Separating an image of a scene illuminated by a light source into direct components such as specular reflection and diffuse reflection, and global components such as interreflection and subsurface scattering is important as preprocessing for various computer vision and graphics applications. Conventional methods cannot separate direct and global components from novel viewpoints, and have difficulties in robustly separating those components from a small number of images even from known viewpoints. In this paper, we propose a method for synthesizing the direct and global components of a scene from novel viewpoints by using a relatively small number of images. Specifically, our proposed method uses the multi-view images captured by using a coaxial projector-camera system, and then recovers the density and radiance values of each component on the basis of neural radiance fields (NeRF). We conduct a number of experiments using real images captured with a projector-camera system, and confirm the effectiveness of our method. In addition, we demonstrate that our method is useful for two applications: image-based material editing and 3D shape recovery.

Defocus is a crucial cue for image-based microscale depth estimation, yet its measurement depends on spatial appearance changes, such as texture. We show that passively observed polarization is responsive to small irregularities of the surface visible in the microscopic world and can be leveraged for focus measure as a strong texture. Our key idea is to leverage texture from polarization for blur analysis and accurately estimate the focus level of microscopic polarization images. We further utilize normal cues from polarization to create a prior distribution of the focus level between neighboring pixels. We then interpolatively propagate the focus level of discrete image slices at different focus depths while denoising. We implement our method with a single polarization camera with a microscope and recover the per-pixel depth from the multi-focus images. The reconstructed results demonstrate the effectiveness of our method for various microscale objects regardless of the surface texture.

Synthesizing photo-realistic images of a scene from arbitrary viewpoints and under arbitrary lighting environments is one of the important research topics in computer vision and graphics. In this paper, we propose a method for synthesizing photo-realistic images of a scene with fluorescent objects from novel viewpoints and under novel lighting colors. In general, fluorescent materials absorb light with certain wavelengths and then emit light with longer wavelengths than the absorbed ones, in contrast to reflective materials which preserve wavelengths of light. Therefore, we cannot reproduce the colors of fluorescent objects under arbitrary lighting colors by combining conventional view synthesis techniques with the white balance adjustment of the RGB channels. Accordingly, we extend the novel view synthesis based on the neural radiance fields by incorporating the superposition principle of light; our proposed method captures a sparse set of images of a scene from varying viewpoints and under varying light source colors by using a display-camera system, and then synthesize photo-realistic images of the scene without explicitly modeling the geometric and photometric models of the scene. We conduct a number of experiments using real images, and confirm the effectiveness of our method.

Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, we show that the flickers due to AC illumination is useful for intrinsic image decomposition (IID). Our proposed method conducts the light source separation (LSS) followed by the IID under AC illumination. In particular, we reveal the ambiguity in the blind LSS via matrix factorization and the ambiguity in the IID assuming the diffuse reflection model, and then show why and how those ambiguities can be resolved via a physics-based approach. We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing.

Separating diffuse and specular reflection components observed on an object surface is important for preprocessing of various computer vision techniques. Conventionally, diffuse-specular separation based on the polarimetric and color clues assumes that the diffuse/specular reflection components are unpolarized/partially polarized under unpolarized light sources. However, the diffuse reflection component is partially polarized in fact, because the diffuse reflectance is maximal when the polarization direction is parallel to the outgoing plane. Accordingly, we propose a method for separating partially-polarized diffuse and specular reflection components on the basis of the polarization reflection model and the dichromatic reflection model. In particular, our method enables us not only to achieve diffuse-specular separation but also to estimate the polarimetric properties of the object surface from a single color polarization image. We experimentally confirmed that our method performs better than the method assuming unpolarized diffuse reflection components.

Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id). Although a covariance descriptor has been successfully applied to person re-id, it loses the local structure of a region and mean information of pixel features, both of which tend to be the major discriminative information for person re-id. In this paper, we present novel meta-descriptors based on a hierarchical Gaussian distribution of pixel features, in which both mean and covariance information are included in patch and region level descriptions. More specifically, the region is modeled as a set of multiple Gaussian distributions, each of which represents the appearance of a local patch. The characteristics of the set of Gaussian distributions are again described by another Gaussian distribution. Because the space of Gaussian distribution is not a linear space, we embed the parameters of the distribution into a point of Symmetric Positive Definite (SPD) matrix manifold in both steps. We show, for the first time, that normalizing the scale of the SPD matrix enhances the hierarchical feature representation on this manifold. Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the SPD matrix descriptors. The experimental results conducted on five public datasets indicate the effectiveness of the proposed descriptors and the two types of normalizations.

