Using eigencolor normalization for illumination-invariant color object recognition

Color is one of salient features for color object recognition, however, the colors of object images sensitively depend on scene illumination. To overcome the lighting dependency problem, a color constancy or color normalization method has to be used. This paper presents a color image normalization method, called eigencolor normalization, which consists of two phases as follows. First, the compacting method, which was originally used for compensating the adverse effect due to shape distortion for 2-D planar objects, is exploited for 3-D color space to make the color distribution less correlated and more compact. Second, the compact color image is further normalized by rotating the histogram to align with the reference axis computed. Consequently, the object colors are transformed into a new color space, called eigencolor space, which reflects the inherent colors of the object and is more invariant to illumination changes. Experimental results show that our eigencolor normalization method is superior to other existing color constancy or color normalization schemes on achieving more accurate color object recognition.     

Object-based illumination classification

Estimation of scene illumination from a single image or an image sequence has been widely studied in computer vision. The approach presented in this paper, introduces two new issues: (1) illumination classification is performed rather than illumination estimation; (2) an object-based approach is used for illumination evaluation. Thus, pixels associated with an object are considered in the illumination estimation process using the object's spectral characteristics. Simulation and real image experiments, show that the object-based approach indeed improves performance over standard illumination classification.     

An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition

Multimedia Tools and Applications - This paper presents an efficient hybrid DWT-DCT based illumination normalization technique for face recognition. In a face image, illumination usually changes...     

Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion

An efficient method for face recognition which is robust under illumination variations is proposed. The proposed method achieves the illumination invariants based on the illumination-reflection model employing local matching for best classification. Different filters have been tested to achieve the reflectance part of the image, which is illumination invariant, and maximum filter is suggested as the best method for this purpose. A set of adaptively weighted classifiers vote on different sub-images of each input image and a decision is made based on their votes. Image entropy and mutual information are used as weight factors. The proposed method does not need any prior information about the face shape or illumination and can be applied on each image separately. Unlike most available methods, our method does not need multiple images in training stage to get the illumination invariants. Support vector machines and k-nearest neighbors methods are used as classifier. Several experiments are performed on Yale B, Extended Yale B and CMU-PIE databases. Recognition results show that the proposed method is suitable for efficient face recognition under illumination variations.     

Adapting softcopy color reproduction to ambient illumination

Abstract— The perceived colors of an image seen on a self‐luminous display are affected by ambient illumination. The ambient light reflected from the display faceplate is mixed with the image‐forming light emitted by the display. In addition to this direct physical effect of viewing flare, ambient illumination causes perceptual changes by affecting the adaptation state of the viewer's visual system. This paper first discusses these effects and how they can be compensated, outlining a display system able to adjust its output based on prevailing lighting conditions. The emphasis is on compensating for the perceptual effects of viewing conditions by means of color‐appearance modeling. The effects of varying the degree of chromatic adaptation parameter D and the surround compensation parameters c and N_c of the CIECAM97s color‐appearance model were studied in psychophysical experiments. In these memory‐based paired comparison experiments, the observers judged the appearance of images shown on an LCD under three different ambient‐illumination conditions. The dependence of the optimal parameter values on the level of ambient illumination was evident. The results of the final experiment, using a category scaling technique, showed the benefit of using the color‐appearance model with the optimized parameters in compensating for the perceptual changes caused by varying ambient illumination.     

基于特征分类的麻将图像光照不均校正方法

针对麻将排版视觉检测装置采集到的图像存在光照不均问题,提出了一种基于麻将特征分类的自适应图像校正方法。通过对麻将的排版特征进行分析,采用背景差分法和投影法将整体排版麻将灰度图从采集图像中分割出来;将整体排版麻将划分为多个单个麻将,并通过对单个麻将的光照特征进行分析,将单个麻将按光照类型分为均匀类麻将、阴影类麻将和高光类麻将共三类;对阴影类麻将和高光类麻将进行均值归一化处理,再对麻将图像进行整体锐化,并结合色度分量增强麻将的图案特征;根据麻将噪声特征,基于空域幅值统计去除阴影类麻将中的图像噪点。实验结果表明,与现有方法相比,该方法能更好地改善麻将图像光照不均现象,处理后的图像有效地提高了麻将排版识别率。     

Robust face recognition using 2D and 3D data: Pose and illumination compensation

The paper addresses the problem of face recognition under varying pose and illumination. Robustness to appearance variations is achieved not only by using a combination of a 2D color and a 3D image of the face, but mainly by using face geometry information to cope with pose and illumination variations that inhibit the performance of 2D face recognition. A face normalization approach is proposed, which unlike state-of-the-art techniques is computationally efficient and does not require an extended training set. Experimental results on a large data set show that template-based face recognition performance is significantly benefited from the application of the proposed normalization algorithms prior to classification.     

