Road maintenance management presents a complex task for road authorities. The first presumption for the evaluation analysis and correct road construction rehabilitation is to have precise and up-to-date information about road pavement condition and level degradation. Different road crack types were proposed in the state of art in order to provide useful information for making pavement maintenance strategies. For this reason, we present in this paper a novel research to automatically detect and classify road cracks on two-dimensional digital images. Indeed, our proposed package is composed of two methods: crack detection and crack classification. The first method consists in detecting the cracks on images acquired by the VIAPIX® system developed by our company ACTRIS. To do so, we are based on our unsupervised approach cited in for road crack detection on two-dimensional pavement images. Then, in order to categorize each of the detected cracks, the second method of our package is applied. Based on principal component analysis (PCA), our method permits the classification of all the detected cracks into three types: vertical, horizontal, and oblique. The obtained results demonstrate the efficiency of our robust approaches in terms of good detection and classification on a variety of pavement images.
In this paper we present a novel approach for road sign identification and geolocation based on Joint Transform Correlator “JTC” and VIAPIX module. The proposed method is divided into three parts: identification, gathering and geolocation. The first part permits to detect and identify road signs on images acquired by the VIAPIX module  developed by our company ACTRIS . To do so, we are based on our own method cited in  for road sign identification. The second part of our proposed approach consists in gathering the identified road sign by using the JTC technique . Since the VIAPIX® module provides images at an interval of one image per meter, we identify each road sign by finding the number of images where this road sign has been recognized while computing thereby on each of these images its corresponding pixel coordinates. Finally, each road sign is geolocated using its pixel coordinates on several images. At this stage, we are based on the axial stereovision method . Indeed, relying on the pixel coordinates and the distance between different images, we compute the 3D coordinates of each road sign. Thus, GPS coordinates can be then found using the GPS position of the vehicle basing on Vincenty formulae .
Today, communications security, i.e. the discipline of preventing unauthorized interceptors from accessing telecommunications in an intelligible form, while still delivering content to the intended recipients, is a main issue in our modern society especially. In this paper, attention is drawn to the importance and relevance of optical correlation techniques for detection and tracking people. In order to be efficient, these techniques need pre- or post-processing steps to take into account the environmental conditions. The aim of this work is to improve the performance of the optical correlation method, based on a new decision process in order to reduce the false detection rate. To realize this, we propose a method using a VanderLugt correlator with a phase-only filter for face recognition using two criteria for decision making based on the values of the peak-to-correlation energy and the energy distribution in different parts of the correlation plane. In the three-step algorithm, the first stage consists by dividing the correlation plane into nine equal sub-planes. In the second stage the energy of each sub-plane is computed, while in the last stage the classification criterion is realized and the recognition rate is calculated. Numerous tests were performed using the Pointing Head Pose Image Database. They show the effectiveness of the method in terms of face recognition detection rate without pre-processing phase and with 0% false detection.
In this study we present a novel approach for road mark detection and recognition based on the commercial VIAPIX® module. The proposed approach combines two different techniques, an optical one based on correlation and a numerical technique based on the linear SVM (Support Vector Machine) classifier using HOG (Histogram of Gradient) as descriptor. The first step of our proposed approach consists to applying an inverse perspective mapping of the image acquired by the VIAPIX® module. Then, white color segmentation is applied in order to detect all road marks on the road. Next, a classification of the detected objects is performed using the correlation technique. Finally, the linear SVM technique is used for validating the recognized objects.
In this paper, we propose and validate a new system used to explore road assets. In this work we are interested
on the vertical road signs. To do this, we are based on the combination of road signs detection, recognition and
identification using data provides by sensors. The proposed approach consists on using panoramic views
provided by the innovative device, VIAPIX®1, developed by our company ACTRIS2. We are based also on the
optimized correlation technique for road signs recognition and identification on pictures. Obtained results shows
the interest on using panoramic views compared to results obtained using images provided using only one
In this study, we propose a numerical implementation (using a GPU) of an optimized multiple image
compression and encryption technique. We first introduce the double optimization procedure for spectrally
multiplexing multiple images. This technique is adapted, for a numerical implementation, from a recently
proposed optical setup implementing the Fourier transform (FT)1. The new analysis technique is a combination
of a spectral fusion based on the properties of FT, a specific spectral filtering, and a quantization of the
remaining encoded frequencies using an optimal number of bits. The spectral plane (containing the information
to send and/or to store) is decomposed in several independent areas which are assigned according a specific way.
In addition, each spectrum is shifted in order to minimize their overlap. The dual purpose of these operations is
to optimize the spectral plane allowing us to keep the low- and high-frequency information (compression) and to
introduce an additional noise for reconstructing the images (encryption). Our results show that not only can the
control of the spectral plane enhance the number of spectra to be merged, but also that a compromise between
the compression rate and the quality of the reconstructed images can be tuned. Spectrally multiplexing multiple
images defines a first level of encryption. A second level of encryption based on a real key image is used to
reinforce encryption. Additionally, we are concerned with optimizing the compression rate by adapting the size
of the spectral block to each target image and decreasing the number of bits required to encode each block. This
size adaptation is realized by means of the root-mean-square (RMS) time-frequency criterion2. We have found
that this size adaptation provides a good trade-off between bandwidth of spectral plane and number of
reconstructed output images3. Secondly, the encryption rate is improved by using a real biometric key and
randomly changing the rotation angle of each block before spectral fusion. A numerical implementation of this
method using two numerical devices (CPU and GPU) is presented4.
In this paper, we defined a low complexity 2D-DCT architecture. The latter will be able to transform spatial pixels
to spectral pixels while taking into account the constraints of the considered compression standard. Indeed, this
work is our first attempt to obtain one reconfigurable multistandard DCT. Due to our new matrix decomposition,
we could define one common 2D-DCT architecture. The constant multipliers can be configured to handle the
case of RealDCT and/or IntDCT (multiplication by 2). Our optimized algorithm not only provides a reduction
of computational complexity, but also leads to scalable pipelined design in systolic arrays. Indeed, the 8 × 8
StdDCT can be computed by using 4×4 StdDCT which can be obtained by calculating 2×2 StdDCT. Besides,
the proposed structure can be extended to deal with higher number of N (i.e. 16 × 16 and 32 × 32). The
performance of the proposed architecture are better when compared with conventional designs. In particular,
for N = 4, it is found that the proposed design have nearly third the area-time complexity of the existing DCT
structures. This gain is expected to be higher for a greater size of 2D-DCT.
Correlation is based pattern recognition is primarily based on the matching of contours between an unknown
target image and a known reference image. However, it does not usually include the color image information in
the decision making process. In order to render the correlation method sensitive to color change, we propose a
generalized method based on the decomposition of the target image in its three color components using, either
the normalized RGB (red, green, blue) color space, or the normalized HSV (hue, saturation, value) space. Then,
the correlation operation is carried out for each color component and the results are merged in order to make a
final decision. The aforementioned steps can alleviate majority of the problems associated with illumination
changes in the target image by utilizing color information of the target image. To overcome these problems, we
propose to convert the color based contour information into a signature corresponding to the color information of
the target image. Test results are presented to validate the effectiveness of the proposed technique.