In this paper, we address the separation of reflective and fluorescent components in RGB images taken under narrow-band light sources such as LEDs. First, we show that the fluorescent color per pixel can be estimated from at least two images under different light source colors, because the observed color at a surface point is represented by a convex combination of the light source color and the illumination-invariant fluorescent color. Second, we propose a method for robustly estimating the fluorescent color via MAP estimation by taking the prior knowledge with respect to fluorescent colors into consideration. We conducted a number of experiments by using both synthetic and real images, and confirmed that our proposed method works better than the closely related state-of-the-art method and enables us to separate reflective and fluorescent components even from a single image. Furthermore, we demonstrate that our method is effective for applications such as image-based material editing and relighting.

In this paper, we propose a method for separating direct and global components of a dynamic scene per illumination color by using a projector-camera system; it exploits both the color switch and the temporal dithering of a DLP projector. Our proposed method is easy-to-implement because it does not require any self-built equipment and temporal synchronization between a projector and a camera. In addition, our method automatically calibrates the projector-camera correspondence in a dynamic scene on the basis of the consistency in pixel intensities, and optimizes the projection pattern on the basis of noise propagation analysis. We implemented the prototype setup and achieved multispectral direct-global separation of dynamic scenes in 60 Hz. Furthermore, we demonstrated that our method is effective for applications such as image-based material editing and multispectral relighting of dynamic scenes where wavelength-dependent phenomena such as fluorescence are observed.

Separating diffuse and specular reflection components is important for preprocessing of various computer vision techniques such as photometric stereo. In this paper, we address diffuse-specular separation for photometric stereo based on light fields. Specifically, we reveal the low-rank structure of the multi-view images under varying light source directions, and then formulate the diffuse-specular separation as a low-rank approximation of the 3rd order tensor. Through a number of experiments using real images, we show that our proposed method, which integrates the complement clues based on varying light source directions and varying viewing directions, works better than existing techniques.

Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification. In this paper, we present a novel descriptor based on a hierarchical distribution of pixel features. A hierarchical covariance descriptor has been successfully applied for image classification. However, the mean information of pixel features, which is absent in covariance, tends to be major discriminative information of person images. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, we model the region as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, unlike the hierarchical covariance descriptor, the proposed descriptor can model both the mean and the covariance information of pixel features properly. The results of experiments conducted on five databases indicate that the proposed descriptor exhibits remarkably high performance which outperforms the state-of-the-art descriptors for person re-identification.

The appearance of an object depends on the color as well as the direction of a light source illuminating the object. The progress of LEDs enables us to capture the images of an object under multispectral and multidirectional light sources. Separating diffuse and specular reflection components in those images is important for preprocessing of various computer vision techniques such as photometric stereo, material editing, and relighting. In this paper, we propose a robust method for separating reflection components in a set of images of an object taken under multispectral and multidirectional light sources. We consider the set of images as the 3D data whose axes are the pixel, the light source color, and the light source direction, and then show the inherent structures of the 3D data: the rank 2 structure derived from the dichromatic reflection model, the rank 3 structure derived from the Lambert model, and the sparseness of specular reflection components. Based on those structures, our proposed method separates diffuse and specular reflection components by combining sparse NMF and SVD with missing data. We conducted a number of experiments by using both synthetic and real images, and show that our method works better than some of the state-of-the-art techniques.

Hyperspectral imaging is beneficial to many applications but most traditional methods do not consider fluorescent effects which are present in everyday items ranging from paper to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflection and fluorescence so proper separation of these components is necessary for analyzing them. In recent years, effective imaging methods have been proposed but most require capturing the scene under multiple illuminants. In this paper, we demonstrate efficient separation and recovery of reflectance and fluorescence emission spectra through the use of two high frequency illuminations in the spectral domain. With the obtained fluorescence emission spectra from our high frequency illuminants, we then describe how to estimate the fluorescence absorption spectrum of a material given its emission spectrum. In addition, we provide an in depth analysis of our method and also show that filters can be used in conjunction with standard light sources to generate the required high frequency illuminants. We also test our method under ambient light and demonstrate an application of our method to synthetic relighting of real scenes.

