Abstract-Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies onedimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method.
Barcode reading mobile applications to identify products from pictures acquired by mobile devices are widely used by customers from all over the world to perform online price comparisons or to access reviews written by other customers. Most of the currently available 1D barcode reading applications focus on effectively decoding barcodes and treat the underlying detection task as a side problem that needs to be solved using general purpose object detection methods. However, the majority of mobile devices do not meet the minimum working requirements of those complex general purpose object detection algorithms and most of the efficient specifically designed 1D barcode detection algorithms require user interaction to work properly. In this work, we present a novel method for 1D barcode detection in camera captured images, based on a supervised machine learning algorithm that identifies the characteristic visual patterns of 1D barcodes' parallel bars in the two-dimensional Hough Transform space of the processed images. The method we propose is angle invariant, requires no user interaction and can be effectively executed on a mobile device; it achieves excellent results for two standard 1D barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the performance of a state-of-the-art 1D barcode reading library by coupling it with our detection method.
Abstract. This work proposes a new error backpropagation approach as a systematic way to configure and train the Multi-net System MNOD, a recently proposed algorithm able to segment a class of visual objects from real images. First, a single node of the MNOD is configured in order to best resolve the visual object segmentation problem using the best combination of parameters and features. The problem is then how to add new nodes in order to improve accuracy and avoid overfitting situations. In this scenario, the proposed approach employs backpropagation of error maps to add new nodes with the aim of increasing the overall segmentation performance. Experiments conducted on a standard dataset of real images show that our configuration method, using only simple edges and colors descriptors, leads to configurations that produced comparable results in visual objects segmentation.
Abstract:In this study we propose a new strategy to perform an object segmentation using a multi neural network approach. We started extending our previously presented object detection method applying a new segment based classification strategy. The result obtained is a segmentation map post processed by a phase that exploits the GrabCut algorithm to obtain a fairly precise and sharp edges of the object of interest in a full automatic way. We tested the new strategy on a clothing commercial dataset obtaining a substantial improvement on the quality of the segmentation results compared with our previous method. The segment classification approach we propose achieves the same improvement on a subset of the Pascal VOC 2011 dataset which is a recent standard segmentation dataset, obtaining a result which is inline with the state of the art.
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