Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

A model-based method for 3D reconstruction of cerebellar parallel fibres from high-resolution electron microscope images

In order to understand how the brain works, we need to understand how its neural circuits process information. Electron microscopy remains the only imaging technique capable of providing sufficient resolution to reconstruct the dense connectivity between all neurons in a circuit. Automated electron microscopy techniques are approaching the point where usefully large circuits might be successfully imaged, but the development of automated reconstruction techniques lags far behind. No fully-automated reconstruction technique currently produces acceptably accurate reconstructions, and semi-automated approaches currently require an extreme amount of manual effort. This reconstruction bottleneck places severe limits on the size of neural circuits that can be reconstructed. Improved automated reconstruction techniques are therefore highly desired and under active development. The human brain contains ~86 billion neurons and ~80% of these are located in the cerebellum. Of these cerebellar neurons, the vast majority are granule cells. The axons of these granule cells are called parallel fibres and tend to be oriented in approximately the same direction, making 2+1D reconstruction approaches feasible. In this work we focus on the problem of reconstructing these parallel fibres and make four main contributions: (1) a model-based algorithm for reconstructing 2D parallel fibre cross-sections that achieves state of the art 2D reconstruction performance; (2) a fully-automated algorithm for reconstructing 3D parallel fibres that achieves state of the art 3D reconstruction performance; (3) a semi-automated approach for reconstructing 3D parallel fibres that significantly improves reconstruction accuracy compared to our fully-automated approach while requiring ~40 times less labelling effort than a purely manual reconstruction; (4) a "gold standard" ground truth data set for the molecular layer of the mouse cerebellum that will provide a valuable reference for the development and benchmarking of reconstruction algorithms

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p