In recent years, fluorescence analysis of scenes has received attention in computer vision. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescent emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescent emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel under unknown varying directional illumination. We show that for general isotropic materials and uniformly distributed light directions, the geodesic distance between intensity profiles is linearly related to the angular difference of their corresponding surface normals, and that the intensity distribution of the intensity profile reveals reflectance properties. Based on these observations, we develop two methods for surface normal estimation; one for a general setting that uses only the recorded intensity profiles, the other for the case where a BRDF database is available while the exact BRDF of the target scene is still unknown. Quantitative and qualitative evaluations are conducted using both synthetic and real-world scenes, which show the state-of-the-art accuracy of smaller than 10 degree without using reference data and 5 degree with reference data for all 100 materials in MERL database.

In this paper, we address the problem of free head motion in appearance-based gaze estimation. This problem remains challenging because head motion changes eye appearance significantly, and thus, training images captured for an original head pose cannot handle test images captured for other head poses. To overcome this difficulty, we propose a novel gaze estimation method that handles free head motion via eye image synthesis based on a single camera. Compared with conventional fixed head pose methods with original training images, our method only captures four additional eye images under four reference head poses, and then, precisely synthesizes new training images for other unseen head poses in estimation. To this end, we propose a single-directional (SD) flow model to efficiently handle eye image variations due to head motion. We show how to estimate SD flows for reference head poses first, and then use them to produce new SD flows for training image synthesis. Finally, with synthetic training images, joint optimization is applied that simultaneously solves an eye image alignment and a gaze estimation. Evaluation of the method was conducted through experiments to assess its performance and demonstrate its effectiveness.

In recent years, fluorescence analysis of scenes has received attention. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescence emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescence emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

Metric learning to learn a good distance metric for distinguishing different people while being insensitive to intra-person variations is widely applied to person re-identification. In previous works, local histograms are densely sampled to extract spatially localized information of each person image. The extracted local histograms are then concatenated into one vector that is used as an input of metric learning. However, the dimensionality of such a concatenated vector often becomes large while the number of training samples is limited. This leads to an over fitting problem. In this work, we argue that such a problem of over-fitting comes from that it is each local histogram dimension (e.g. color brightness bin) in the same position is treated separately to examine which part of the image is more discriminative. To solve this problem, we propose a method that analyzes discriminative image positions shared by different local histogram dimensions. A common weight map shared by different dimensions and a distance metric which emphasizes discriminative dimensions in the local histogram are jointly learned with a unified discriminative criterion. Our experiments using four different public datasets confirmed the effectiveness of the proposed method.

We investigate the appearance-based gaze estimation problem, with respect to its essential difficulty in reducing the number of required training samples, and other practical issues such as slight head motion, image resolution variation, and eye blinking. We cast the problem as mapping high-dimensional eye image features to low-dimensional gaze positions, and propose an adaptive linear regression (ALR) method as the key to our solution. The ALR method adaptively selects an optimal set of sparsest training samples for the gaze estimation via ℓ(1)-optimization. In this sense, the number of required training samples is significantly reduced for high accuracy estimation. In addition, by adopting the basic ALR objective function, we integrate the gaze estimation, subpixel alignment and blink detection into a unified optimization framework. By solving these problems simultaneously, we successfully handle slight head motion, image resolution variation and eye blinking in appearance-based gaze estimation. We evaluated the proposed method by conducting experiments with multiple users and variant conditions to verify its effectiveness.

Antony Lam1 http://research.nii.ac.jp/~antony Art Subpa-Asa2 [email protected] Imari Sato1 http://research.nii.ac.jp/~imarik Takahiro Okabe3 http://www.pluto.ai.kyutech.ac.jp/~okabe Yoichi Sato4 http://www.hci.iis.u-tokyo.ac.jp/~ysato 1 National Institute of Informatics Tokyo, Japan 2 The Stock Exchange of Thailand Bangkok, Thailand 3 Kyushu Institute of Technology Fukuoka, Japan 4 The University of Tokyo Tokyo, Japan

We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel across multi-illuminance images. We show that for general isotropic materials, the geodesic distance between intensity profiles is linearly related to the angular difference of their surface normals, and that the intensity distribution of an intensity profile conveys information about the reflectance properties, when the intensity profile is obtained under uniformly distributed directional lightings. Based on these observations, we show that surface normals can be estimated up to a convex/concave ambiguity. A solution method based on matrix decomposition with missing data is developed for a reliable estimation. Quantitative and qualitative evaluations of our method are performed using both synthetic and real-world scenes.

