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This article presents the results of research into methods and algorithms for processing image sequences aimed at application in autonomous navigation systems. The methods are grouped in the following areas: pre-processing, generation of 2D and 3D scene models, object recognition. and determination of motion parameters from a sequence of images. The article presents methods and algorithms proposed and researched by the authors of the article over the past decade. Description of the methods and algorithms is accompanied by examples and results of experimental studies. [ABSTRACT FROM AUTHOR]

The rapid growth of geographic information technologies in the field of processing and analysis of spatial data has led to a significant increase in the role of geographic information systems in various fields of human activity. However, solving complex problems requires the use of large amounts of spatial data, efficient storage of data on on-board recording media and their transmission via communication channels. This leads to the need to create new effective methods of compression and data transmission of remote sensing of the Earth. The possibility of using fractal functions for image processing, which were transmitted via the satellite radio channel of a spacecraft, is considered. The information obtained by such a system is presented in the form of aerospace images that need to be processed and analyzed in order to obtain information about the objects that are displayed. An algorithm for constructing image encoding--decoding using a class of continuous functions that depend on a finite set of parameters and have fractal properties is investigated. The mathematical model used in fractal image compression is called a system of iterative functions. The encoding process is time consuming because it performs a large number of transformations and mathematical calculations. However, due to this, a high degree of image compression is achieved. This class of functions has an interesting property--knowing the initial sets of numbers, we can easily calculate the value of the function, but when the values of the function are known, it is very difficult to return the initial set of values, because there are a huge number of such combinations. Therefore, in order to de-encode the image, it is necessary to know fractal codes that will help to restore the raster image. [ABSTRACT FROM AUTHOR]

Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding. [ABSTRACT FROM AUTHOR]

Fractal represents a special feature of nature and functional objects. However, fractal based computing can be applied to many research domains because of its fixed property resisted deformation, variable parameters and many unpredictable changes. Theoretical research and practical application of fractal based computing have been hotspots for 30 years and will be continued. There are many pending issues awaiting solutions in this domain, thus this thematic issue containing 14 papers publishes the state-of-the-art developments in theorem and application of fractal based computing, including mathematical analysis and novel engineering applications. The topics contain fractal and multifractal features in application and solution of nonlinear odes and equation. [ABSTRACT FROM AUTHOR]

Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression. [ABSTRACT FROM AUTHOR]

Satellite communication systems are now designed according to an outdated Shannon information theory where all data is transmitted in meaningless bit streams. In this article, a novel noise free digital image communication via satellite has been developed. The proposed scheme is based on fractional compression and a new block cipher cryptosystem based on S8 substitution boxes transformation. The fractal compression technique is selected to compress the aerial images due to its high compression capability. The fractal image compression of the input aerial image is the first stage in the encoding phase. The fractal compression technique divides the original image into two different block sizes called range and domain blocks. The best match between the range and domain blocks is known as the transformation mapping. All the transformation mappings are recorded as the output-compressed file of the input aerial image. [ABSTRACT FROM AUTHOR]

3D image technologies are widely recognized as the next generation of visual presentation considering the achievement of more natural experiences. To produce such images, two cameras are placed in a bit different position. When we seek to compress such images, we need a procedure to compress two images synchronously. In this paper, a procedure is presented for a suitable compression based on fractal compression which shows that we obtain high compression rate with an appropriate image quality; however, since the proposed procedure has a low search speed, we used genetic algorithm to remove the case. [ABSTRACT FROM AUTHOR]

Learning aesthetic judgements is essential for reducing users' fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user's aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists' styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users' preferences. [ABSTRACT FROM AUTHOR]

The development of compression techniques is crucial for several applications that require efficient storage and transmission of large data volumes. Fractal theory has been used in image and video compression due to advantages such as resolution independence, high compression rate, fast decoding, among others. Fractal compression approaches explore the presence of self-similarity to remove data redundancy, allowing high compression while maintaining low quality degradation. Early fractal compression methods presented prohibitive encoding time related to the search for similar regions in the image or video. This work describes a low bit-rate 3D searchless fractal video encoder to perform fast compression with high visual fidelity. Experiments demonstrate that the results of the proposed approach are superior when compared to those obtained by state-of-the-art x264 video encoder at very low bit rates in high motion video sequences. [ABSTRACT FROM AUTHOR]

