Angelos Amanatiadis

A survey on evaluation methods for image interpolation

Image interpolation is applied to Euclidean, affine and projective transformations in numerous imaging applications. However, due to the unique characteristics and wide applications of image interpolation, a separate study of their evaluation methods is crucial. The paper studies different existing methods for the evaluation of image interpolation techniques. Furthermore, an evaluation method utilizing ground truth images for the comparisons is proposed. Two main classes of analysis are proposed as the basis for the assessments: performance evaluation and cost evaluation. The presented methods are briefly described, followed by comparative discussions. This survey provides information for the appropriate use of the existing evaluation methods and their improvement, assisting also in the designing of new evaluation methods and techniques.

Digital Image Scaling

The proposed scaling algorithm outperforms other standard and widely used scaling techniques. The algorithm uses a mask of maximum four pixels and calculates the final luminosity of each pixel combining two factors; the percentage of area that mask covers from each source pixel and the difference in luminosity between the source pixels. The interpolation is capable of scaling both grey-scale and color images of any resolution in any scaling factor. Its key characteristics and low complexity make the interpolation very fast and capable of real time implementation. The performance results in a variety of standard tests are presented and compared to other scaling algorithms

Image moment invariants as local features for content based image retrieval using the Bag-of-Visual-Words model

A new image descriptor specifically designed for image retrieval tasks is introduced.Evaluation of affine moment invariants in the area of image retrieval.The usage of image chromaticities improves the overall retrieval performance. This paper presents an image retrieval framework that uses affine image moment invariants as descriptors of local image areas. Detailed feature vectors are generated by feeding the produced moments into a Bag-of-Visual-Words representation. Image moment invariants have been selected for their compact representation of image areas as well as due to their ability to remain unchanged under affine image transformations. Three different setups were examined in order to evaluate and discuss the overall approach. The retrieval results are promising compared with other widely used local descriptors, allowing the proposed framework to serve as a reference point for future image moment local descriptors applied to the general task of content based image retrieval.

Digital Image Stabilization by Independent Component Analysis

In this paper, we propose a novel digital-image-stabilization scheme based on independent component analysis (ICA). The method utilizes ICA and information obtained from the image sequence to deconvolve the ego-motion from the unwanted motion of the sequence. We notice that the motion observed in image sequences captured from consumer electronics such as handheld cameras and third-generation mobile phones is mainly caused by two independent motions: the camera motion (ego-motion) and the undesired hand jitter (high-frequency motion). The extensive and successful application of ICA in both the statistical and the signal processing community has helped us to realize that the independence property of these two primary signals facilitates the application of ICA for deconvolution by maximizing their statistical independence. Sets of estimated local motion vectors of the sequence are introduced to the ICA system for separation. Subsequently, we process the unmixed motion vectors to classify the signals into ego-motion and high-frequency motion. Subsequently, when the permutation ambiguity is resolved, the appropriate sign and energy are assigned to the ego-motion vector, resulting in the stabilized image sequence. Experimental results have shown that, apart from the successful deconvolution of the two different motions, the proposed scheme exhibits superior performance compared to other digital-image-stabilization algorithms.

Binary Image 2D Shape Learning and Recognition Based on Lattice-Computing (LC) Techniques

This work introduces a Type-II fuzzy lattice reasoning (FLRtypeII) scheme for learning/generalizing novel 2D shape representations. A 2D shape is represented as an element—induced from populations of three different shape descriptors—in the product lattice (F3,⪯), where (F,⪯) denotes the lattice of Type-I intervals’ numbers (INs). Learning is carried out by inducing Type-II INs, i.e. intervals in (F,⪯). Our proposed techniques compare well with alternative classification methods from the literature in three benchmark classification problems. Competitive advantages include an accommodation of granular data as well as a visual representation of a class. We discuss extensions to gray/color images, etc.

Design and Implementation of a Fuzzy Area-Based Image-Scaling Technique

In this paper, we propose the design and implementation of an interpolation scheme for performing image scaling by utilizing a dynamic mask combined with a sophisticated neighborhood averaging fuzzy algorithm. The functions that contribute to the final interpolated image are the areas of the input pixels, overlapped by a dynamic mask, and the difference in intensity between the input pixels. Fuzzy if-then rules for these two functions are presented to carry out the interpolation task. Simulation results have shown a fine high-frequency response and a low interpolation error, in comparison with other widely used algorithms. The interpolation can be applied to both gray-scale and color images for any scaling factor. The proposed hardware structure is implemented in a field-programmable gate array (FPGA) chip and is based on a sequence of pipeline stages and parallel processing to minimize computation times. The fuzzy image interpolation implementation combines a fuzzy inference system and an image-interpolation technique in one hardware system. Its main features are the ability to accurately approximate the Gaussian membership functions used by the fuzzy inference system with very few memory requirements and its high-frequency performance of 65 MHz, making it appropriate for real-time imaging applications. The system can magnify gray-scale images of up to 10-bit resolution. The maximum input image size is 1024 times 1024 pixels for a maximum of 800% magnification.

An integrated architecture for adaptive image stabilization in zooming operation

This paper presents a novel architecture for integrating digital stabilizer with the camera zooming process. The proposed architecture scheme allows integration for both optical and digital zooming operation scenarios. The stabilizer adjusts its operational parameters from the current optical zooming status for a refined local motion estimation. Subsequently, the global motion compensation vectors, produced by the digital stabilizer, are sent to the digital zooming system, where the image compensation is merged within the interpolation process. Experimental results indicate that the proposed architecture can improve not only the quantitative performance of digital stabilization but also the computational efficiency when zooming is employed.

A fuzzy multi-sensor architecture for indoor navigation

This paper presents an indoor navigation system based on sensor data from first responder wearable modules. The proposed system integrates data from an inertial sensor, a digital camera and a radio frequency identification device using a sophisticated fuzzy algorithm. To improve the navigation accuracy, different types of first responder activities and operational conditions were examined and classified according to extracted qualitative attributes. The vertical acceleration data, which indicates the periodic vibration during gait cycle, is used to evaluate the accuracy of the inertial based navigation subsystem. The amount of strong feature correspondences assess the quality of the three-dimensional scene knowledge from digital camera feedback. Finally, the qualitative attribute, in order to evaluate the efficiency of the radio frequency identification subsystem, is the degree of probability of each location estimate. Fuzzy if-then rules are then applied to these three attributes in order to carry out the fusion task. Simulation results based on the proposed architecture have shown better navigation effectiveness and lower positioning error compared with the used stand alone navigation systems.

A comparative study of invariant descriptors for shape retrieval

This paper presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Specifically, we studied Fourier, angular radial transform and image moment descriptors for shape representation. Since shape is one of the most widely used image feature exploited in content-based image retrieval systems, we studied for each descriptor, the number of coefficients needed for indexing and their retrieval performance. Results showed that moment descriptors present the best performance in both terms of shape representation quality as well as in the amount of required coefficients.

Methods and techniques for intelligent navigation and manipulation for bomb disposal and rescue operations

Handing a teleoperated robotic mechanism demands special skills and involves particular problems. Especially in cases of robots dealing with rescue operations or bomb disposal. In such cases any lost in communications might arise unpredictable results. Also either a bomb or a survivor need attentional handling. In this paper we describe automatic methods and techniques developed on a multifunctional teleoperated robot. These intend to assist both the robot and the human operator in accomplishing their mission towards rescue or bomb disposal.