An automatic image analysis methodology for the measurement of droplet size distributions in liquid–liquid dispersion: round object detection

This article presents an efficient and economical automatic image analysis technique for the droplet characterization in a liquid–liquid dispersion. The methodology employs a combination of the Satoshi Suzuki's [Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process. 1985;30:32–46] find contours algorithm and the method of minimal enclosing circle identification, proposed by Emo Welzl [Smallest enclosing disks (balls and ellipsoids). Berlin, Heidelberg: Springer; 1991. p. 359–370. chapter 24], to achieve the objectives. The round object detection algorithm has been designed for the identification and verification of correct droplets in the mixture which helped to increase the accuracy of automatic detection. Tests have been performed on various sets of images obtained during several emulsification processes and contain examples of droplets which differ in size, density, volume and appearance etc. An effective communication between the two methodologies and newly introduced algorithms demonstrated an accuracy of 90% or above in the measurement of droplet size distribution and Sauter mean diameters through an automatic vision-based system

Anveshak - A Groundtruth Generation Tool for Foreground Regions of Document Images

We propose a graphical user interface based groundtruth generation tool in this paper. Here, annotation of an input document image is done based on the foreground pixels. Foreground pixels are grouped together with user interaction to form labeling units. These units are then labeled by the user with the user defined labels. The output produced by the tool is an image with an XML file containing its metadata information. This annotated data can be further used in different applications of document image analysis.Comment: Accepted in DAR 201

Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics

Advances in AI based computer vision has led to a significant growth in synthetic image generation and artificial image tampering with serious implications for unethical exploitations that undermine person identification and could make render AI predictions less explainable.Morphing, Deepfake and other artificial generation of face photographs undermine the reliability of face biometrics authentication using different electronic ID documents.Morphed face photographs on e-passports can fool automated border control systems and human guards.This paper extends our previous work on using the persistent homology (PH) of texture landmarks to detect morphing attacks.We demonstrate that artificial image tampering distorts the spatial distribution of texture landmarks (i.e. their PH) as well as that of a set of image quality characteristics.We shall demonstrate that the tamper caused distortion of these two slim feature vectors provide significant potentials for building explainable (Handcrafted) tamper detectors with low error rates and suitable for implementation on constrained devices.Comment: 6 pages, 7 figures, 3 table

О распознавании форменных объектов крови на основе медицинских изображений

Рассматривается задача постановки возможного диагноза по гематологическому анализу цифрового изображения эритроцитов. Описываются шаги по предварительной обработке изображения для уменьшения шумов и точности сегментации объектов клеток на классы. Для каждого этапа приведены примеры работы фильтро

Unconventional TV Detection using Mobile Devices

Recent studies show that the TV viewing experience is changing giving the rise of trends like "multi-screen viewing" and "connected viewers". These trends describe TV viewers that use mobile devices (e.g. tablets and smart phones) while watching TV. In this paper, we exploit the context information available from the ubiquitous mobile devices to detect the presence of TVs and track the media being viewed. Our approach leverages the array of sensors available in modern mobile devices, e.g. cameras and microphones, to detect the location of TV sets, their state (ON or OFF), and the channels they are currently tuned to. We present the feasibility of the proposed sensing technique using our implementation on Android phones with different realistic scenarios. Our results show that in a controlled environment a detection accuracy of 0.978 F-measure could be achieved.Comment: 4 pages, 14 figure

WordFences: Text localization and recognition

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a unique pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing by combining semantic word segmentation with a voting scheme for merging segmentations of multiple scales, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, end-to-end word recognition achieves state-of-the-art 86% F-Score on ICDAR13