Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting/recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision/recall and edit distance.

Adaptive binarization is an important first step in many document analysis and OCR processes. This paper describes a fast adaptive binarization algorithm that yields the same quality of binarization as the Sauvola method,¹ but runs in time close to that of global thresholding methods (like Otsu's method²), independent of the window size. The algorithm combines the statistical constraints of Sauvola's method with integral images.³ Testing on the UW-1 dataset demonstrates a 20-fold speedup compared to the original Sauvola algorithm.

With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset. Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier’s performance and calls for additional performance metrics to substantiate the results.

There is an urgent need for the development of sampling techniques which can provide accurate and precise count, size, and biomass data for fish. This information is essential to support the decision-making processes of fisheries and marine conservation managers and scientists. Digital video technology is rapidly improving, and it is now possible to record long periods of high resolution digital imagery cost effectively, making single or stereo-video systems one of the primary sampling tools. However, manual species identification, counting, and measuring of fish in stereo-video images is labour intensive and is the major disincentive against the uptake of this technology. Automating species identification using technologies developed by researchers in computer vision and machine learning would transform marine science. In this article, a new paradigm of image set classification is presented that can be used to achieve improved recognition rates for a number of fish species. State-of-the-art image set construction, modelling, and matching algorithms from computer vision literature are discussed with an analysis of their application for automatic fish species identification. It is demonstrated that these algorithms have the potential of solving the automatic fish species identification problem in underwater videos captured within unconstrained environments.