Fast Quadratic Shape From Shading With Unknown Light Direction

An algorithm is proposed that extracts 3D shape from shading information in a digital image. The algorithm assumes that there is only a single source of light producing the image, that the surface of the shape giving rise to the image is Lambertian (matte) and that its shape can be locally approximated by a quadratic function. Previous work shows that under these assumptions, robust shape from shading is possible, though slow for large images because a non-linear optimization method is applied in order to estimate local quadratic surface patches from image intensities. The work presented here shows that local quadratic surface patch estimates can be computed, without prior knowledge of the light source direction, via a linear least squares optimization, thus greatly improving the algebraic complexity and run-time of the existing algorithms.

Fast Quadratic Shape From Shading With Unknown Light Direction

An algorithm is proposed that extracts 3D shape from shading information in a digital image. The algorithm assumes that there is only a single source of light producing the image, that the surface of the shape giving rise to the image is Lambertian (matte) and that its shape can be locally approximated by a quadratic function. Previous work shows that under these assumptions, robust shape from shading is possible, though slow for large images because a non-linear optimization method is applied in order to estimate local quadratic surface patches from image intensities. The work presented here shows that local quadratic surface patch estimates can be computed, without prior knowledge of the light source direction, via a linear least squares optimization, thus greatly improving the algebraic complexity and run-time of this existing algorithms.

Fast Quadratic Shape From Shading With Unknown Light Direction

An algorithm is proposed that extracts 3D shape from shading information in a digital image. The algorithm assumes that there is only a single source of light producing the image, that the surface of the shape giving rise to the image is Lambertian (matte) and that its shape can be locally approximated by a quadratic function. Previous work shows that under these assumptions, robust shape from shading is possible, though slow for large images because a non-linear optimization method is applied in order to estimate local quadratic surface patches from image intensities. The work presented here shows that local quadratic surface patch estimates can be computed, without prior knowledge of the light source direction, via a linear least squares optimization, thus greatly improving the algebraic complexity and run-time of this existing algorithms.

Deep Light Direction Reconstruction from single RGB images

In augmented reality applications, consistent illumination between virtual and real objects is important for creating an immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of the virtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed a method to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using a modified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. To avoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introduced stereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the light source direction, our approach does not require depth information and thus does not rely on special red-green-blue- depth (RGB-D) images as input.

Image Forensics Based on Lighting Estimation

Computer generated images are assumed to be a key part in each person’s life in this era of information technology, where individuals effectively inhabit the advertisements, magazines, websites, televisions and many more. At the point when digital images played their role, the event of violations in terms of misrepresentation of information, use of their wrong doings winds up and also becomes easier with the help of image editing application programs. To be legitimate, if anyone does wrong anything then the proposed method can be used for a correct identification of the forgery and the imitations in the digital images. In existing techniques, researchers have suggested most well-known types of digital photographic manipulations based on source, meta-data, image copying, splicing and many more. The proposed approach is inspired by physics-based techniques and requires less human involvement. The presented approach works for images having any type of objects present in the scene, i.e. not only limited to human faces and selection of same intensity regions of the image. By assessing the lighting parameters, the proposed technique identifies the manipulated object and returns angle of incidence w.r.t light source direction. The demonstrated result produces forgery recognition rate of 92% on an image dataset comprising of various types of manipulated images.

Turbine Blade Illusion

In January 2017, a large wind turbine blade was installed temporarily in a city square as a public artwork. At first sight, media photographs of the installation appeared to be fakes – the blade looks like it could not really be part of the scene. Close inspection of the object shows that its paradoxical visual appearance can be attributed to unconscious assumptions about object shape and light source direction.

The hollow-face illusion monocularly observed in a box

Under monocular conditions, 40 students observed the reverse of polychrome and monochrome masks and judged them to be concave, convex or flat. The mask was presented upright and illuminated from above, below, right and left and in the upside down position illuminated from below. The magnitude of the perceived depth or relief was estimated using a retractable tape measure. Regardless of color, lighting and orientation, the majority of responses indicated that the hollow masks were perceived to be convex. No significant differences were observed between the depth or convexity of the metric magnitudes and scalar magnitudes of the concave masks in relation to variations in the light source direction, color, and position. The illusory depth, seeing the concave mask as convex, is a robust phenomenon that suggests the predominant role of higher-order processes over the low-order processes in visual face perception.

A New Algorithm to Exposing Image Forgeries by Detecting Ambient Illumination Consistency

Illumination consistency makes one of important roles in image authenticity, especially in splicing and copy-move rendering. This paper proposes a new method to estimate illuminant direction based on idea of binary sort tree. The algorithm calculates the least error function to structure the binary tree of illuminant direction. And it doesn’t need to expand much time to solve pathosis equation and access accuracy is improved. The experiment shows that, the algorithm can effectively calculate the light source direction of different objects and backgrounds in an image especially in infinite light source, and the detection correct rate reaches 85.4% in general situation , what’s more, 88.1% in infinite light source. Anyway, the algorithm in this paper is a great advancement of blind forensics based on consistency in lighting direction.