PIAFusion: A progressive infrared and visible image fusion network based on illumination aware

Infrared and visible image fusion aims to synthesize a single fused image containing salient targets and abundant texture details even under extreme illumination conditions. However, existing image fusion algorithms fail to take the illumination factor into account in the modeling process. In this paper, we propose a progressive image fusion network based on illumination-aware, termed as PIAFusion, which adaptively maintains the intensity distribution of salient targets and preserves texture information in the background. Specifically, we design an illumination-aware sub-network to estimate the illumination distribution and calculate the illumination probability. Moreover, we utilize the illumination probability to construct an illumination-aware loss to guide the training of the fusion network. The cross-modality differential aware fusion module and halfway fusion strategy completely integrate common and complementary information under the constraint of illumination-aware loss. In addition, a new benchmark dataset for infrared and visible image fusion, i.e., Multi-Spectral Road Scenarios (available at https://github.com/Linfeng-Tang/MSRS), is released to support network training and comprehensive evaluation. Extensive experiments demonstrate the superiority of our method over state-of-the-art alternatives in terms of target maintenance and texture preservation. Particularly, our progressive fusion framework could round-the-clock integrate meaningful information from source images according to illumination conditions. Furthermore, the application to semantic segmentation demonstrates the potential of our PIAFusion for high-level vision tasks. Our codes will be available at https://github.com/Linfeng-Tang/PIAFusion.     

Evaluation and modelling across-track illumination variation in hyperion images using quadratic regression

The across-track illumination variation in Earth Observing-1 (EO-1) Hyperion images is a result of wavelength-shift and full-width-at-half-maximum (FWHM)-shift in the cross-track direction. Correction in across-track illumination variation is necessary for accurate spectral matching and classification. This contribution reviews the available methods for the correction of across-track illumination variation, and evaluates them for correcting a Hyperion image of study area around the Udaipur city in western India. We also describe and demonstrated a new technique for correcting these artefacts. For each band, the spatial trends of (a) nonlinear shifts in the nominal centre wavelengths of bands across the image columns and (b) nonlinear changes in the nominal FWHM of bands across the image columns are modelled using quadratic regression and are compensated using a radiance correction factor estimated from the columns characterized by minimum illumination variations in a spectrally flat area of the image. A series of statistical measures, spectral matching, minimum noise fraction transform (MNF) images, and post-correction classification results were used to evaluate the performance of the proposed algorithm vis-à-vis some of the previous methods on the Hyperion image of the study area. The results indicate that the proposed method effectively corrects the across-track illumination effects in the Hyperion image of the study area, and also show better performance in lithological as well as for land-use and land-cover mapping, as compared to the other previous methods.     

Homomorphic filtering based illumination normalization method for face recognition

The appearance of a face image is severely affected by illumination conditions that will hinder the automatic face recognition process. To recognize faces under varying lighting conditions, a homomorphic filtering-based illumination normalization method is proposed in this paper. In this work, the effect of illumination is effectively reduced by a modified implementation of homomorphic filtering whose key component is a Difference of Gaussian (DoG) filter, and the contrast is enhanced by histogram equalization. The resulted face image is not only reduced illumination effect but also preserved edges and details that will facilitate the further face recognition task. Among others, our method has the following advantages: (1) neither does it need any prior information of 3D shape or light sources, nor many training samples thus can be directly applied to single training image per person condition; and (2) it is simple and computationally fast because there are mature and fast algorithms for the Fourier transform used in homomorphic filter. The Eigenfaces method is chosen to recognize the normalized face images. Experimental results on the Yale face database B and the CMU PIE face database demonstrate the significant performance improvement of the proposed method in the face recognition system for the face images with large illumination variations.     

