Uncontrolled Illumination Conditions Research Articles

INTEGRATED 3D ACQUISITION OF COMPLEX WOODEN ARTEFACTS: THE PIFFETTI'S LIBRARY IN QUIRINALE PALACE (ROME)

Abstract. Integrated surveying with active and passive systems is a well-established process for understanding artifacts at different scales and levels of complexity. But some acquisition problems still exist, related to the presence of non-optically cooperating materials, reflective surfaces, and non-homogeneous and uncontrolled illumination conditions. Especially in these cases, the integration between different 3D acquisition techniques allows us to test the response of different instruments concerning the boundary conditions, going to compare the results. This activity highlights the pros and cons of each technique, identifying the right balance for future applications with the same conditions. The case study analyzed in the article is the Piffetti Library, a wooden work from the first half of the 1800s present at the Quirinale Palace (Rome). The artifact shows multiple levels of environmental complexity (small space with movement constraints and uncontrollable light), and it is composed of several wood pieces with a very glossy finish. The presence of shelving, free-form surfaces, and sculptural details requires careful planning to survey the artifact with consistent resolution. The use of different active and passive acquisition methods is tested, defining an integrated methodology coherent with the complexity of the artifact and the context in which it is positioned. The dual purpose is to define a possible replicable protocol, arriving at the definition of reliable 3D data for subsequent analysis and virtual reconstructions.

Analysis of Image Preprocessing and Binarization Methods for OCR-Based Detection and Classification of Electronic Integrated Circuit Labeling

Automatic recognition and classification of electronic integrated circuits based on optical character recognition combined with the analysis of the shape of their housings are essential to machine vision methods supporting the production of electronic parts, especially small-volume ones in the through-hole technology, characteristic of printed circuit boards. Since such methods utilize binary images, applying appropriate image preprocessing and thresholding methods significantly influences the obtained results, particularly in uncontrolled illumination conditions. Therefore, the examination of various adaptive image binarization algorithms for this purpose is conducted in this paper, together with the experimental verification of the proposed method based on the pixel voting approach.

An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning

A common problem for machine vision applications is uncontrolled illumination conditions that cause undesired artifacts on sensorial data. For instance, quality inspection using color cameras, while having wide industrial application, requires manual illumination adjustment and is severely affected by external lighting sources and the physical properties of the inspected object. To overcome this problem, we propose an autonomous illumination solution, that adjusts illumination via a Deep Reinforcement Learning (DRL) agent following a goal-oriented reward that takes into account image entropy and specularity. The system is validated in a challenging vehicle documentation use case where vehicle images are captured under various lighting conditions using a camera and an in-house built illumination system. The DRL agent learns to control illumination levels directly from high-dimensional visual inputs by mapping the interactions from the environment to the reward-driven control actions of the illumination system, targeting an optimal illumination zone even under the appearance of abrupt illumination changes in the environment.

An improved Weber-face-based method for face recognition under uncontrolled illumination conditions

The paper presents a new face recognition system robust to illumination variations and moderate occlusion. Two main contributions are discussed. First, we introduce an approach based on contrast equalisation (CE) to improve the traditional Weber-face (WF) technique and make it more robust. Second, we use the local binary patterns (LBP) and local phase quantisation (LPQ) descriptors to make the Weber-face method more resilient to variations in illumination by exploiting both spatial and frequency domains information. By combining the two descriptors, enhanced facial features are obtained showing more discriminating power for variable lighting conditions as well as occlusion. The concept of complementing the WF model with spatial-frequency descriptors is novel and shown to result in a robust system resilient to changing lighting conditions, variations in pose, and occlusion. The method was compared to a number of existing techniques over three public databases. The proposed algorithm outperformed existing techniques under challenging environments.

An improved Weber-face-based method for face recognition under uncontrolled illumination conditions

The paper presents a new face recognition system robust to illumination variations and moderate occlusion. Two main contributions are discussed. First, we introduce an approach based on contrast equalisation (CE) to improve the traditional Weber-face (WF) technique and make it more robust. Second, we use the local binary patterns (LBP) and local phase quantisation (LPQ) descriptors to make the Weber-face method more resilient to variations in illumination by exploiting both spatial and frequency domains information. By combining the two descriptors, enhanced facial features are obtained showing more discriminating power for variable lighting conditions as well as occlusion. The concept of complementing the WF model with spatial-frequency descriptors is novel and shown to result in a robust system resilient to changing lighting conditions, variations in pose, and occlusion. The method was compared to a number of existing techniques over three public databases. The proposed algorithm outperformed existing techniques under challenging environments.

On the use of ear and profile faces for distinguishing identical twins and nontwins

AbstractThis study aims to measure the efficiency of ear and profile face in distinguishing identical twins under identification and verification modes. In addition, to distinguish identical twins by ear and profile face separately, we propose to fuse these traits with all possible binary combinations of left ear, left profile face, right ear, and right profile face. Fusion is implemented by score‐level fusion and decision‐level fusion techniques in the proposed method. Additionally, feature‐level fusion is used for comparison. All experiments in this paper are also implemented on nontwins individuals, and the recognition performance of twins and nontwins are compared. Local binary patterns, local phase quantization, and binarized statistical image features approaches are used as texture‐based descriptors for feature extraction process. Images under controlled and uncontrolled lighting are tested. Ear and profile images from ND‐TWINS‐2009‐2010 dataset are used in the experiments. The experimental results show that the proposed method is more accurate and reliable than using ear or profile face images separately. The performance of the proposed method for recognizing identical twins as recognition rate is 100% and 99.45%, and equal error rates are 0.54% and 1.63% in controlled and uncontrolled illumination conditions, respectively.

A novel illumination-robust local descriptor based on sparse linear regression

Robust face recognition under uncontrolled illumination conditions is an important problem for real face recognition systems. In this paper, we introduce a novel illumination-robust local descriptor named Sparse Linear Regression Binary (SLRB) descriptor. The SLRB descriptor is a bit string by binarizing the sparse linear regression coefficients in a local block. It is an illumination-insensitive descriptor based on the locally linear consistency assumption under the Lambertian reflectance model. We use the cosine similarity and Hamming similarity as the similarity measure for the SLRB descriptor of two different images respectively. Experimental results on the Extended Yale-B and CMU-PIE face database show a promising performance compared to the existing representative approaches.

Two-view line matching algorithm based on context and appearance in low-textured images

A novel approach for line detection and matching is proposed, aimed at achieving good performance with low-textured scenes, under uncontrolled illumination conditions. Line detection is performed by means of phase-based edge detector over Gaussian scale-space, followed by a multi-scale fusion stage which has been proven to be profitable in minimizing the number of fragmented and overlapped segments. Line matching is performed by an iterative process that uses structural information collected through the use of different line neighborhoods, making the set of matched lines grow robustly at each iteration. Results show that this approach is suitable to deal with low-textured scenes, and also robust under a wide variety of image transformations.

Generalized Weber-face for illumination-robust face recognition

Robust face recognition under uncontrolled illumination conditions is one of the key challenges for real-time face recognition systems. Weber-face (WF) is an illumination insensitive face representation based on Weber׳ law. In this letter, we develop a generalized Weber-face (GWF) which extracts the statistics of multi-scale information from face images. By assigning different weights to the inner-ground and outer-ground we further develop a weighted GWF (wGWF) version. Based on our experiments on the extended Yale-B and FERET face database we show that the proposed methods are robust to illumination variations and can obtain promising performance comparable with existing approaches.

A biologically inspired scale-space for illumination invariant feature detection

This paper presents a new illumination invariant operator, combining the nonlinear characteristics of biological center-surround cells with the classic difference of Gaussians operator. It specifically targets the underexposed image regions, exhibiting increased sensitivity to low contrast, while not affecting performance in the correctly exposed ones. The proposed operator can be used to create a scale-space, which in turn can be a part of a SIFT-based detector module. The main advantage of this illumination invariant scale-space is that, using just one global threshold, keypoints can be detected in both dark and bright image regions. In order to evaluate the degree of illumination invariance that the proposed, as well as other, existing, operators exhibit, a new benchmark dataset is introduced. It features a greater variety of imaging conditions, compared to existing databases, containing real scenes under various degrees and combinations of uniform and non-uniform illumination. Experimental results show that the proposed detector extracts a greater number of features, with a high level of repeatability, compared to other approaches, for both uniform and non-uniform illumination. This, along with its simple implementation, renders the proposed feature detector particularly appropriate for outdoor vision systems, working in environments under uncontrolled illumination conditions.

