The use of color in QR codes brings extra data capacity, but also inflicts tremendous challenges on the decoding process due to chromatic distortion, cross-channel color interference and illumination variation. Particularly, we further discover a new type of chromatic distortion in high-density color QR codes, cross-module color interference, caused by the high density which also makes the geometric distortion correction more challenging. To address these problems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which jointly model these different types of chromatic distortion. Extended from SVM and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular objective function to learn a color classifier. Furthermore, a robust geometric transformation method and several pipeline refinements are proposed to boost the decoding performance for mobile applications. We put forth and implement a framework for high-capacity color QR codes equipped with our methods, called HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR code samples. The comparison with the baseline method [2] on CUHK-CQRC shows that HiQ at least outperforms [2] by 188 in decoding success rate and 60 in bit error rate. Our implementation of HiQ in iOS and Android also demonstrates the effectiveness of our framework in real-world applications.
H. Bagherinia and R. Manduchi @cite_18 propose to model color variation under various illuminations using a low-dimensional subspace, e.g., principal component analysis, without requiring reference color patches. T. Shimizu et. al. @cite_15 propose a 64-color 2D barcode and augment the RGB color space using seed colors which functions as references to facilitate color classification. Their method uses 15-dim or 27-dim feature both in training and testing which is prohibitively time-consuming for mobile devices in real-world applications. To decode color barcodes from blurry images, H. Bagherinia and R. Manduchi @cite_9 propose an iterative method to address the blur-induced color mixing from neighboring color patches. However, their method takes more than 7 seconds on a desktop with Intel i5-2520M CPU @ 2.50GHz to process a single image, which is completely unacceptable for mobile applications.
@cite_9 @cite_18 @cite_15
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Colorizing two–dimensional (2-D) barcodes is a promising modality for improvement on design and data capacity. The number of colors employed in recent 2-D color barcodes is limited less than 8. To increase the number of recognizable colors, we propose a color recognition method which classifies 64 colors, using an extended color space and pattern recognition techniques. The proposed method employs 4 seed colors embedded within 64-color code to prevent influences from environment illumination. In the proposed method, we extend the color representation for each code color in 2-D barcode from three components to higher number component. And a classifier is trained using the extended colors captured from several environmental lighting conditions. The classifier is selectable. In this paper, artificial neural network (ANN), support vector machine (SVM) and nearest neighbor (NN) are employed as the classifier. The experimental results shows that the proposed method achieves MSER less than 3.0 . Comparing the number of seed colors, 4 seed color is comparable to that using 8 seed color. SVM achieves the best recognition performance of 1.17 SER. These results suggest that 64-color recognition is possible by the proposed method. There is increased interest in the use of color barcodes to encode more information per area unit than regular, black-and-white barcodes. For example, Microsoft's HCCB technology uses 4 or 8 colors per patch. Unfortunately, the observed color of a surface depends as much on the illuminant spectrum (and other viewing parameters) as on the surface reflectivity, which complicates the task of decoding the content of the barcode. A popular solution is to append to the barcode a “palette” with the reference colors. In this paper, we propose a new approach to color barcode decoding, one that does not require a reference color palette. Our algorithm decodes groups of color bars at once, exploiting the fact that joint color changes can be represented by a low-dimensional space. Decoding a group of bars (a “barcode element”) is thus equivalent to searching for the nearest subspace in a dataset. We also propose algorithms to select subsets of barcode elements that can be decoded with low error probability. Our experimental results show that our barcode decoding algorithm enables substantial information rate increase with respect to system that display a color palette, at a very low decoding error rate. We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13 for SVM and 7 for convolutional nets. On a segmentation recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16 7 error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second. We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a “holistic” approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their 3D poses in the form of 2D projections of the corners of their 3D bounding boxes. This, however, is not sufficient for handling objects from the recent T-LESS dataset: These objects exhibit an axis of rotational symmetry, and the similarity of two images of such an object under two different poses makes training the CNN challenging. We solve this problem by restricting the range of poses used for training, and by introducing a classifier to identify the range of a pose at run-time before estimating it. We also use an optional additional step that refines the predicted poses. We improve the state-of-the-art on the LINEMOD dataset from 73.7 [2] to 89.3 of correctly registered RGB frames. We are also the first to report results on the Occlusion dataset [1 ] using color images only. We obtain 54 of frames passing the Pose 6D criterion on average on several sequences of the T-LESS dataset, compared to the 67 of the state-of-the-art [10] on the same sequences which uses both color and depth. The full approach is also scalable, as a single network can be trained for multiple objects simultaneously.