Hyper spectral imaging is beneficial to many applications but current methods do not consider fluorescent effects which are present in everyday items ranging from paper, to clothing, to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflectance and fluorescence. So proper separation of these components is necessary for analyzing them. In this paper, we demonstrate efficient separation and recovery of reflective and fluorescent emission spectra through the use of high frequency illumination in the spectral domain. With the obtained fluorescent emission spectra from our high frequency illuminants, we then present to our knowledge, the first method for estimating the fluorescent absorption spectrum of a material given its emission spectrum. Conventional bispectral measurement of absorption and emission spectra needs to examine all combinations of incident and observed light wavelengths. In contrast, our method requires only two hyper spectral images. The effectiveness of our proposed methods are then evaluated through a combination of simulation and real experiments. We also demonstrate an application of our method to synthetic relighting of real scenes.

Camera spectral sensitivity plays an important role for various color-based computer vision tasks. Although several methods have been proposed to estimate it, their applicability is severely restricted by the requirement for a known illumination spectrum. In this work, we present a single-image estimation method using fluorescence with no requirement for a known illumination spectrum. Under different illuminations, the spectral distributions of fluorescence emitted from the same material remain unchanged up to a certain scale. Thus, a camera's response to the fluorescence would have the same chromaticity. Making use of this chromaticity invariance, the camera spectral sensitivity can be estimated under an arbitrary illumination whose spectrum is unknown. Through extensive experiments, we proved that our method is accurate under different illuminations. Moreover, we show how to recover the spectra of daylight from the estimated results. Finally, we use the estimated camera spectral sensitivities and daylight spectra to solve color correction problems.

We propose a novel technique called bispectral photometric stereo that makes effective use of fluorescence for shape reconstruction. Fluorescence is a common phenomenon occurring in many objects from natural gems and corals, to fluorescent dyes used in clothing. One of the important characteristics of fluorescence is its wavelength-shifting behavior: fluorescent materials absorb light at a certain wavelength and then reemit it at longer wavelengths. Due to the complexity of its emission process, fluorescence tends to be excluded from most algorithms in computer vision and image processing. In this paper, we show that there is a strong similarity between fluorescence and ideal diffuse reflection and that fluorescence can provide distinct clues on how to estimate an object's shape. Moreover, fluorescence's wavelength-shifting property enables us to estimate the shape of an object by applying photometric stereo to emission-only images without suffering from specular reflection. This is the significant advantage of the fluorescence-based method over previous methods based on reflection.

Characteristics of the human visual field are well known to be different in central (fovea) and peripheral areas. Existing computational models of visual saliency, however, do not take into account this biological evidence. The existing models compute visual saliency uniformly over the retina and, thus, have difficulty in accurately predicting the next gaze (fixation) point. This paper proposes to incorporate human visual field characteristics into visual saliency, and presents a computational model for producing such a saliency map. Our model integrates image features obtained by bottom-up computation in such a way that weights for the integration depend on the distance from the current gaze point where the weights are optimally learned using actual saccade data. The experimental results using a large number of fixation/saccade data with wide viewing angles demonstrate the advantage of our saliency map, showing that it can accurately predict the point where one looks next.

To infer human gaze from eye appearance, various methods have been proposed. However, most of them assume a fixed head pose because allowing free head motion adds 6 degrees of freedom to the problem and requires a prohibitively large number of training samples. In this paper, we aim at solving the appearance-based gaze estimation problem under free head motion without significantly increasing the cost of training. The idea is to decompose the problem into subproblems, including initial estimation under fixed head pose and subsequent compensations for estimation biases caused by head rotation and eye appearance distortion. Then each subproblem is solved by either learning-based method or geometric-based calculation. Specifically, the gaze estimation bias caused by eye appearance distortion is learnt effectively from a 5-seconds video clip. Extensive experiments were conducted to verify the effectiveness of the proposed approach. 1