Face recognition across illumination

Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

自适应同态对数光照补偿

人脸检测有广泛的应用前景,但目前的许多人脸检测算法对光照有一定的要求,过亮过暗偏光图像使检测率急剧下降。提出一种自适应同态对数光照补偿方法,用对数函数作为基函数,使变换后的图像符合人的视觉特点;设置对数函数的平移系数,使变换对各种程度过暗过亮偏光阴影图像都可以进行有效的补偿;引入同态补偿公式来补偿光照的同时增强图像细节,使变换后的图像更清晰。实验结果表明,该方法对过暗过亮的人脸图像都可以进行有效的光照补偿,有助于提高人脸检测率。     

Image plane sweep volume illumination

In recent years, many volumetric illumination models have been proposed, which have the potential to simulate advanced lighting effects and thus support improved image comprehension. Although volume ray-casting is widely accepted as the volume rendering technique which achieves the highest image quality, so far no volumetric illumination algorithm has been designed to be directly incorporated into the ray-casting process. In this paper we propose image plane sweep volume illumination (IPSVI), which allows the integration of advanced illumination effects into a GPU-based volume ray-caster by exploiting the plane sweep paradigm. Thus, we are able to reduce the problem complexity and achieve interactive frame rates, while supporting scattering as well as shadowing. Since all illumination computations are performed directly within a single rendering pass, IPSVI does not require any preprocessing nor does it need to store intermediate results within an illumination volume. It therefore has a significantly lower memory footprint than other techniques. This makes IPSVI directly applicable to large data sets. Furthermore, the integration into a GPU-based ray-caster allows for high image quality as well as improved rendering performance by exploiting early ray termination. This paper discusses the theory behind IPSVI, describes its implementation, demonstrates its visual results and provides performance measurements.     

Display illumination with modulated directional backlight

Illumination is essential for modern life as colorful world is perceived by human visionary system. Display technology has been developing rapidly in recent decades, and the basic principle is related to the way that the image is illuminated and light is emanated. Traditional illumination is provided by different types of light sources, and the display image is visible in large viewing space until the emanating light decays to zero. This work proposes and demonstrates a novel illumination scheme for a display in which the displaying images are visible only in specific spatial regions. The directional backlight ensures the image propagating to specific direction while imaging visibility can be controlled to terminate abruptly at certain distance from the display screen while exerting no influence to nearby regions. The working principle for such an illumination scheme is the use of the modulated coherent directional backlight through an axicon lens. It is shown that the illumination scheme can robustly deliver carried image information to the designated viewing region. This new illumination scheme has many advantages over conventional illumination, including its usage for personal display, very lower energy consumption, as well as minimizing light hazard pollution.     

基于光照分类的可变光照下人脸识别方法

针对人脸识别中的光照变化问题,借鉴“分而治之”的思想,提出通过光照分类来提高不同光照情况下人脸的识别率。根据人脸图像灰度随光照变化的分布特点,将图像划分为三类：无偏光类、左偏光类和右偏光类,分别在不同的光照子集中对人脸图像进行处理与识别,并在YALEB人脸库上完成实验验证。结果表明,该方法不需要进行光照归一化处理,有效减弱了光照不均匀对人脸识别的影响,在提高识别率的同时降低了运算量,识别率可从未分类前的86.7%提高到99.6%,对于可变光照下的人脸识别有一定的应用前景。     

Video repairing under variable illumination using cyclic motions

This paper presents a complete system capable of synthesizing a large number of pixels that are missing due to occlusion or damage in an uncalibrated input video. These missing pixels may correspond to the static background or cyclic motions of the captured scene. Our system employs user-assisted video layer segmentation, while the main processing in video repair is fully automatic. The input video is first decomposed into the color and illumination videos. The necessary temporal consistency is maintained by tensor voting in the spatio-temporal domain. Missing colors and illumination of the background are synthesized by applying image repairing. Finally, the occluded motions are inferred by spatio-temporal alignment of collected samples at multiple scales. We experimented on our system with some difficult examples with variable illumination, where the capturing camera can be stationary or in motion.     

Rotation invariant features for color texture classification and retrieval under varying illumination

This article proposes a new quaternion-based method for rotation invariant color texture classification under illumination variance with respect to direction and spectral band. The color of an object varies according to the spectral power distribution, object-illumination, and viewing geometry of the light source. The quaternion representation of color is shown to be effective, which treats color channels as single unit rather than separate components. New texture signatures are extracted by calculating the norm of the Quaternion fourier spectrum. These signatures are proved to be invariant under image rotation and illumination rotation. Moreover, these features are also invariant to the color spaces. The robustness of different color spaces against varying illumination in color Texture classification with 45 samples of 15 outex texture classes are examined. Comparative results show that the proposed method is efficient in rotation invariant texture classification.