Regularity scalable image coding based on wavelet singularity detection

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc

Extending the stixel world using polynomial ground manifold approximation

Stixel-based segmentation is specifically designed towards obstacle detection which combines road surface estimation in traffic scenes, stixel calculations, and stixel clustering. Stixels are defined by observed height above road surface. Road surfaces (ground manifolds) are represented by using an occupancy grid map. Stixel-based segmentation may improve the accuracy of real-time obstacle detection, especially if adaptive to changes in ground manifolds (e.g. with respect to non-planar road geometry). In this paper, we propose the use of a polynomial curve fitting algorithm based on the v-disparity space for ground manifold estimation. This is beneficial for two reasons. First, the coordinate space has inherently finite boundaries, which is useful when working with probability densities. Second, it leads to reduced computation time. We combine height segmentation and improved ground manifold algorithms together for stixel extraction. Our experimental results show a significant improvement in the accuracy of the ground manifold detection (an 8% improvement) compared to occupancy-grid mapping methods

A novel method for handling vehicle occlusion in visual traffic surveillance

This paper presents a novel algorithm for handling occlusion in visual traffic surveillance (VTS) by geometrically splitting the model that has been fitted onto the composite binary vehicle mask of two occluded vehicles. The proposed algorithm consists of a critical points detection step, a critical points clustering step and a model partition step using the vanishing point of the road. The critical points detection step detects the major critical points on the contour of the binary vehicle mask. The critical points clustering step selects the best critical points among the detected critical points as the reference points for the model partition. The model partition step partitions the model by exploiting the information of the vanishing point of the road and the selected critical points. The proposed algorithm was tested on a number of real traffic image sequences, and has demonstrated that it can successfully partition the model that has been fitted onto two occluded vehicles. To evaluate the accuracy, the dimensions of each individual vehicle are estimated based on the partitioned model. The estimation accuracies in vehicle width, length and height are 95.5%, 93.4% and 97.7% respectively.published_or_final_versio

Biologically inspired composite image sensor for deep field target tracking

The use of nonuniform image sensors in mobile based computer vision applications can be an effective solution when computational burden is problematic. Nonuniform image sensors are still in their infancy and as such have not been fully investigated for their unique qualities nor have they been extensively applied in practice. In this dissertation a system has been developed that can perform vision tasks in both the far field and the near field. In order to accomplish this, a new and novel image sensor system has been developed. Inspired by the biological aspects of the visual systems found in both falcons and primates, a composite multi-camera sensor was constructed. The sensor provides for expandable visual range, excellent depth of field, and produces a single compact output image based on the log-polar retinal-cortical mapping that occurs in primates. This mapping provides for scale and rotational tolerant processing which, in turn, supports the mitigation of perspective distortion found in strict Cartesian based sensor systems. Furthermore, the scale-tolerant representation of objects moving on trajectories parallel to the sensor\u27s optical axis allows for fast acquisition and tracking of objects moving at high rates of speed. In order to investigate how effective this combination would be for object detection and tracking at both near and far field, the system was tuned for the application of vehicle detection and tracking from a moving platform. Finally, it was shown that the capturing of license plate information in an autonomous fashion could easily be accomplished from the extraction of information contained in the mapped log-polar representation space. The novel composite log-polar deep-field image sensor opens new horizons for computer vision. This current work demonstrates features that can benefit applications beyond the high-speed vehicle tracking for drivers assistance and license plate capture. Some of the future applications envisioned include obstacle detection for high-speed trains, computer assisted aircraft landing, and computer assisted spacecraft docking