Portable high-quality sports cameras (e.g. head or helmet mounted) built for recording dynamic first-person video footage are becoming a common item among many sports enthusiasts. We address the novel task of discovering first-person action categories (which we call ego-actions) which can be useful for such tasks as video indexing and retrieval. In order to learn ego-action categories, we investigate the use of motion-based histograms and unsupervised learning algorithms to quickly cluster video content. Our approach assumes a completely unsupervised scenario, where labeled training videos are not available, videos are not pre-segmented and the number of ego-action categories are unknown. In our proposed framework we show that a stacked Dirichlet process mixture model can be used to automatically learn a motion histogram codebook and the set of ego-action categories. We quantitatively evaluate our approach on both in-house and public YouTube videos and demonstrate robust ego-action categorization across several sports genres. Comparative analysis shows that our approach outperforms other state-of-the-art topic models with respect to both classification accuracy and computational speed. Preliminary results indicate that on average, the categorical content of a 10 minute video sequence can be indexed in under 5 seconds.

Aesthetic quality classification plays an important role in how people organize large photo collections. In particular, color harmony is a key factor in the various aspects that determine the perceived quality of a photo, and it should be taken into account to improve the performance of automatic aesthetic quality classification. However, the existing models of color harmony take only simple color patterns into consideration-e.g., patches consisting of a few colors-and thus cannot be used to assess photos with complicated color arrangements. In this work, we tackle the challenging problem of evaluating the color harmony of photos with a particular focus on aesthetic quality classification. A key point is that a photograph can be seen as a collection of local regions with color variations that are relatively simple. This led us to develop a method for assessing the aesthetic quality of a photo based on the photo's color harmony. We term the method `bags-of-color-patterns.' Results of experiments on a large photo collection with user-provided aesthetic quality scores show that our aesthetic quality classification method, which explicitly takes into account the color harmony of a photo, outperforms the existing methods. Results also show that the classification performance is improved by combining our color harmony feature with blur, edges, and saliency features that reflect the aesthetics of the photos.

The problem of estimating human gaze from eye appearance is regarded as mapping high-dimensional features to low-dimensional target space. Conventional methods require densely obtained training samples on the eye appearance manifold, which results in a tedious calibration stage. In this paper, we introduce an adaptive linear regression (ALR) method for accurate mapping via sparsely collected training samples. The key idea is to adaptively find the subset of training samples where the test sample is most linearly representable. We solve the problem via l1-optimization and thoroughly study the key issues to seek for the best solution for regression. The proposed gaze estimation approach based on ALR is naturally sparse and low-dimensional, giving the ability to infer human gaze from variant resolution eye images using much fewer training samples than existing methods. Especially, the optimization procedure in ALR is extended to solve the subpixel alignment problem simultaneously for low resolution test eye images. Performance of the proposed method is evaluated by extensive experiments against various factors such as number of training samples, feature dimensionality and eye image resolution to verify its effectiveness.

Recently developed video editing techniques have enabled us to create realistic synthesized videos. Therefore, using video data as evidence in places such as courts of law requires a method to detect forged videos. In this study, we developed an approach to detect suspicious regions in a video of a static scene on the basis of the noise characteristics. The image signal contains irradiance-dependent noise the variance of which is described by a noise level function (NLF) as a function of irradiance. We introduce a probabilistic model providing the inference of an NLF that controls the characteristics of the noise at each pixel. Forged pixels in the regions clipped from another video camera can be differentiated by using maximum a posteriori estimation for the noise model when the NLFs of the regions are inconsistent with the rest of the video. We demonstrate the effectiveness of our proposed method by adapting it to videos recorded indoors and outdoors. The proposed method enables us to highly accurately evaluate the per-pixel authenticity of the given video, which achieves denser estimation than prior work based on block-level validation. In addition, the proposed method can be applied to various kinds of videos such as those contaminated by large noise and recorded with any scan formats, which limits the applicability of the existing methods.

We present a novel technique, termed attached shadow coding, for estimating surface normals from shadows when the reflectance and lighting conditions are unknown. Our key idea is encoding surface points via attached shadows observed under different light source directions and then estimating surface normals on the basis of the similarity of the attached shadow codes. Because shadows do not rely on reflectance properties, our method is applicable to surfaces with various complex reflectances such as anisotropic and composite materials. Moreover, our method is robust against noise because it takes advantage of the combination of weak constraints imposed by a number of light sources. We theoretically show that the distance between the codes at two surface points is equal to the angle between the corresponding surface normals under the assumption of uniform lighting and a convex object. Our method embeds high-dimensional codes into a 3D surface normal space so that the inter-code distances are preserved. Furthermore, we extend the method in order to alleviate the effects of nonuniform lighting and cast shadows. Experimental results demonstrate the effectiveness of our method.

In this work, we propose a method for tracking individuals in crowds. Our method is based on a trajectory-based clustering approach that groups trajectories of image features that belong to the same person. The key novelty of our method is to make use of a person's individuality, that is, the gait features and the temporal consistency of local appearance to track each individual in a crowd. Gait features in the frequency domain have been shown to be an effective biometric cue in discriminating between individuals, and our method uses such features for tracking people in crowds for the first time. Unlike existing trajectory-based tracking methods, our method evaluates the dissimilarity of trajectories with respect to a group of three adjacent trajectories. In this way, we incorporate the temporal consistency of local patch appearance to differentiate trajectories of multiple people moving in close proximity. Our experiments show that the use of gait features and the temporal consistency of local appearance contributes to significant performance improvement in tracking people in crowded scenes.

This paper proposes a novel method for automatically cropping a photo using a quality classifier that assesses whether the cropped region is agreeable to users. We statistically build this quality classifier using large photo collections available on websites where people manually insert quality scores to photos. We first trim the original image and then decide on the candidates for cropping. We find the cropped region with the highest quality score by applying the quality classifier to the candidates. Current automatic photo cropping techniques search for attention grabbing regions that consist of salient pixels from the original photo. They are not always pleasant to users because they do not take into account the quality of the cropped region. Our method with the quality classifier outperforms a state-of-the-art method that takes into consideration only the user's attention for automatic photo cropping.

This paper presents a technique for determining an object's shape based on the similarity of radiance changes observed at points on its surface under varying illumination. To examine the similarity, we use an observation vector that represents a sequence of pixel intensities of a point on the surface under different lighting conditions. Assuming convex objects under distant illumination and orthographic projection, we show that the similarity between two observation vectors is closely related to the similarity between the surface normals of the corresponding points. This enables us to estimate the object's surface normals solely from the similarity of radiance changes under unknown distant lighting by using dimensionality reduction. Unlike most previous shape reconstruction methods, our technique neither assumes particular reflection models nor requires reference materials. This makes our method applicable to a wide variety of objects made of different materials.

In this study, we demonstrate the effectiveness of using extended light sources for modeling the appearance of an object for varying illumination. Extended light sources have a radiance distribution that is similar to that of the Gaussian function and have the potential of functioning as a low-pass filter when the appearance of an object is sampled under them. This enables us to obtain a set of basis images of an object for variable illumination from input images of the object taken under those light sources without suffering aliasing caused by insufficient sampling of its appearance. Furthermore, extended light sources are useful in terms of reducing high contrast in image intensities due to specular and diffuse reflection components. This helps us observe both specular and diffuse reflection components of an object in the same image taken with a single shutter speed. We have tested our proposed approach based on extended light sources with objects of complex appearance that are generally difficult to model using image-based modeling techniques.

For object recognition under varying illumination conditions, we propose a method based on photometric alignment. The photometric alignment is known as a technique that models both diffuse reflection components and attached shadows under a distant point light source by using three basis images. However, in order to reliably reproduce these components in a test image, we have to take into account outliers such as specular reflection components and shadows in the test image. Accordingly, our proposed method utilizes Random Sample Consensus (RANSAC), which has been used successfully for estimating basis images. In the present study, we have conducted experiments using the Yale Face Database B and confirmed that a combination of the photometric alignment and RANSAC provides a simple but effective method for object recognition under varying illumination conditions.

Previous studies have demonstrated that the appearance of an object under varying illumination conditions can be represented by a low-dimensional linear subspace. A set of basis images spanning such a linear subspace can be obtained by applying the principal component analysis (PCA) for a large number of images taken under different lighting conditions. While the approaches based on PCA have been used successfully for object recognition under varying illumination conditions, little is known about how many images would be required in order to obtain the basis images correctly. In this study, we present a novel method for analytically obtaining a set of basis images of an object for arbitrary illumination from input images of the object taken under a point light source. The main contribution of our work is that we show that a set of lighting directions can be determined for sampling images of an object depending on the spectrum of the object's BRDF in the angular frequency domain such that a set of harmonic images can be obtained analytically based on the sampling theorem on spherical harmonics. In addition, unlike the previously proposed techniques based on spherical harmonics, our method does not require the 3D shape and reflectance properties of an object used for rendering harmonics images of the object synthetically.