MinMax Radon Barcodes for Medical Image Retrieval

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US

An Epipolar Line from a Single Pixel

Computing the epipolar geometry from feature points between cameras with very different viewpoints is often error prone, as an object's appearance can vary greatly between images. For such cases, it has been shown that using motion extracted from video can achieve much better results than using a static image. This paper extends these earlier works based on the scene dynamics. In this paper we propose a new method to compute the epipolar geometry from a video stream, by exploiting the following observation: For a pixel p in Image A, all pixels corresponding to p in Image B are on the same epipolar line. Equivalently, the image of the line going through camera A's center and p is an epipolar line in B. Therefore, when cameras A and B are synchronized, the momentary images of two objects projecting to the same pixel, p, in camera A at times t1 and t2, lie on an epipolar line in camera B. Based on this observation we achieve fast and precise computation of epipolar lines. Calibrating cameras based on our method of finding epipolar lines is much faster and more robust than previous methods.Comment: WACV 201

Universal Barcode Detector via Semantic Segmentation

Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches

Vision Based Extraction of Nutrition Information from Skewed Nutrition Labels

An important component of a healthy diet is the comprehension and retention of nutritional information and understanding of how different food items and nutritional constituents affect our bodies. In the U.S. and many other countries, nutritional information is primarily conveyed to consumers through nutrition labels (NLs) which can be found in all packaged food products. However, sometimes it becomes really challenging to utilize all this information available in these NLs even for consumers who are health conscious as they might not be familiar with nutritional terms or find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training. So it is essential to automate this data collection and interpretation process by integrating Computer Vision based algorithms to extract nutritional information from NLs because it improves the user’s ability to engage in continuous nutritional data collection and analysis. To make nutritional data collection more manageable and enjoyable for the users, we present a Proactive NUTrition Management System (PNUTS). PNUTS seeks to shift current research and clinical practices in nutrition management toward persuasion, automated nutritional information processing, and context-sensitive nutrition decision support. PNUTS consists of two modules, firstly a barcode scanning module which runs on smart phones and is capable of vision-based localization of One Dimensional (1D) Universal Product Code (UPC) and International Article Number (EAN) barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The algorithm localizes barcodes in images by computing Dominant Orientations of Gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The algorithm is implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. The algorithm was evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. The DOG algorithm was coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scan lines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass. This module is also coupled to a comprehensive NL database from which nutritional information can be retrieved on demand. Currently our NL database consists of more than 230,000 products. The second module of PNUTS is an algorithm whose objective is to determine the text skew angle of an NL image without constraining the angle’s magnitude. The horizontal, vertical, and diagonal matrices of the (Two Dimensional) 2D Haar Wavelet Transform are used to identify 2D points with significant intensity changes. The set of points is bounded with a minimum area rectangle whose rotation angle is the text’s skew. The algorithm’s performance is compared with the performance of five text skew detection algorithms on 1001 U.S. nutrition label images and 2200 single- and multi-column document images in multiple languages. To ensure the reproducibility of the reported results, the source code of the algorithm and the image data have been made publicly available. If the skew angle is estimated correctly, optical character recognition (OCR) techniques can be used to extract nutrition information

Computer vision reading on stickers and direct part marking on horticultural products : challenges and possible solutions

Traceability of products from production to the consumer has led to a technological advancement in product identification. There has been development from the use of traditional one-dimensional barcodes (EAN-13, Code 128, etc.) to 2D (two-dimensional) barcodes such as QR (Quick Response) and Data Matrix codes. Over the last two decades there has been an increased use of Radio Frequency Identification (RFID) and Direct Part Marking (DPM) using lasers for product identification in agriculture. However, in agriculture there are still considerable challenges to adopting barcodes, RFID and DPM technologies, unlike in industry where these technologies have been very successful. This study was divided into three main objectives. Firstly, determination of the effect of speed, dirt, moisture and bar width on barcode detection was carried out both in the laboratory and a flower producing company, Brandkamp GmbH. This study developed algorithms for automation and detection of Code 128 barcodes under rough production conditions. Secondly, investigations were carried out on the effect of low laser marking energy on barcode size, print growth, colour and contrast on decoding 2D Data Matrix codes printed directly on apples. Three different apple varieties (Golden Delicious, Kanzi and Red Jonaprince) were marked with various levels of energy and different barcode sizes. Image processing using Halcon 11.0.1 (MvTec) was used to evaluate the markings on the apples. Finally, the third objective was to evaluate both algorithms for 1D and 2D barcodes. According to the results, increasing the speed and angle of inclination of the barcode decreased barcode recognition. Also, increasing the dirt on the surface of the barcode resulted in decreasing the successful detection of those barcodes. However, there was 100% detection of the Code 128 barcode at the company’s production speed (0.15 m/s) with the proposed algorithm. Overall, the results from the company showed that the image-based system has a future prospect for automation in horticultural production systems. It overcomes the problem of using laser barcode readers. The results for apples showed that laser energy, barcode size, print growth, type of product, contrast between the markings and the colour of the products, the inertia of the laser system and the days of storage all singularly or in combination with each other influence the readability of laser Data Matrix codes and implementation on apples. There was poor detection of the Data Matrix code on Kanzi and Red Jonaprince due to the poor contrast between the markings on their skins. The proposed algorithm is currently working successfully on Golden Delicious with 100% detection for 10 days using energy 0.108 J mm-2 and a barcode size of 10 × 10 mm2. This shows that there is a future prospect of not only marking barcodes on apples but also on other agricultural products for real time production

Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet.

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications

Decoding Book Barcode Images

This thesis investigated a method of barcode reconstruction to address the recovery of a blurred and convoluted one-dimensional barcode. There are a lot of types of barcodes used today, such as Code 39, Code 93, Code 128, etc. Our algorithm applies to the universal barcode, EAN 13. We extend the methodologies proposed by Iwen et al. (2013) in the journal article A Symbol-Based Algorithm for Decoding barcodes. The algorithm proposed in the paper requires a signal measured by a laser scanner as an input. The observed signal is modeled as a true signal corrupted by a Gaussian convolution, additional noises, and an unknown multiplier. The known barcode dictionaries were incorporated into the forward map between the true barcode and the observed barcode. Unlike the one proposed by Iwen et al., we take dictionaries of different patterns into account, specifically for decoding book barcodes from images which are captured with smartphones. We also presented numerical experiments that examined the performance of the proposed algorithm and illustrated that the unique determination of barcode digits is possible even in the presence of noise