In recent years, the search for computational systems that classify images based on aesthetic properties has gained momentum. Such systems have a wide range of potential applications, including image search, organization, acquisition and generation. This work explores the use of complexity estimates to predict the aesthetic merit of photographs. We use a set of image metrics and two different classifiers. Our approach classifies images gathered from a photography web site, attempting to reproduce the evaluation made by a group of users. For this purpose, we use complexity estimate metrics based on the encoding size and compression error of JPEG and fractal compression, which are applied to the original value channel and to the images resulting from applying Sobel and Canny filters to this channel. By employing these estimates, in conjunction with the average and standard deviation of the value channel, i.e., 20 features, a success rate of 74.59% was attained. Using the three most influential features yields a success rate of 71.34%, which is competitive with the best results reported in the literature, 71.44%, using the same dataset. [ABSTRACT FROM PUBLISHER]

Fractal image compression is one of the most promising techniques for image compression due to advantages such as resolution independence and fast decompression. It exploits the fact that natural scenes present self-similarity to remove redundancy and obtain high compression rates with smaller quality degradation compared to traditional compression methods. The main drawback of fractal compression is its computationally intensive encoding process, due to the need for searching regions with high similarity in the image. Several approaches have been developed to reduce the computational cost to locate similar regions. In this work, we propose a method based on robust feature descriptors to speed up the encoding time. The use of robust features provides more discriminative and representative information for regions of the image. When the regions are better represented, the search for similar parts of the image can be reduced to focus only on the most likely matching candidates, which leads to reduction on the computational time. Our experimental results show that the use of robust feature descriptors reduces the encoding time while keeping high compression rates and reconstruction quality. [ABSTRACT FROM AUTHOR]

Fractal geometry provides a means for describing and analysing the complexity of various features present in digital images. In this paper, characteristics of Fractal based compression of satellite data have been tested for Indian Remote Sensing (IRS) images (of different bands and resolution). The fidelity and efficiency of the algorithm and its relationship with spatial complexity of images is also evaluated. Results obtained from fractal compression have been compared with popularly used compression methods such as JPEG 2000, WinRar. The effect of bands and pixel resolution on the compression rate has also been examined. The results from this study show that the fractal based compression method provides higher compression rate while maintaining the information content of RS images to a great extent than that of JPEG. This paper also asserts that information loss due to fractal compression is minimal. It may be concluded that fractal technique has many potential advantages for compression of satellite images. [ABSTRACT FROM AUTHOR]

A novel watermarking method is proposed to hide a binary watermark into image files compressed by fractal block coding. This watermarking method utilizes a special type of orthogonalization fractal coding method where the fractal affine transform is determined by the range block mean and contrast scaling. Such orthogonalization fractal decoding is a mean-invariant iteration. In contrast, the fractal parameters of classical fractal compression are very sensitive to any change of domain block pool and to common signal and geometric distortion. Hence, it is impossible to directly place a watermark in fractal parameters. The proposed watermark embedding procedure inserts a permutated pseudo-random binary sequence into the quantized range block means. The watermark is detected by computing the correlation coefficient between the original and the extracted watermark. Experimental results show that the proposed fractal watermarking scheme is robust against common signal and geometric distortion such as JPEG compression, low-pass filtering, rescaling, and clipping. [ABSTRACT FROM PUBLISHER]

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, luminance offset and contrast scaling parameter are typically used as the fractal indices. However, luminance offset and contrast scaling parameter are strongly correlated. In this paper, we prove that range block mean and contrast scaling parameters are independent. Based on this independence, we propose four statistical indices for efficient image retrieval. In addition, we propose an efficient hierarchical indexing strategy based on the de and ac component analysis. Experimental results on a database of 416 texture images, created by decomposing 26 images, indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods. [ABSTRACT FROM PUBLISHER]