Vassilios Vonikakis

Robust document binarization with OFF center-surround cells

This paper presents a new method for degraded-document binarization, inspired by the attributes of the Human Visual System (HVS). It can deal with various types of degradations, such as uneven illumination, shadows, low contrast, smears, and heavy noise densities. The proposed algorithm combines the characteristics of the OFF center-surround cells of the HVS with the classic Otsu binarization technique. Cells of two different scales are combined, increasing the efficiency of the algorithm and reducing the extracted noise in the final output. A new response function, which regulates the output of the cell according to the local contrast and the local lighting conditions is also introduced. The Otsu technique is used to binarize the outputs of the OFF center-surround cells. Quantitative experiments performed on a set of various computer-generated degradations, such as noise, shadow, and low contrast demonstrate the superior performance of the proposed method against six other well-established techniques. Qualitative and OCR comparisons also confirm these results.

Enhancement of Perceptually Salient Contours using a Parallel Artificial Cortical Network

In this paper we present a parallel artificial cortical network inspired by the Human visual system, which enhances the salient contours of an image. The network consists of independent processing elements, which are organized into hypercolumns. They process concurrently the distinct orientations of all the edges of the image. These processing elements are a new set of orientation kernels appropriate for the discrete lattice of the hypercolumns. The Gestalt laws of proximity and continuity that describe the process of saliency extraction in the human brain are encoded by means of weights. These weights interconnect the kernels according to a novel connection pattern based on co-exponentiality. The output of every kernel is modulated by the outputs of its neighboring kernels, according to a new affinity function. This function takes into account the degree of difference between the facilitation of the two lobes of the kernel. Saliency enhancement results as a consequence of the local interactions between the kernels. The network was tested on real and synthetic images and displays promising results for both. Comparisons with other methods with the same scope, demonstrate that the proposed method performs adequately. Furthermore it exhibits O(N) complexity with execution times that have never been reported by any other method so far, even though it is executed on a conventional PC

Fast automatic compensation of under/over-exposured image regions

This paper presents a new algorithm for spatially modulated tone mapping in Standard Dynamic Range (SDR) images. The method performs image enhancement by lightening the tones in the under-exposured regions while darkening the tones in the over-exposured, without affecting the correctly exposured ones. The tone mapping function is inspired by the shunting characteristics of the center-surround cells of the Human Visual System (HVS). This function is modulated differently for every pixel, according to its surround. The surround is calculated using a new approach, based on the oriented cells of the HVS, which allows it to adapt its shape to the local contents of the image and, thus, minimize the halo effects. The method has low complexity and can render 1MPixel images in approximately 1 second when executed by a conventional PC.

Multi-scale image contrast enhancement

This paper presents a new method suitable for the contrast enhancement of digital images. The proposed algorithm employs the center-surround architecture of the human visual system (HVS) for the comparison of the central pixel with the mean intensity of its surround. These two values are inputs to a mapping function, which calculates the new intensity value of the central pixel, in a way that increases small local intensity differences while preserving the large ones. The proposed method is applied to three spatial scales by employing different surround sizes. As a result, contrast enhancement is performed in different spatial frequencies of the original image. The proposed method is tested against other similar methods and is found to exhibit better results in the enhancement of low-contrast images, such as medical, aerial or foggy scenes, revealing visual information that otherwise would not be available to the human observer.

Extraction of salient contours in color images

In this paper we present an artificial cortical network, inspired by the Human Visual System (HVS), which extracts the salient contours in color images. Similarly to the primary visual cortex, the network consists of orientation hypercolumns. Lateral connections between the hypercolumns are modeled by a new connection pattern based on co-exponentiality. The initial color edges of the image are extracted in a way inspired by the double-opponent cells of the HVS. These edges are inputs to the network, which outputs the salient contours based on the local interactions between the hypercolumns. The proposed network was tested on real color images and displayed promising performance, with execution times small enough even for a conventional personal computer.

On the evaluation of illumination compensation algorithms

This paper presents a comparison framework for algorithms that can diminish the effects of illumination in images. Its main objective is to reveal the positive and negative characteristics of such algorithms, allowing researchers to select the most appropriate one for their target application. The proposed framework utilizes artificial illumination degradations on real images, which are then processed by the tested algorithms. The results are evaluated by an ensemble of performance metrics, highlighting the various characteristics of the algorithms across a range of different image attributes. The proposed framework represents a useful tool for the selection of illumination compensation algorithms due to a) its quantitative nature, b) its multifaceted analysis and c) its easy reproducibility. The validity of the proposed framework is tested by applying it to the enhancement results of four illumination compensation algorithms, which are used as preprocessing in two classic computer vision applications. The improvements brought about by the algorithms are in accordance with the predictions of the proposed framework.

Calculating Retinal Contrast from Scene Content: A Program

This paper describes a computer program for calculating the contrast image on the human retina from an array of scene luminances. We used achromatic transparency targets and measured test targets luminances with meters. We used the CIE standard Glare Spread Function (GSF) to calculate the array of retinal contrast. This paper describes the CIE standard, the calculation and the analysis techniques comparing the calculated retinal image with observer data. The paper also describes in detail the techniques of accurate measurements of HDR scenes, conversion of measurements to input data arrays, calculation of the retinal image, including open source MATLAB code, pseudocolor visualization of HDR images that exceed the range of standard displays, and comparison of observed sensations with retinal stimuli.

Recognition of 2-d shapes inspired by the human visual system

This paper presents a new method suitable for shape recognition, inspired by the Primary Visual Cortex of the Human Visual System. It extracts a description for 2-dimensional shapes with a closed contour, regardless of their size, rotation and position, with low computational cost. The paper introduces a new computational approach to the modeling of the hypercolumns of the Primary Visual Cortex, which requires significantly less computational burden and which is highly parallel. A new shape descriptor based on the relative angles of an object is also proposed. It produces close results for different shapes of the same object, it is proportion-flexible and it can identify distorted shapes correctly. Experimental results prove that the method is adequate for shape-based image retrieval and classification, as well as for efficiently storage of edges and line drawings.

Real-Time Biologically-Inspired Image Exposure Correction

This chapter presents a real-time FPGA implementation of a biologically-inspired image enhancement algorithm. The algorithm compensates for the under/over-exposed image regions, emerging when High Dynamic Range (HDR) scenes are captured by contemporary imaging devices. The transformations of the original algorithm, which are necessary in order to meet the requirements of an FPGA-based hardware system, are presented in detail. The proposed implementation, which is synthesized in Altera’s Stratix II GX: EP2SGX130GF1508C5 FPGA device, features pipeline architecture, allowing the real-time rendering of color video sequences (25fps) with frame sizes up to 2.5Mpixels.