Cellular Automata as a Tool for Image Processing

Rosin, Paul L.; Sun, Xianfang

doi:10.1142/9789814343008_0012

Cited by 6 publications

(9 citation statements)

References 26 publications

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“…Lafe also proposed CA methods, by which information building blocks, called basis functions (or bases), can be generated from the evolving states, and called it Cellular Automata Transforms (CAT) applying them to image and video compression [30]. During the late years, it has also been shown by several researchers [31], [32], [33], [34], [35], [36], [37], [38] that CA can be used to perform some standard image processing tasks to a high level of performance, as well as in up-to-date computer vision fields, like stereo vision [39], [40], [41]. On the other hand, CAs have been extensively used as a VLSI architecture [42].…”

Section: Hardwarementioning

confidence: 99%

Human and Fire Detection from High Altitude UAV Images

Giitsidis

Karakasis

Γαστεράτος

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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Illegal migration as well as wildfires constitute commonplace situations in southern European countries, where the mountainous terrain and thick forests make the surveillance and location of these incidents a tall task. This territory could benefit from Unmanned Aerial Vehicles (UAVs) equipped with optical and thermal sensors in conjunction with sophisticated image processing and computer vision algorithms, in order to detect suspicious activity or prevent the spreading of a fire. Taking into account that the flight height is about to two kilometers, human and fire detection algorithms are mainly based on blob detection. For both processes thermal imaging is used in order to improve the accuracy of the algorithms, while in the case of human recognition information like movement patterns as well as shadow size and shape are also considered. For fire detection a blob detector is utilized in conjunction with a color based descriptor, applied to thermal and optical images, respectively. Unlike fire, human detection is a more demanding process resulting in a more sophisticated and complex algorithm. The main difficulty of human detection originates from the high flight altitude. In images taken from high altitude where the ground sample distance is not small enough, people appear as small blobs occupying few pixels, leading corresponding research works to be based on blob detectors to detect humans. Their shadows as well as motion detection and object tracking can then be used to determine whether these regions of interest do depict humans. This work follows this motif as well; nevertheless, its main novelty lies in the fact that the human detection process is adapted for high altitude and vertical shooting images in contrast with the majority of other similar works where lower altitudes and different shooting angles are considered. Additionally, in the interest of making our algorithms as fast as possible in order for them to be used in real time during the UAV flights, parallel image processing with the help of a specialized hardware device based on Field Programmable Gate Array (FPGA) is being worked on.

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Section: Hardwarementioning

confidence: 99%

Human and Fire Detection from High Altitude UAV Images

Giitsidis

Karakasis

Γαστεράτος

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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“…16,17 When CA are used for edge detection, the image is usually viewed as a two-dimensional cellular automaton where each pixel is taken as a cell that connects to its neighboring pixels (cells), and the pixel values (0 or 1 in binary images) are the state values. 15 Elementary CA is the most prevalent CA where the cell’s state is either 0 or 1. So, it is instinctive to use CA for binary images.…”

Section: Introductionmentioning

confidence: 99%

“…CA based edge detection has several advantages over traditional approaches since it is computationally efficient, and can be tuned for specific applications by appropriate selection or learning of rules. 15 A cellular automaton is an assemblage of cells arranged in an N-dimensional (N-D) grid, such that state change of each cell is represented as a function of time, with a characterized set of rules that comprises the states of the neighboring cells. In CA, rules are applied to the whole grid simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Non-linear cellular automata based edge detector for optical character images

et al. 2016

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Design of parallel algorithms for edge detection is extremely important for image analysis and understanding. Cellular automata are the most common and simple models of parallel computation and over the last decade, numerous cellular automata techniques have already been proposed. This paper presents a novel method for edge detection of optical character images based on a variant of cellular automata, called non-linear cellular automata. The method consists of three stages and each stage is simple to understand and implement. A standard binarization technique is applied to a grayscale image and boundary conditions are added to the resultant image. Finally, non-linear cellular automata rules are designed and applied simultaneously to all pixels of the image. The suggested scheme has been validated on optical characters (handwritten as well as printed) of different languages. Furthermore, results are compared with standard edge detection techniques in terms of different performance parameters like entropy, kappa values, true positives, false negatives, etc. It is observed that the suggested scheme is superior to other standard schemes. Hence, the scheme has the potential application for character recognition.

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“…Lafe has also proposed CA methods, by which information building blocks, called basis functions (or bases), can be generated from the evolving states, and called it Cellular Automata Transforms (CAT) applying them to image and video compression in [19]. In recent years, it has also been shown by several researchers [2,12,26,[30][31][32] that CA can be used to perform some standard image processing tasks to a high level of performance, as well as in up-to-date computer vision fields, such as stereo vision [9,22,24]. For example, Rosin in [30] proposed the training of CA to perform several image processing tasks, namely noise filtering (also applied to grayscale images using threshold decomposition), thinning, and convex hulls, while the same author proposed in [31] the application of CA in intensity images instead of binary ones, able to perform many different image processing tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.…”

Section: Introductionmentioning

confidence: 99%

Cellular Automata for Image Resizing

Ioannidis

Sirakoulis

Andreadis

2014

Emergence, Complexity and Computation

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Abstract. During the last years, several methods have been applied to tackle the image resizing problem. Most of these methods are derived from image interpolation techniques for image enlargement. Among them, the edge-directed interpolation methods succeed to preserve the edges of the low resolution image and produce crisper results compared to the space invariant models. In this chapter, we present an edge-directed method which exploits the simplicity and the inherent parallelism of the Cellular Automata (CA) computational tool to generate high resolution images from low resolution acquired images. This task is accomplished with the help of the Canny Edge Detector so as to discriminate the edge regions from the homogenous ones. Moreover, appropriate CA states and transition rules were designed to evolve the CA, which, eventually, attempt to enhance the quality of the edge areas. The orientation of the edge cells are considered in order to preserve effectively the edges of the initial image. The presented experimental results in terms of PSNR values and processing time demonstrate the effectiveness of the proposed method when compared to well-known methods as well as its suitability, especially for systems with low requirements specifications when further image processing is required.

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Cellular Automata as a Tool for Image Processing

Cited by 6 publications

References 26 publications

Human and Fire Detection from High Altitude UAV Images

Human and Fire Detection from High Altitude UAV Images

Non-linear cellular automata based edge detector for optical character images

Cellular Automata for Image Resizing

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