An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

Joint Visual and Wireless Tracking System

Object tracking is an important component in many applications including surveillance, manufacturing, inventory tracking, etc. The most common approach is to combine a surveillance camera with an appearance-based visual tracking algorithm. While this approach can provide high tracking accuracy, the tracker can easily diverge in environments where there are much occlusions. In recent years, wireless tracking systems based on different frequency ranges are becoming more popular. While systems using ultra-wideband frequencies suffer similar problems as visual systems, there are systems that use frequencies as low as in those in the AM band to circumvent the problems of obstacles, and exploit the near-field properties between the electric and magnetic waves to achieve tracking accuracy down to about one meter. In this dissertation, I study the combination of a visual tracker and a low-frequency wireless tracker to improve visual tracking in highly occluded area. The proposed system utilizes two homographies formed between the world coordinates with the image coordinates of the head and the foot of the target person. Using the world coordinate system, the proposed system combines a visual tracker and a wireless tracker in an Extended Kalman Filter framework for joint tracking. Extensive experiments have been conducted using both simulations and real videos to demonstrate the validity of our proposed scheme

A mathematical model for computerized car crash detection using computer vision techniques

My proposed approach to the automatic detection of traffic accidents in a signalized intersection is presented here. In this method, a digital camera is strategically placed to view the entire intersection. The images are captured, processed and analyzed for the presence of vehicles and pedestrians in the proposed detection zones. Those images are further processed to detect if an accident has occurred; The mathematical model presented is a Poisson distribution that predicts the number of accidents in an intersection per week, which can be used as approximations for modeling the crash process. We believe that the crash process can be modeled by using a two-state method, which implies that the intersection is in one of two states: clear (no accident) or obstructed (accident). We can then incorporate a rule-based AI system, which will help us in identifying that a crash has taken or will possibly take place; We have modeled the intersection as a service facility, which processes vehicles in a relatively small amount of time. A traffic accident is then perceived as an interruption of that service

Mathematical modeling for partial object detection.

From a computer vision point of view, the image is a scene consisting of objects of interest and a background represented by everything else in the image. The relations and interactions among these objects are the key factors for scene understanding. In this dissertation, a mathematical model is designed for the detection of partially occluded faces captured in unconstrained real life conditions. The proposed model novelty comes from explicitly considering certain objects that are common to occlude faces and embedding them in the face model. This enables the detection of faces in difficult settings and provides more information to subsequent analysis in addition to the bounding box of the face. In the proposed Selective Part Models (SPM), the face is modelled as a collection of parts that can be selected from the visible regular facial parts and some of the occluding objects which commonly interact with faces such as sunglasses, caps, hands, shoulders, and other faces. With the face detection being the first step in the face recognition pipeline, the proposed model does not only detect partially occluded faces efficiently but it also suggests the occluded parts to be excluded from the subsequent recognition step. The model was tested on several recent face detection databases and benchmarks and achieved state of the art performance. In addition, detailed analysis for the performance with respect to different types of occlusion were provided. Moreover, a new database was collected for evaluating face detectors focusing on the partial occlusion problem. This dissertation highlights the importance of explicitly handling the partial occlusion problem in face detection and shows its efficiency in enhancing both the face detection performance and the subsequent recognition performance of partially occluded faces. The broader impact of the proposed detector exceeds the common security applications by using it for human robot interaction. The humanoid robot Nao is used to help in teaching children with autism and the proposed detector is used to achieve natural interaction between the robot and the children by detecting their faces which can be used for recognition or more interestingly for adaptive interaction by analyzing their expressions

Pedestrian detection and tracking using stereo vision techniques

Automated pedestrian detection, counting and tracking has received significant attention from the computer vision community of late. Many of the person detection techniques described so far in the literature work well in controlled environments, such as laboratory settings with a small number of people. This allows various assumptions to be made that simplify this complex problem. The performance of these techniques, however, tends to deteriorate when presented with unconstrained environments where pedestrian appearances, numbers, orientations, movements, occlusions and lighting conditions violate these convenient assumptions. Recently, 3D stereo information has been proposed as a technique to overcome some of these issues and to guide pedestrian detection. This thesis presents such an approach, whereby after obtaining robust 3D information via a novel disparity estimation technique, pedestrian detection is performed via a 3D point clustering process within a region-growing framework. This clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. This pedestrian detection technique requires no external training and is able to robustly handle challenging real-world unconstrained environments from various camera positions and orientations. In addition, this thesis presents a continuous detect-and-track approach, with additional kinematic constraints and explicit occlusion analysis, to obtain robust temporal tracking of pedestrians over time. These approaches are experimentally validated using challenging datasets consisting of both synthetic data and real-world sequences gathered from a number of environments. In each case, the techniques are evaluated using both 2D and 3D groundtruth methodologies