Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Bounded PCA based Multi Sensor Image Fusion Employing Curvelet Transform Coefficients

The fusion of thermal and visible images acts as an important device for target detection. The quality of the spectral content of the fused image improves with wavelet-based image fusion. However, compared to PCA-based fusion, most wavelet-based methods provide results with a lower spatial resolution. The outcome gets better when the two approaches are combined, but they may still be refined. Compared to wavelets, the curvelet transforms more accurately depict the edges in the image. Enhancing the edges is a smart way to improve spatial resolution and the edges are crucial for interpreting the images. The fusion technique that utilizes curvelets enables the provision of additional data in both spectral and spatial areas concurrently. In this paper, we employ an amalgamation of Curvelet Transform and a Bounded PCA (CTBPCA) method to fuse thermal and visible images. To evidence the enhanced efficiency of our proposed technique, multiple evaluation metrics and comparisons with existing image merging methods are employed. Our approach outperforms others in both qualitative and quantitative analysis, except for runtime performance. Future Enhancement-The study will be based on using the fused image for target recognition. Future work should also focus on this method’s continued improvement and optimization for real-time video processing

Suspension Testing of 3 Heavy Vehicles - Methodology and Preliminary Frequency Analysis

Three air-sprung heavy vehicles (HVs) were instrumented and tested on typical suburban and highway road sections at typical operational speeds. The vehicles used were a tri-axle semi-trailer towed with a prime mover, an interstate coach with 3 axles and a school bus with 2 axles. The air springs (air bags) of the axle/axle group of interest were configured such that they could be connected using either standard longitudinal air lines or an innovative suspension system comprising larger-than-standard longitudinal air lines. Data for dynamic forces on axles, wheels and chassis were gathered for the purposes of: analysis of the relative performance of the HVs for the two sizes of air lines; informing the QUT/Main Roads project Heavy vehicle suspensions – testing and analysis; and providing a reference source for future projects. This reports sets down the methodology and preliminary results of the testing carried out. Accordingly, Fast-Fourier plots are provided to show indicative frequency spectra for HV axles, wheel forces and air springs during typical use. The results are documented in Appendices 3 to 5. There appears to be little or no correlation between dynamic forces in the air springs and the wheel forces in the HVs tested. Axle-hop at frequencies between 10-15 Hz predominated for unsprung masses in the HV suspensions tested. Air-spring forces are present in the sub-1.0 Hz to approximately 2 Hz frequency range. With the qualification that only one set of data from each test speed is presented herein, in general, the peaks in the frequency spectra of the body-bounce forces and wheel forces were reduced for the tests with the larger longitudinal air lines. More research needs to be done on the load sharing mechanisms between axles on air-sprung HVs. In particular, how and whether improved load sharing can be effected and whether better load sharing between axles will reduce dynamic wheel and chassis forces. This last point, in particular, in relation to the varied dynamic measures used by the HV testing community to compare different suspension types

On Using High-Definition Body Worn Cameras for Face Recognition from a Distance

Recognition of human faces from a distance is highly desirable for law-enforcement. This paper evaluates the use of low-cost, high-definition (HD) body worn video cameras for face recognition from a distance. A comparison of HD vs. Standard-definition (SD) video for face recognition from a distance is presented. HD and SD videos of 20 subjects were acquired in different conditions and at varying distances. The evaluation uses three benchmark algorithms: Eigenfaces, Fisherfaces and Wavelet Transforms. The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video. This scenario resembles real-life conditions of video surveillance and law-enforcement activities. However, at a close range, face data obtained from SD video result in similar, if not better recognition accuracy than using HD face data of the same range