Data mining based learning algorithms for semi-supervised object identification and tracking

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, and track objects with predictable high degrees of specificity and sensitivity. Automatic object detection and tracking algorithms face several obstacles, such as large and incomplete datasets, ill-defined regions of interest (ROIs), variable scalability, lack of compactness, angular regions, partial occlusions, environmental variables, and unknown potential object classes, which work against their ability to achieve accurate real-time results. Methods must produce fast and accurate results by streamlining image processing, data compression and reduction, feature extraction, classification, and tracking algorithms. Data mining techniques can sufficiently address these challenges by implementing efficient and accurate dimensionality reduction with feature extraction to refine incomplete (ill-partitioning) data-space and addressing challenges related to object classification, intra-class variability, and inter-class dependencies. A series of methods have been developed to combat many of the challenges for the purpose of creating a sensor exploitation and tracking framework for real time image sensor inputs. The framework has been broken down into a series of sub-routines, which work in both series and parallel to accomplish tasks such as image pre-processing, data reduction, segmentation, object detection, tracking, and classification. These methods can be implemented either independently or together to form a synergistic solution to object detection and tracking. The main contributions to the SE field include novel feature extraction methods for highly discriminative object detection, classification, and tracking. Also, a new supervised classification scheme is presented for detecting objects in urban environments. This scheme incorporates both novel features and non-maximal suppression to reduce false alarms, which can be abundant in cluttered environments such as cities. Lastly, a performance evaluation of Graphical Processing Unit (GPU) implementations of the subtask algorithms is presented, which provides insight into speed-up gains throughout the SE framework to improve design for real time applications. The overall framework provides a comprehensive SE system, which can be tailored for integration into a layered sensing scheme to provide the war fighter with automated assistance and support. As more sensor technology and integration continues to advance, this SE framework can provide faster and more accurate decision support for both intelligence and civilian applications

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Lymphatic Filariasis detection in microscopic images

In Africa, the propagation of parasites like the lymphatic filariasis is complicatingseriously the efforts of health professionals to cure certain diseases. Although there aremedicines capable to treat the lymphatic filariasis, this needs to be discovered firstly which isnot always an easy task having into account that in most countries affected by this disease it canonly be detected at night (nocturne). The lymphatic filariasis is then, a parasitical infectionwhich can originate changes or ruptures in the lymphatic system as well as an abnormal growthof certain areas of the body causing pain, incapacity and social stigma.Approximately 1.23 billion people in 58 countries from all over the world are threatenedby this disease which requires a preventive treatment to stop its propagation which makes iteven more important for the existence of a mechanism that is less costly and more agile in theanalysis of a blood smear to verify the existence of microfilariae (little worms that are producedby other adult worms while housed in the lymphatic system).The lymphatic filariasis is caused by an infection with nematodes ("roundworms") of theFilariodidea family in which three types are inserted: Wuchereria Bancroft, responsible for 90%of all cases; Brugia Malayi, responsible for almost every remaining; B.Timori also causing thedisease. All three have characteristics that can differentiate them which allow them to beidentified.The current identification process of the disease consists on the analysis of microfilariae ina blood smear with a blood sample through a microscope and its identification by the observer.Taking this into account, it is intended to develop image analysis and processingtechniques for the recognition and counting of the two principal types of filarial worms from athin blood smear, a smartphone and a portable microscope making the detection possiblewithout the need of a health professional and consequent automation of the process. To makethis possible an adapter smartphone-microscope can be used to obtain an image with themagnification of 40x3. The images can then be analyzed in a server or in the smartphone, if ithas enough processing for it. It is expected from this process that the need to resort to labs toprocess the blood smear gets fulfilled making the process more accessible and agile instead ofcostly and slow.For the detection of the parasites from the acquired images it is intended to implement,experiment and choose the more adequate operations. These comprise pre-processing operationswith the goal to enhance the acquired images and eliminate possible artifacts prevenient fromthe acquisition system. However, the principal operations should be those that allow theverification of existence or nonexistence, recognition and classification of the pretendedparasites. Processing and analysis techniques that are common in these processes are based inthe extraction of features (e.g. SIRF, SURF, and FLANN) template similarity, edge detectionand description of contours and recognition of statistical patterns.Once detected and recognized one or more parasites and its types should be defined andused a rule to declare the presence of the disease and its stage

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.Comment: 29 pages; 13 figures; 8 table

Machine learning approach to thermite weld defects detection and classification.

Masters Degree. University of KwaZulu- Natal, Durban.The defects formed during the thermite welding process between two sections of rails require the welded joints to be inspected for quality purpose. The commonly used non-destructive method for inspection is Radiography testing. However, the detection and classification of various defects from the generated radiography imagesremains a costly, lengthy and subjective process as it is purely conducted manually by trained experts. It has been shown that most rail breaks occur due to a crack that initiated from the weld joint defect that was not detected. To meet the requirements of the modern technologies, the development of an automated detection and classification model is significantly demanded by the railway industry. This work presents a method based on image processing and machine learning techniques to automatically detect and classify welding defects. Radiography images are first enhanced using the Contrast Limited Adaptive Histogram Equalisation method; thereafter, the Chan-Vese Active Contour Model is applied to the enhanced images to segment and extract the weld joint as the Region of Interest from the image background. A comparative investigation between the Local Binary Patterns descriptor and the Bag of Visual Words approach with Speeded Up Robust Features descriptor was carried out for extracting features in the weld joint images. The effectiveness of the aforementioned feature extractors was evaluated using the Support Vector Machines, K-Nearest Neighbours and Naive Bayes classifiers. This study’s experimental results showed that the Bag of Visual Words approach when used with the Support Vector Machines classifier, achieves the best overall classification accuracy of 94.66%. The proposed method can be expanded in other industries where Radiography testing is used as the inspection tool

In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

A synthetic image analysis strategy is proposed for in-situ crystal size measurement and shape identification for monitoring crystallization processes, based on using a real-time imaging system. The proposed method consists of image processing, feature analysis, particle sieving, crystal size measurement, and crystal shape identification. Fundamental image features of crystals are selected for efficient classification. In particular, a novel shape feature, referred to as inner distance descriptor, is introduced to quantitatively describe different crystal shapes, which is relatively independent of the crystal size and its geometric direction in an image captured for analysis. Moreover, a pixel equivalent calibration method based on subpixel edge detection and circle fitting is proposed to measure crystal sizes from the captured images. In addition, a kernel function based method is given to deal with nonlinear correlations between multiple features of crystals, facilitating computation efficiency for real-time shape identification. Case study and experimental results from the cooling crystallization of l-glutamic acid demonstrate that the proposed image analysis method can be effectively used for in-situ crystal size measurement and shape identification with good accuracy