Bit-plane stack filter algorithm for focal plane processors

This work presents a novel parallel technique to implement stack morphological filters for image processing. The method relies on applying the image bitwise decomposition to manipulate the grayscale image at a bit-plane level, while simple logical operations and Positive Boolean Functions (PBF’s) are executed in parallel to derive the transformed bit-planes. The relationship between the bitwise and threshold decomposition is closely investigated and analysed, which lead us to derive an algorithm whose control flow is full binary encoded. Furthermore, the algorithm exhibits an interesting performance, which depends on the image histogram thanks to its hierarchical processing and the study of the relationship among binary decompositions

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

A mathematical morphology based approach for vehicle detection in road tunnels

A novel approach to automatically detect vehicles in road tunnels is presented in this paper. Non-uniform and poor illumination conditions prevail in road tunnels making difficult to achieve robust vehicle detection. In order to cope with the illumination issues, we propose a local higher-order statistic filter to make the vehicle detection invariant to illumination changes, whereas a morphological-based background subtraction is used to generate a convex hull segmentation of the vehicles. An evaluation test comparing our approach with a benchmark object detector shows that our approach outperforms in terms of false detection rate and overlap area detection

Object identification by using orthonormal circus functions from the trace transform

In this paper we present an efficient way to both compute and extract salient information from trace transform signatures to perform object identification tasks. We also present a feature selection analysis of the classical trace-transform functionals, which reveals that most of them retrieve redundant information causing misleading similarity measurements. In order to overcome this problem, we propose a set of functionals based on Laguerre polynomials that return orthonormal signatures between these functionals. In this way, each signature provides salient and non-correlated information that contributes to the description of an image object. The proposed functionals were tested considering a vehicle identification problem, outperforming the classical trace transform functionals in terms of computational complexity and identification rate

Real-time vehicle matching for multi-camera tunnel surveillance

Tracking multiple vehicles with multiple cameras is a challenging problem of great importance in tunnel surveillance. One of the main challenges is accurate vehicle matching across the cameras with non-overlapping fields of view. Since systems dedicated to this task can contain hundreds of cameras which observe dozens of vehicles each, for a real-time performance computational efficiency is essential. In this paper, we propose a low complexity, yet highly accurate method for vehicle matching using vehicle signatures composed of Radon transform like projection profiles of the vehicle image. The proposed signatures can be calculated by a simple scan-line algorithm, by the camera software itself and transmitted to the central server or to the other cameras in a smart camera environment. The amount of data is drastically reduced compared to the whole image, which relaxes the data link capacity requirements. Experiments on real vehicle images, extracted from video sequences recorded in a tunnel by two distant security cameras, validate our approach

Vehicle classification for road tunnel surveillance

Vehicle classification for tunnel surveillance aims to not only retrieve vehicle class statistics, but also prevent accidents by recognizing vehicles carrying dangerous goods. In this paper, we describe a method to classify vehicle images that experience different geometrical variations and challenging photometrical conditions such as those found in road tunnels. Unlike previous approaches, we propose a classification method that does not rely on the length and height estimation of the vehicles. Alternatively, we propose a novel descriptor based on trace transform signatures to extract salient and non-correlated information of the vehicle images. Also, we propose a metric that measures the complexity of the vehicles’ shape based on corner point detection. As a result, these features describe the vehicle’s appearance and shape complexity independently of the scale, pose, and illumination conditions. Experiments with vehicles captured from three different cameras confirm the saliency and robustness of the features proposed, achieving an overall accuracy of 97.5% for the classification of four different vehicle classes. For vehicles transporting dangerous goods, our classification scheme achieves an average recall of 97.6% at a precision of 98.6% for the combination of lorries and tankers, which is a very good result considering the scene conditions

Non-overlapping multi-camera detection and tracking of vehicles in tunnel surveillance

We propose a real-time multi-camera tracking approach to follow vehicles in a tunnel surveillance environment with multiple non-overlapping cameras. In such system, vehicles have to be tracked in each camera and passed correctly from one camera to another through the tunnel. This task becomes extremely difficult when intra-camera errors are accumulated. Most typical issues to solve in tunnel scenes are due to low image quality, poor illumination and lighting from the vehicles. Vehicle detection is performed using Adaboost detector, speeded up by separating different cascades for cars and trucks improving general accuracy of detection. A Kalman Filter with two observations, given by the vehicle detector and an averaged optical flow vector, is used for single-camera tracking. Information from collected tracks is used for feeding the inter-camera matching algorithm, which measures the correlation of Radon transform-like projections between the vehicle images. Our main contribution is a novel method to reduce the false positive rate induced by the detection stage. We impose recall over precision in the detection correctness, and identify false positives patterns which are then included subsequently in a high-level decision making step. Results are presented for the case of 3 cameras placed consecutively in an inter-city tunnel. We demonstrate the increased tracking performance of our method compared to existing Bayesian filtering techniques for vehicle tracking in tunnel surveillance

Histogram computation based on image bitwise decomposition

In this paper, a new method to compute the image histogram is presented, along with the image maximum and minimum values. It is intended for highly parallel architectures such as the ones found in Focal Plane Processors (FPP). This new approach exploits this parallelism relying on the privatization technique to avoid the memory collision problem, while the bin frequency is obtained through image bitwise manipulation. Unlike traditional privatization techniques, our method exhibits a trade off between processing time and bin size. That is, it can be adapted as a power-of-two bin size histogram and the computation time decreases exponentially as the bin size is reduced on each power of two, allowing high computational flexibility