Networks · 2021

Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.

Cited by 39Open access

EXCLUSIVE OR processing (binary image subtraction) using thick Fourier holograms

Clark C. Guest, Mir Mojtaba Mirsalehi, Thomas K. Gaylord · Applied Optics · 1984

Theoretical and experimental results are presented for parallel EXCLUSIVE OR processing using thick Fourier holograms. The data pages used contained 1024 bits in a 32 × 32 format. A holographically stored data page is reconstructed together with an input data page that is imaged through the system. The amplitudes of the two wave fronts are adjusted to be equal, and their relative phase is adjusted to be 180°, thus producing the bit-by-bit EXCLUSIVE OR operation between the pages. Using an expanded reference beam for the geometrical configuration treated, it is calculated that the average dynamic range between a 0 and a 1 in the output power would be 17.4 dB. Experimentally, it is shown that excellent EXCLUSIVE OR results are obtainable. Photographs of these results are presented. An average dynamic range of 7.7 dB was measured, the decrease from the calculated value being primarily due to noise in the video method of detection used in the measurements. The probabilities of miss and false alarm and the total probability of error were also measured. The use of a small diameter reference beam, necessary in some applications, causes a large mismatch in the shapes of the two wave fronts, thus degrading the EXCLUSIVE OR results. It is shown theoretically and experimentally that using an aperture at the recording material produces partial compensation of the wave fronts, thus improving the EXCLUSIVE OR results.

Cited by 14

The Neighboring Pixel Representation for Efficient Binary Image Processing Operations

Rafał Kapela, Agnieszka Rybarczyk · 2006

Many image processing operations such as: segmentation, contour tracking, morphological operations, etc. needs an additional information about pixel neighborhood. In the other hand memory access operations are often (comparing to working time of other hardware devices) time-consuming, so checking values of neighboring pixels via memory access could lead to low performance of the algorithm. Our binary pixel representation contains information of it's neighborhood on one byte only, which is stored at the pixel's address. Moreover, because of information about zeros in neighboring pixels, computational time can be efficiently reduced - no need to checking memory cells where zero values are stored. Putting the pixels' values in dual-memory bank two image processing operations can be done simultaneously. This paper presents the algorithm of converting binary image to our notation, it's hardware realization and description of basics binary image operations based on the new notation. As an example of usage the notation the hardware realization of contour tracking algorithm was introduced as a preprocessor for contour shape descriptor of MPEG-7 standard

Cited by 5

Measurement errors in processing of binary and gray-scale image

Т. В. Миронова · Optoelectronics Instrumentation and Data Processing · 2014

The statistical error of correlation measurement of the shift is estimated using the binary image model. The influence of image properties on the accuracy of subpixel measurements of the position of the maximum of the correlation peak is studied. Recommendations for the choice of parameters of the image and correlation algorithm are given.

Cited by 0

SPmat: A Framework and Data Representation for Binary Image Processing

Fabrizio Pedersoli, George Tzanetakis · 2018

We propose an optimized framework for binary image processing, characterized by a highly bit-packed representation of pixels and their square neighbourhood. The Super-Packed (SPmat) representation for binary images enables the easy use of bit-wise computations for developing fast processing algorithms, such as: morphology, contours, run-length, and thinning, in a unified framework. With several experiments, we show that the aforementioned algorithms can be consistently sped-up, and outperform by a large margin available software implementations. The software package is freely available on github at the url https://github.com/fpeder/spmat to support reproducibility.

Cited by 0

Quick Response Code Binary Research based on Basic Image Processing

Wu Yun, Shaoyong Yu, Mei Yang · Sensors and Materials · 2019

The goal of this paper is to binarize quick response (QR) codes with basic image processing methods, so that they can be well recognized in extreme cases. In this paper, we avoid the complex computation that comes from sophisticated algorithms, and employ block truncation coding (BTC) to simplify the computation to make processing fast. The final results show that complex algorithms are not required in QR code binarization, and the basic image processing algorithm can achieve faster and better processing results.

Cited by 2Open access

LC-BiDet: Laterally Connected Binary Detector With Efficient Image Processing

Xichuan Zhou, Rui Ding, Haijun Liu · IEEE Signal Processing Letters · 2022

Recently, binary neural networks have received increasing interest in object detection community, since compared to the full-precision detection networks, they can reduce the memory and computation requirements significantly due to the efficient XNOR and BITCOUNT operations introduced by binarization. However, the final detection performance of binary detectors always suffers from a severe degradation compared with the full-precision counterparts. In this letter, to achieve a better trade-off between the inference efficiency and the detection performance, we propose to learn a laterally connected binary detector based on the multiple parallel binary subnets with a group sparse regularization term. Firstly, we simply adopt the parallel structure to improve the representation capacity of binary detectors. Secondly, we introduce the dense lateral connections between binary subnets in each convolutional layer to increase the information flows. Finally, to reduce the redundancy of the dense connections, we take the L21 norm as the regularization term to optimize the lateral connections in an end-to-end manner. Experimental results on PASCAL VOC and COCO datasets show that our proposed laterally connected binary detector could outperform the other state-ofthe- art binary detectors.

Cited by 3

A Polynomial Algebra Approach To Binary And Gray Image Processing

Prabir Bhattacharya, Kai Qian · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1989

A binary image is represented by a polynomial in two variables over GF(2) and several algebraic operators are developed in this environment to process images, for example, to find the contour, shrink, magnify and approximate images. The data structure for storing images by polynomials is obtained. The image processing technigues developed can be used to process gray-level images and also colored pictures.

Cited by 0