Mohd Asyraf Zulkifley

Robust hierarchical multiple hypothesis tracker for multiple-object tracking

Robust multiple object tracking is the backbone of many higher-level applications such as people counting, behavioral analytics and biomedical imaging. We enhance multiple hypothesis tracker robustness to the problems of split, merge, occlusion and fragment through hierarchical approach. Foreground segmentation and clustered optical flow are used as the first-level tracker input. Only associated track of the first level is fed into the second level with the additional of two virtual measurements. Occlusion predictor is obtained by using the predicted data of each track to distinguish between merge and occlusion. Kalman filter is used to predict and smooth the tracks state. Gaussian modelling is used to measure the quality of the hypotheses. Histogram intersection is applied to limit the size expansion of the track. The results show improvement both in terms of accuracy and precision compared to the benchmark trackers [1, 2].

Robust Foreground Detection: A Fusion of Masked GreyWorld, Probabilistic Gradient Information and Extended Conditional Random Field Approach

Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.

Sudden event recognition: a survey

Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

Telepointer technology in telemedicine : a review

Telepointer is a powerful tool in the telemedicine system that enhances the effectiveness of long-distance communication. Telepointer has been tested in telemedicine, and has potential to a big influence in improving quality of health care, especially in the rural area. A telepointer system works by sending additional information in the form of gesture that can convey more accurate instruction or information. It leads to more effective communication, precise diagnosis, and better decision by means of discussion and consultation between the expert and the junior clinicians. However, there is no review paper yet on the state of the art of the telepointer in telemedicine. This paper is intended to give the readers an overview of recent advancement of telepointer technology as a support tool in telemedicine. There are four most popular modes of telepointer system, namely cursor, hand, laser and sketching pointer. The result shows that telepointer technology has a huge potential for wider acceptance in real life applications, there are needs for more improvement in the real time positioning accuracy. More results from actual test (real patient) need to be reported. We believe that by addressing these two issues, telepointer technology will be embraced widely by researchers and practitioners.

Design and development of a content-based medical image retrieval system for spine vertebrae irregularity

BackgroundContent-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.MethodsIn this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures.ResultsExperimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets.ConclusionsThe proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.

An analysis of x-ray image enhancement methods for vertebral bone segmentation

Image enhancement is a critical component in getting a good segmentation, especially for X-ray images. Magnification of the contrast and sharpness of the image will increase the accuracy of the subsequent modules for an autonomous disease diagnosis system. In this paper, we analyze various methods of preprocessing techniques for vertebral bone segmentation. Three methods are considered which are histogram equalization (HE), gamma correction (GC) and contrast limited adaptive histogram equalizer (CLAHE). This work aims to compare and quantify the precision and accuracy of the techniques that are used to enhance the image quality. Experimental results of the system yield favorable results where the most accurate technique is CLAHE, followed by GC and HE.

Robust Observation Detection for Single Object Tracking: Deterministic and Probabilistic Patch-Based Approaches

In video analytics, robust observation detection is very important as the content of the videos varies a lot, especially for tracking implementation. Contrary to the image processing field, the problems of blurring, moderate deformation, low illumination surroundings, illumination change and homogenous texture are normally encountered in video analytics. Patch-Based Observation Detection (PBOD) is developed to improve detection robustness to complex scenes by fusing both feature- and template-based recognition methods. While we believe that feature-based detectors are more distinctive, however, for finding the matching between the frames are best achieved by a collection of points as in template-based detectors. Two methods of PBOD—the deterministic and probabilistic approaches—have been tested to find the best mode of detection. Both algorithms start by building comparison vectors at each detected points of interest. The vectors are matched to build candidate patches based on their respective coordination. For the deterministic method, patch matching is done in 2-level test where threshold-based position and size smoothing are applied to the patch with the highest correlation value. For the second approach, patch matching is done probabilistically by modelling the histograms of the patches by Poisson distributions for both RGB and HSV colour models. Then, maximum likelihood is applied for position smoothing while a Bayesian approach is applied for size smoothing. The result showed that probabilistic PBOD outperforms the deterministic approach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavy processing requirement.

Getting robust observation for single object tracking: a statistical Kernel-based approach

Mean shift-based algorithms perform well when the tracked object is in the vicinity of the current location. This cause any fast moving object especially when there is no overlapping region between the frames fails to be tracked. The aim of our algorithm is to offer robust kernel-based observation as an input to a single object tracking. We integrate kernel-based method with feature detectors and apply statical decision making. The foundation of the algorithm is patch matching where Epanechnikov kernel-based histogram is used to find the best patch. The patch is built based on Shi and Tomasi [1] corner detector where a vector descriptor is built at each detected corner. The patches are built at every matched points and the similarity between two histograms are modelled by Gaussian distribution. Two set of histograms are built based on RGB and HSV colour space where Neyman-Pearson method decides the best colour model. Diamond search configuration is applied to smooth out the patch position by applying maximum likelihood method. The works by Comaniciu et al. [2] is used as performance comparison. The results show that our algorithm performs better as we have no failure yet lesser average accuracy in tracking fast moving object.

Robust hierarchical multiple hypothesis tracker for multiple object tracking

Robust multiple object tracking is the backbone of many higher-level applications such as people counting, behavioral analytics and biomedical imaging. We enhance multiple hypothesis tracker robustness to the problems of split, merge, occlusion and fragment through hierarchical approach. Foreground segmentation and clustered optical flow are used as the first-level tracker input. Only associated track of the first level is fed into the second level with the additional of two virtual measurements. Occlusion predictor is obtained by using the predicted data of each track to distinguish between merge and occlusion. Kalman filter is used to predict and smooth the tracks state. Gaussian modelling is used to measure the quality of the hypotheses. Histogram intersection is applied to limit the size expansion of the track. The results show improvement both in terms of accuracy and precision compared to the benchmark trackers [1, 2].

On analyzing various density functions of local binary patterns for optic disc segmentation

In building an automated glaucoma detection system, optic disc segmentation is the first step that needs to be implemented follows by optic cup segmentation in order to quantify the severity level of glaucoma. Glaucoma is an ocular eye disease that can lead to gradual vision loss and permanent blindness if it is not treated in the early stage. Many glaucoma patients are unaware of their disease since they rarely encounter any symptom that can lead to glaucoma. Thus, detecting glaucoma during the early stage is very important to reduce the treatment risk. This paper proposes optic disc segmentation by using local binary patterns operator (LBP), a feature for textural classification in image processing. LBP is utilized only on red channel of RGB fundus image because of higher contrast between optic disc and its surrounding area compared to the blue and green channels. Smoothing technique, specifically, histogram equalization is performed to improve the quality of input image before LBP method is applied. Lastly, morphological operation and filtering are applied to filter out the artifacts and remove the noise from the segmented image. RIM-One database is used to validate the simulation results with Exponential distribution achieve better performance with average accuracy and precision of 0.8951 and 0.7390 respectively.