Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of cameras and optical devices has made it economically feasible to deploy front-mounted intelligent systems for visual-based event detection. Prior to vehicle event detection, detecting vehicles robustly in real time is challenging, especially conducting detection process in images captured by a dynamic camera. Therefore in this study, a robust vehicle detector is developed. The proposed contribution is three-fold. Road modelling is first proposed to confine detection area for maintaining low computation complexity and reducing false alarms as well. Haar-like features and eigencolours are then employed for the vehicle detector. To tackle the occlusion problem, chamfer distance is used to estimate the probability of each individual vehicle. Consequently, to find the bounding box of vehicle candidate, the authors take the configuration of a normalised chamfer distance map that corresponds to the maximum score as the target using exponential entropy. Experiments on an extensive dataset show that the proposed system can effectively detect vehicles under different lighting and traffic conditions, and thus demonstrates its feasibility in real-world environments.

The authors propose a novel object detection algorithm based on shape matching using a single sketch of an object. The proposed algorithm uses circular arc segments to describe image edges; this approach is advantageous for shape description, shape expression and reconstruction. Circular arcs are initially segmented from the image contour using the split-and-merge method, and they are extended, being partially overlapped with neighbouring circular arcs. The extracted circular arcs of the object sketch constitute an attributed relational graph as a structured object model. Circular arcs in the test image are refined by the bottom-up process of circular arc extension, and matched with circular arcs in the object model by the top-down process of end-point adjustment. The authors design end-point-based shape descriptors to encode local shape information. Hough voting aggregates the detection of circular arcs to localise the object. Probabilistic relaxation verifies the detection candidates and delineate the object boundaries. The proposed object detection system benefits from reliable extraction of contour segments, efficient and discriminative shape encoding, and flexible and robust shape matching. It exhibits competitive object detection performance in experiments using real images.

A novel approach is presented to gain high classification rate for each class of ImageCLEF 2007 medical database. The learning phase consists of four iterations where different classification models were generated as per iteration. For the iterations, a model generation process was performed in two steps. The first step starts with construction of a model from the entire dataset. This model was then assessed to filter high accuracy classes (HAC). These classes were those predicted with an accuracy rate above 80%. This evaluation performed on 20% of the training dataset was taken as test data. In the second step, classes under HAC were only used to construct the classification model. The same processes will be performed in the next iteration on the classes which were left with accuracy below 80% from the previous iteration. The methodology presented is based on a bag of visual words for feature extraction and the radial basis function (RBF)-based support vector machine classifier. As a result, four classification models were generated from 77, 17, 12 and 10 classes, respectively. These models were constructed and evaluated on a database consisting of 11 000 medical X-ray images (training dataset) and 1000 (testing dataset) of 116 classes. The accuracy rate obtained by each generated model outperformed the results obtained by only one model on the entire dataset.

An adaptive shadow detection algorithm is proposed to eliminate interference on object detection from the shadow. The algorithm uses three components in YUV colour space to identify shadow pixels from the candidate foreground. An adaptive threshold estimator is designed to improve shadow detection accuracy and adaptive capacity in various lighting conditions. This estimator uses edge detection method to obtain global texture, as well statistical calculations to obtain the thresholds. Algorithm has the characteristic of low complexity and little restraint; hence it is suitable for real time-moving shadow detection in various lighting conditions. Experiment results show that this algorithm can obtain a high detection accuracy and the time-assume is greatly shortened compared with other algorithms with similar accuracy.

In this study, the authors present a new area-based stereo matching algorithm that computes dense disparity maps for a real-time vision system. Although many stereo matching algorithms have been proposed in recent years, correlation-based algorithms still have an edge because of speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation-based algorithms. In the proposed approach, two correlation windows are used to improve the performance of the algorithm while maintaining its real-time suitability. The CPU implementation of the proposed algorithm computes more than 10 frame/s. Unlike other area-based stereo matching algorithms, this method works very well at disparity boundaries as well as in low textured image areas and computes a dense and sharp disparity map. Evaluations on the benchmark Middlebury stereo datasets have been performed to demonstrate the qualitative and quantitative performance of the proposed algorithm.

Laser range finder and omnidirectional cameras are becoming a promising combination of sensors to extract rich environmental information. This information includes textured plane extraction, vanishing points, catadioptric projection of vertical and horizontal lines, or invariant image features. However, many indoor scenes do not have enough texture information to describe the environment. In these situations, vertical edges could be used instead. This study presents a sensor model that is able to extract three-dimensional position of vertical edges from a range-augmented omnidirectional vision sensor. Using the unified spherical model for central catadioptric sensors and the proposed sensor model, the vertical edges are locally projected, improving the data association for mapping and localisation. The proposed sensor model was tested using the FastSLAM algorithm to solve the simultaneous localisation and mapping problem in indoor environments. Real-world qualitative and quantitative experiments are presented to validate the proposed approach using a Pioneer-3DX mobile robot equipped with a URG-04LX laser range finder and an omnidirectional camera with parabolic mirror.

It is essential to build good image representations for many computer vision tasks. In this study, the authors propose a hierarchical spatial pyramid max pooling method based on scale-invariant feature transform (SIFT) features and sparse coding, which builds image representations through a hierarchical network. It includes three parts: SIFT features’ extraction, sparse coding and spatial pyramid max pooling. To mimic visual cortex, spatial pyramid max pooling is, firstly, performed on the original SIFT features in the image patches, which distils the features and extracts the most distinctive and significant feature, the SIFT-pooled feature, in each local patch, instead of using the original SIFT features as usual. Then, a dictionary is trained using some random SIFT-pooled features and sparse coding is performed using the trained dictionary for all SIFT-pooled features through K-singular value decomposition algorithm. Finally, on the sparse codes of all image patches, spatial pyramid max pooling is carried again on the image level. The image representations will be built by concatenating the pooling features of each level. The authors use the algorithm and simple linear support vector machine (SVM) for image classification on three datasets: Caltech-101, Caltech-256 and 15-Scenes and the experimental results show that the authors algorithm can reach a competitive performance compared with recently published results.