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Image analysis and machine learning for detecting malaria

A review of recent advances in lane detection and departure warning system

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this article devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions.

Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite's Life Stage

This paper presents Automatic Number Plate extraction, character segmentation and recognition for Indian vehicles. In India, number plate models are not followed strictly. Characters on plate are in different Indian languages, as well as in English. Due to variations in the representation of number plates, vehicle number plate extraction, character segmentation and recognition are crucial. We present the number plate extraction, character segmentation and recognition work, with english characters. Number plate extraction is done using Sobel filter, morphological operations and connected component analysis. Character segmentation is done by using connected component and vertical projection analysis. Character recognition is carried out using Support Vector machine (SVM). The segmentation accuracy is 80% and recognition rate is 79.84 %.

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Number Plate Recognition for Indian Vehicles

Malaria is a serious worldwide health issue which causes globally an expected 3.4 billion individuals in danger of malaria in 2013. Malaria is an entirely preventable and treatable disease [1]. For fast diagnosis and acute treatment of Malaria is important to reduce the death rate. As the parasite changes morphology in its different life stages and its types varies, an experienced technician is required to identify types of malaria parasites in laboratory diagnosis. Current malaria analysis depends essentially on microscopic examination of Giemsa-stained thick and thin blood films, this method is time consuming and routine. According to the World Health Organization, it causes more than 1 million deaths arising from approximately 300 to 500 million infections every year [2]. For the proper medication of patient it is important to identify the species. For acute diagnosis of malaria, numerous analysts have proposed automated malaria detection devices using digital image processing. In this paper, we provide an approach to identify the species of malaria. The system describes image acquisition, preprocessing, segmentation algorithms, and classifier.

Automated system for malaria parasite identification

Blood cell segmentation is a critical innovation for automatic differential blood counting, classification and analysis in clinical examination. In color blood cell images segmentation and recognition are two essential issues in the field of biomedical cell morphology. This paper approaches methods to segment the blood cells from microscopic thin blood images. This data is the premise to perform higher level tasks for example, automatic differential blood counting, detection of different diseases such as Malaria, Babesia, Chagas disease, Anemia, Leukemia etc. A noteworthy necessity of an automated, real-time, computer vision-based cell segmentation system is an efficient method for segmenting different blood component such as Red Blood Cells (RBCs), White Blood Cells (WBCs), Platelets from input images for blood count as well as to detect the parasites present in blood cells. Input images are captured by connecting digital camera to microscope. Captured images are enhanced and segmented using K-mean clustering as well as global threshold. Overlapping cells are separated using Sobel edge detector and Watershed transform.

S. P. Narote

Papers

A review of recent advances in lane detection and departure warning system

Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite's Life Stage

Number Plate Recognition for Indian Vehicles

Automated system for malaria parasite identification

Blood cell segmentation from microscopic blood images