Comparing face recognition algorithms to humans on challenging tasks

We compared face identification by humans and machines using images taken under a variety of uncontrolled illumination conditions in both indoor and outdoor settings. Natural variations in a person's day-to-day appearance (e.g., hair style, facial expression, hats, glasses, etc.) contributed to the difficulty of the task. Both humans and machines matched the identity of people (same or different) in pairs of frontal view face images. The degree of difficulty introduced by photometric and appearance-based variability was estimated using a face recognition algorithm created by fusing three top-performing algorithms from a recent international competition. The algorithm computed similarity scores for a constant set of same-identity and different-identity pairings from multiple images. Image pairs were assigned to good , moderate , and poor accuracy groups by ranking the similarity scores for each identity pairing, and dividing these rankings into three strata. This procedure isolated the role of photometric variables from the effects of the distinctiveness of particular identities. Algorithm performance for these constant identity pairings varied dramatically across the groups. In a series of experiments, humans matched image pairs from the good, moderate, and poor conditions, rating the likelihood that the images were of the same person (1: sure same - 5: sure different). Algorithms were more accurate than humans in the good and moderate conditions, but were comparable to humans in the poor accuracy condition. To date, these are the most variable illumination- and appearance-based recognition conditions on which humans and machines have been compared. The finding that machines were never less accurate than humans on these challenging frontal images suggests that face recognition systems may be ready for applications with comparable difficulty. We speculate that the superiority of algorithms over humans in the less challenging conditions may be due to the algorithms' use of detailed, view-specific identity information. Humans may consider this information less important due to its limited potential for robust generalization in suboptimal viewing conditions.

Photographing Paintings by Image Fusion

This paper addresses the problem of obtaining a quality photograph of a painting by multi-image fusion methods. The problem is particularly challenging because of the uncontrolled illumination conditions and of the destructive reflection speckle present in most photographs of paintings. A fully automatic image processing chain is described that, starting from several bursts of a painting taken under different angles, permits one to obtain the best possible result by eliminating highlights and motion blur by robust statistics, reducing noise by fusion, and compensating optical distortion in the registration process. This image fusion method is applicable to photographs of a painting taken with a hand-held camera without any particular setup. It works under bad lighting conditions and eliminates motion blur, even when the painting is protected by a glass screen creating structured reflections of the room. The careful discussion of each step of the processing chain also permits one to review and discuss the efficiency of the image fusion tools recently proposed in the literature and insert several new ones in the chain.

Face recognition based on improved Retinex and sparse representation

In this paper, we proposed a method based on improved Retinex theory and sparse representation to deal with the difficulties for face recognition under inhomogeneous illumination. In our work, the total variation model was introduced to optimize the parameters of Retinex and the illumination insensitive features were extracted as the dictionary of sparse representation. Finally, the facial images could be recognized by the proposed algorithm. The experimental results on different benchmark face databases indicated that the proposed approach could be more efficient than traditional methods for face images under uncontrolled illumination conditions.

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved.

Modeling hair from multiple views

In this paper, we propose a novel image-based approach to model hair geometry from images taken at multiple viewpoints. Unlike previous hair modeling techniques that require intensive user interactions or rely on special capturing setup under controlled illumination conditions, we use a handheld camera to capture hair images under uncontrolled illumination conditions. Our multi-view approach is natural and flexible for capturing. It also provides inherent strong and accurate geometric constraints to recover hair models.In our approach, the hair fibers are synthesized from local image orientations. Each synthesized fiber segment is validated and optimally triangulated from all visible views. The hair volume and the visibility of synthesized fibers can also be reliably estimated from multiple views. Flexibility of acquisition, little user interaction, and high quality results of recovered complex hair models are the key advantages of our method.

Textures revisited

We describe texture generation methods for complex objects. Recently developed 3D scanning devices and high-resolution cameras can capture the complex geometry of an object and yield high-resolution images. However, generating a textured model from this input data is still a difficult problem. This task is divided into three subproblems: parameterization, texture combination, and texture restoration. A low-distortion parameterization method is presented, which minimizes geometry stretch energy. Photographs of the object taken from multiple viewpoints under modestly uncontrolled illumination conditions are merged into a seamless texture using our new texture combination method. We also demonstrate a texture restoration method that can fill in missing pixel information when the input photographs do not provide sufficient information to cover the entire surface due to self-occlusion or registration errors. Our methods are fully automatic, except for the registration process between a 3D model and input photographs. We demonstrate the application of our method to human face models for evaluation. The techniques presented in this paper make a consistent and complete pipeline to generate the texture of a complex object.