Systematic segmentation method based on PCA of image hue features for white blood cell counting

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works

Efficient online subspace learning with an indefinite kernel for visual tracking and recognition

We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an incremental KPCA in Krein space that does not require the calculation of preimages and therefore is both efficient and exact. Our approach has been motivated by the application of visual tracking for which we wish to employ a robust gradient-based kernel. We use the proposed nonlinear appearance model learned online via KPCA in Krein space for visual tracking in many popular and difficult tracking scenarios. We also show applications of our kernel framework for the problem of face recognition

Face recognition in an unconstrained environment for monitoring student attendance

Traditional paper based attendance monitoring systems are time consuming and suscep- tible to both error and data loss. Where technical advances have attempted to solve the problem, they tend to improve only small portions i.e. confidence that data has been collected satisfactorily can be very high but technology can also be difficult to use, time consuming and impossible especially if the overall system is down. Camera based face recognition has the potential to resolve most monitoring problems. It is passive, easy and inexpensive to utilise; and if supported by a human safeguard can be very reliable. This thesis evaluates a strategy to monitor lecture attendance using images captured by cheap web cams in an unconstrained environment. A traditional recognition pipeline is utilised in which faces are automatically detected and aligned to a standard coordinate system before extracting Scale Invariant Feature Transform (SIFT), Local Binary Pattern (LBP) and Eigenface based features for classification. A greedy algorithm is employed to match captured faces to reference images with faces labelled and added to the training set over time. Performance is evaluated on images captured from a small lecture series over ten weeks. It is evident that performance improves during the series as new reference material is included within the training data. This correlation demonstrates that the success of the system is determined not only by the on-going capturing process but also the quality and variability of the initial training data. Whilst the system is capable of reasonable success, the experiments show that it also yields an unacceptably high false positive rate and cannot be used in isolation. This is primarily because the greedy nature of the algorithm allows the possibility of assigning multiple images of the same person captured in the same lecture to different students including ‘no shows’