Retinex theory for color image enhancement: A systematic review

A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images contrast enhancement. In this review, the classical theory of Retinex has been covered. Moreover, advance and improved techniques of Retinex, proposed in the literature, have been addressed. Strength and weakness aspects of each technique are discussed and compared. An optimum parameter is needed to be determined to define the image degradation level. Such parameter determination would help in quantifying the amount of adjustment in the Retinex theory. Thus, a robust framework to modify the reflectance component of the Retinex theory can be developed to enhance the overall quality of color images

Unconstrained Iris Recognition

This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical features (curves and edges) from the degraded iris images addresses the weakness of 1D texture features extracted by the classical methods based on textural analysis wavelet transform. Our experiments show significant improvements in the segmentation and recognition accuracy when compared to the-state-of-the-art results

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system

Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%

Propuesta de arquitectura y circuitos para la mejora del rango dinámico de sistemas de visión en un chip diseñados en tecnologías CMOS profundamente submicrométrica

El trabajo presentado en esta tesis trata de proponer nuevas técnicas para la expansión del rango dinámico en sensores electrónicos de imagen. En este caso, hemos dirigido nuestros estudios hacia la posibilidad de proveer dicha funcionalidad en un solo chip. Esto es, sin necesitar ningún soporte externo de hardware o software, formando un tipo de sistema denominado Sistema de Visión en un Chip (VSoC). El rango dinámico de los sensores electrónicos de imagen se define como el cociente entre la máxima y la mínima iluminación medible. Para mejorar este factor surgen dos opciones. La primera, reducir la mínima luz medible mediante la disminución del ruido en el sensor de imagen. La segunda, incrementar la máxima luz medible mediante la extensión del límite de saturación del sensor. Cronológicamente, nuestra primera opción para mejorar el rango dinámico se basó en reducir el ruido. Varias opciones se pueden tomar para mejorar la figura de mérito de ruido del sistema: reducir el ruido usando una tecnología CIS o usar circuitos dedicados, tales como calibración o auto cero. Sin embargo, el uso de técnicas de circuitos implica limitaciones, las cuales sólo pueden ser resueltas mediante el uso de tecnologías no estándar que están especialmente diseñadas para este propósito. La tecnología CIS utilizada está dirigida a la mejora de la calidad y las posibilidades del proceso de fotosensado, tales como sensibilidad, ruido, permitir imagen a color, etcétera. Para estudiar las características de la tecnología en más detalle, se diseñó un chip de test, lo cual permite extraer las mejores opciones para futuros píxeles. No obstante, a pesar de un satisfactorio comportamiento general, las medidas referentes al rango dinámico indicaron que la mejora de este mediante sólo tecnología CIS es muy limitada. Es decir, la mejora de la corriente oscura del sensor no es suficiente para nuestro propósito. Para una mayor mejora del rango dinámico se deben incluir circuitos dentro del píxel. No obstante, las tecnologías CIS usualmente no permiten nada más que transistores NMOS al lado del fotosensor, lo cual implica una seria restricción en el circuito a usar. Como resultado, el diseño de un sensor de imagen con mejora del rango dinámico en tecnologías CIS fue desestimado en favor del uso de una tecnología estándar, la cual da más flexibilidad al diseño del píxel. En tecnologías estándar, es posible introducir una alta funcionalidad usando circuitos dentro del píxel, lo cual permite técnicas avanzadas para extender el límite de saturación de los sensores de imagen. Para este objetivo surgen dos opciones: adquisición lineal o compresiva. Si se realiza una adquisición lineal, se generarán una gran cantidad de datos por cada píxel. Como ejemplo, si el rango dinámico de la escena es de 120dB al menos se necesitarían 20-bits/píxel, log2(10120/20)=19.93, para la representación binaria de este rango dinámico. Esto necesitaría de amplios recursos para procesar esta gran cantidad de datos, y un gran ancho de banda para moverlos al circuito de procesamiento. Para evitar estos problemas, los sensores de imagen de alto rango dinámico usualmente optan por utilizar una adquisición compresiva de la luz. Por lo tanto, esto implica dos tareas a realizar: la captura y la compresión de la imagen. La captura de la imagen se realiza a nivel de píxel, en el dispositivo fotosensor, mientras que la compresión de la imagen puede ser realizada a nivel de píxel, de sistema, o mediante postprocesado externo. Usando el postprocesado, existe un campo de investigación que estudia la compresión de escenas de alto rango dinámico mientras se mantienen los detalles, produciendo un resultado apropiado para la percepción humana en monitores convencionales de bajo rango dinámico. Esto se denomina Mapeo de Tonos (Tone Mapping) y usualmente emplea solo 8-bits/píxel para las representaciones de imágenes, ya que éste es el estándar para las imágenes de bajo rango dinámico. Los píxeles de adquisición compresiva, por su parte, realizan una compresión que no es dependiente de la escena de alto rango dinámico a capturar, lo cual implica una baja compresión o pérdida de detalles y contraste. Para evitar estas desventajas, en este trabajo, se presenta un píxel de adquisición compresiva que aplica una técnica de mapeo de tonos que permite la captura de imágenes ya comprimidas de una forma optimizada para mantener los detalles y el contraste, produciendo una cantidad muy reducida de datos. Las técnicas de mapeo de tonos ejecutan normalmente postprocesamiento mediante software en un ordenador sobre imágenes capturadas sin compresión, las cuales contienen una gran cantidad de datos. Estas técnicas han pertenecido tradicionalmente al campo de los gráficos por ordenador debido a la gran cantidad de esfuerzo computacional que requieren. Sin embargo, hemos desarrollado un nuevo algoritmo de mapeo de tonos especialmente adaptado para aprovechar los circuitos dentro del píxel y que requiere un reducido esfuerzo de computación fuera de la matriz de píxeles, lo cual permite el desarrollo de un sistema de visión en un solo chip. El nuevo algoritmo de mapeo de tonos, el cual es un concepto matemático que puede ser simulado mediante software, se ha implementado también en un chip. Sin embargo, para esta implementación hardware en un chip son necesarias algunas adaptaciones y técnicas avanzadas de diseño, que constituyen en sí mismas otra de las contribuciones de este trabajo. Más aún, debido a la nueva funcionalidad, se han desarrollado modificaciones de los típicos métodos a usar para la caracterización y captura de imágenes

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations