Recognition and matching in the presence of deformation and lighting change

Natural images of objects and scenes show a fascinating amount of variability due to different factors like lighting and viewpoint change, occlusion, articulation and non-rigid deformation. There are certain cases like recognition of specular objects and images with arbitrary deformations where existing techniques do not perform well. For image deformation, we propose a method for faster keypoint matching with histogram descriptors and a completely deformation invariant representation. We also propose a method for improving specular object recognition. Histograms are a powerful statistical representation for keypoint matching and content based image retrieval. The earth mover's distance (EMD) is an important perceptually meaningful metric for comparing histograms, but it suffers from high (O(n3 log n)) computational complexity. We propose a novel linear time algorithm for approximating EMD with the weighted L1 norm of the wavelet transform of the difference histogram. We prove that the resulting wavelet EMD metric is equivalent to EMD. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation. We also give a fast algorithm for the best partial EMD match between two histograms. Images of non-planar object can undergo a large non-linear deformation due to a viewpoint change. Complex deformations occur in images of non-rigid objects, for example, in medical image sequences. We propose using the contour tree as a novel framework invariant to arbitrary deformations for representing and comparing images. It represents all the deformation invariant information in an image. Lighting changes greatly affect the appearance of specular objects and make recognition difficult much more than for Lambertian objects. In model based recognition of specular objects, an important constraint is that the estimated lighting should be non-negative everywhere. We propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models

Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions

Lighting and display screens: Models for predicting luminance limits and disturbance

An investigation of the level of disturbance caused by reflections from a variety of display screens, including interactive whiteboards, has been carried out using three test methods: Luminance adjustment, category rating and reading. The results from the luminance adjustment test and the category rating test were consistent, both showing similar significant effects of lighting-display parameters on the disturbance caused by screen reflections. In contrast, the objective measure of task performance in the reading test was barely responsive to reflections on the screens. Two models have been developed, one to predict the luminaire luminance at which 95% of observers were not disturbed by the reflections and the other to predict the rating of disturbance caused by reflections from the screens. Both models are based on lighting-display parameters including the size and luminance of the reflected light source and the specular reflectance, the effect of haze reflection and the background luminance of the display screen. These models can be used generally, to guide lighting recommendations and, specifically, to identify suitable luminaires to be used with given set of display screens or suitable display screens to be used with a given lighting installation

AirCode: Unobtrusive Physical Tags for Digital Fabrication

We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper