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Background Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas. Results We designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data. Conclusion Malaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data.

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\%$\end{document}. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis.

Objective: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results are obtained for parasite detection in thick blood smears for a smartphone application using deep learning methods. Significance: Automated parasite detection running on smartphones is a promising alternative to manual parasite counting for malaria diagnosis, especially in areas lacking experienced parasitologists.

Purpose Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis. Methods A main tool for diagnosing tuberculosis is the conventional chest X-ray. We are investigating the possibility of discriminating automatically between drug-resistant and drug-sensitive tuberculosis in chest X-rays by means of image analysis and machine learning methods. Results For discriminating between drug-sensitive and drug-resistant tuberculosis, we achieve an area under the receiver operating characteristic curve (AUC) of up to 66%, using an artificial neural network in combination with a set of shape and texture features. We did not observe any significant difference in the results when including follow-up X-rays for each patient. Conclusion Our results suggest that a chest X-ray contains information about the likelihood of a drug-resistant tuberculosis infection, which can be exploited computationally. We therefore suggest to repeat the experiments of our pilot study on a larger set of chest X-rays.

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.

A malarial infection is diagnosed and monitored by screening microscope images of blood smears for parasite-infected red blood cells. Millions of blood slides are manually screened for parasites every year, which is a tedious and error-prone process, and which largely depends on the expertise of the microscopists. We have developed a software to perform this task on a smartphone, using machine learning and image analysis methods for counting infected red blood cells automatically. The method we implemented first needs to detect and segment red blood cells. However, the presence of white blood cells (WBCs) contaminates the red blood cell detection and segmentation process because WBCs can be miscounted as red blood cells by automatic cell detection methods. As a result, a preprocessing step for WBC elimination is essential. Our paper proposes a novel method for white blood cell segmentation in microscopic images of blood smears. First, a range filtering algorithm is used to specify the location of white blood cells in the image following a Chan- Vese level-set algorithm to estimate the boundaries of each white blood cell present in the image. The proposed segmentation algorithm is systematically tested on a database of more than 1300 thin blood smear images exhibiting approximately 1350 WBCs. We evaluate the performance of the proposed method for the two WBC detection and WBC segmentation steps by comparing the annotations provided by a human expert with the results produced by the proposed algorithm. Our detection technique achieves a 96.37 % overall precision, 98.37 % recall, and 97.36 % Fl-score. The proposed segmentation method grants an overall 82.28 % Jaccard Similarity Index. These results demonstrate that our approach allows us to filter out WBCs, which significantly improves the precision of the cell counts for malaria diagnosis.

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1). Funding Agencies|Slovenian research agency research programs [P2-0214, P2-0094]; Slovenian research agency project [J2-8175]; Czech Science Foundation Project [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme Visual Surveillance and Insight; Faculty of Computer Science, University of Ljubljana, Slovenia

Weighting pixel contribution considering its location is a key feature in many fundamental image processing tasks including filtering, object modeling and distance matching. Several techniques have been proposed that incorporate Spatial information to increase the accuracy and boost the performance of detection, tracking and recognition systems at the cost of speed. But, it is still not clear how to efficiently ex- tract weighted local histograms in constant time using integral histogram. This paper presents a novel algorithm to compute accurately multi-scale Spatially weighted local histograms in constant time using Weighted Integral Histogram (SWIH) for fast search. We applied our spatially weighted integral histogram approach for fast tracking and obtained more accurate and robust target localization result in comparison with using plain histogram., 5 pages, 7 figures

Persistent detection and tracking of moving vehicles in airborne imagery provide indispensable information for many traffic surveillance applications including traffic monitoring and management, navigation systems, activity recognition and event detection. This paper presents a collaborative Spatial Pyramid Context-aware detection and Tracking system (SPCT) for moving vehicles in dense urban aerial imagery. The proposed system is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG (PHoG) and exploit integral histogram as building block to meet the demands of real-time performance. The extensive experiments on ARGUS and ABQ wide aerial video and comparison with state-of-the-art single object trackers confirm that combining complementary tracking cues in an intelligent fusion framework is essential to address the challenges of persistent tracking in low frame rate Wide Aerial Motion Imagery (WAMI).

Most Wide Area Motion Imagery (WAMI) based trackers use motion based cueing for detecting and tracking moving objects. The results are very high false alarm rates in urban environments with tall structures due to parallax effects. This paper proposes an accurate moving object detection method using a precise orthorectification approach for ground stabilization combined with accurate multiview depth maps to reduce the number of false positives induced by parallax effects by 90 percent. Proposed hybrid moving vehicle detection approach for large scale aerial urban imagery is based on fusion of motion detection mask obtained from median-based background subtraction and tall structures height mask provided by image depth map information. Using buildings mask enables us to improve the object level detection accuracy in terms of F-measure by 57 percent from 22.2% to 79.2%.

Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automatic image recognition technologies based on machine learning have been applied to malaria blood smears for diagnosis before. However, the practical performance has not been sufficient so far. This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. In a ten-fold cross-validation based on 27,578 single cell images, the average accuracy of our new 16-layer CNN model is 97.37%. A transfer learning model only achieves 91.99% on the same images. The CNN model shows superiority over the transfer learning model in all performance indicators such as sensitivity (96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision (97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews correlation coefficient (94.75% vs 85.25%).

The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

Malaria is a blood disease caused by thePlasmodiumparasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.

Current video tracking systems often employ a rich set of intensity, edge, texture, shape and object level features combined with descriptors for appearance modeling. This approach increases tracker robustness but is compu- tationally expensive for realtime applications and localization accuracy can be adversely affected by including distracting features in the feature fusion or object classification processes. This paper explores offline feature subset selection using a filter-based evaluation approach for video tracking to reduce the dimensionality of the feature space and to discover relevant representative lower dimensional subspaces for online tracking. We com- pare the performance of the exhaustive FOCUS algorithm to the sequential heuristic SFFS, SFS and RELIEF feature selection methods. Experiments show that using offline feature selection reduces computational complex- ity, improves feature fusion and is expected to translate into better online tracking performance. Overall SFFS and SFS perform very well, close to the optimum determined by FOCUS, but RELIEF does not work as well for feature selection in the context of appearance-based object tracking.

The integral histogram for images is an efficient preprocessing method for speeding up diverse computer vision algorithms including object detection, appearance-based tracking, recognition and segmentation. Our proposed Graphics Processing Unit (GPU) implementation uses parallel prefix sums on row and column histograms in a cross-weave scan with high GPU utilization and communication-aware data transfer between CPU and GPU memories. Two different data structures and communication models were evaluated. A 3-D array to store binned histograms for each pixel and an equivalent linearized 1-D array, each with distinctive data movement patterns. Using the 3-D array with many kernel invocations and low workload per kernel was inefficient, highlighting the necessity for careful mapping of sequential algorithms onto the GPU. The reorganized 1-D array with a single data transfer to the GPU with high GPU utilization, was 60 times faster than the CPU version for a 1K ×1K image reaching 49 fr/sec and 21 times faster for 512×512 images reaching 194 fr/sec. The integral histogram module is applied as part of the likelihood of features tracking (LOFT) system for video object tracking using fusion of multiple cues.

The visualization of an audio signal’s time structure is the primary step for every rhythm synchronizer system. To achieve this, principle components of the musical signal beats as strength, tempo and onset time must be detected. A filter bank of 180 different comb filters (from 60 bpm to 240 bpm) is convolved with different styles of musical signal to visualize according to human perception. In each interval, tempo in frequency domain and then its onset time in time domain are extracted. Now the key point is how to cover the gap between each two adjacent beat occurrences, from the last beat terminal to the next beat onset time. A magic number of 100 milliseconds (corresponding to human perception) is used. This interval dichotomizes equally. The first 50ms is used to stretch the signal to the relaxation point at the zero and the next one to grow it up to the next consequent beat onset. Therefore, a continuous sinusoidal signal is presented to visualize the corresponding music rhythm. Different simulation plots of various music styles and their overlaps have illustrated the proposed method.

Cellular Genetic Algorithms (cGAs) are a subclass of Genetic Algorithms (GAs) in which the population diversity and exploration are enhanced thanks to the existence of small overlapped neighborhoods. Such kind of structured algorithms is well suited for complex problems. In this paper, a cGA for solving the vehicle routing problem with time windows (VRPTW) is proposed. The benchmark of Solomon is selected for testing the proposed cGAs, and compares them with some other heuristics in the literature. The results demonstrate that the proposed cGA has a good potential for solving this kind of problems.

This paper introduce a harmonic sinusoidal voltage that will actuate a 3M's VHB 4905/4910 Dielectric Elastomer Artificial Muscle (DEAM). This actuation voltage that is actually a sinusoidal pattern of music rhythm will synchronize the artificial muscle movements with music harmony. To have this manner, first a sinusoidal pattern of music rhythm will be extracted. Principles of this pattern are strengths, tempos and phases of original music rhythm at different intervals. Then due to irregular and infinite range of beat strength, a fuzzy controller will be proposed to map beat strength of music rhythm in the plausible range of artificial muscle displacements. This fuzzy controller is responsible to output a cogent strain rate in response to the current interval beati and its calculated tempo i and certainly according to human perception. Frequency of the harmonic voltage signal which is the tempo of rhythm has also been adjusted to respect ratio 1:2 of DE response frequency. Results in the forms of plots and an offline simulation have demonstrated the performance of proposed actuation voltage.