Shape Similarity Measurement for Known-Object Localization: A New Normalized Assessment

International audienceThis paper presents a new, normalized measure for assessing a contour-based object pose. Regarding binary images, the algorithm enables supervised assessment of known-object recognition and localization. A performance measure is computed to quantify differences between a reference edge map and a candidate image. Normalization is appropriate for interpreting the result of the pose assessment. Furthermore, the new measure is well motivated by highlighting the limitations of existing metrics to the main shape variations (translation, rotation, and scaling), by showing how the proposed measure is more robust to them. Indeed, this measure can determine to what extent an object shape differs from a desired position. In comparison with 6 other approaches, experiments performed on real images at different sizes/scales demonstrate the suitability of the new method for object-pose or shape-matching estimation

Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

In this paper, we present a new color image regularization method using a rotating smoothing filter. This approach combines a pixel classification method, which roughly determines if a pixel belongs to a homogenous region or an edge with an anisotropic perceptual edge detector capable of computing two precise diffusion directions. Using a now classical formulation, image regularization is here treated as a variational model, where successive iterations of associated PDE (Partial Differential Equation) are equivalent to a diffusion process. Our model uses two kinds of diffusion: isotropic and anisotropic diffusion. Anisotropic diffusion is accurately controlled near edges and corners, while isotropic diffusion is applied to smooth regions either homogeneous or corrupted by noise. A comparison of our approach with other regularization methods applied on real images demonstrate that our model is able to efficiently restore images as well as handle diffusion, and at the same time preserve edges and corners well

RSD-DOG : A New Image Descriptor based on Second Order Derivatives

International audienceThis paper introduces the new and powerful image patch de-scriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order fea-tures/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC

A Novel Image Descriptor Based on Anisotropic Filtering

International audienceIn this paper, we present a new image patch descriptor for object detection and image matching. The descriptor is based on the standard HoG pipeline. The descriptor is generated in a novel way, by embedding the response of an oriented anisotropic derivative half Gaussian kernel in the Histogram of Orientation Gradient (HoG) framework. By doing so, we are able to bin more curvature information. As a result, our descriptor performs better than the state of art descriptors such as SIFT, GLOH and DAISY. In addition to this, we repeat the same procedure by replacing the anisotropic derivative half Gaussian kernel with a compu-tationally less complex anisotropic derivative half exponential kernel and achieve similar results. The proposed image descriptors using both the kernels are very robust and shows promising results for variations in brightness, scale, rotation, view point, blur and compression. We have extensively evaluated the effectiveness of the devised method with various challenging image pairs acquired under varying circumstances

Faked Speech Detection with Zero Knowledge

Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word 'blindly' refers to the ability to detect mimicked audio without references or real sources. The proposed model was trained on a set of important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English). These datasets have been made available, in raw form, through GitHub for the use of the research community at https://github.com/SaSs7/Dataset. For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy.Comment: 14 pages, 4 figures (6 if you count subfigures), 2 table

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results

Outgassing Behavior of C/2012 S1 (ISON) From September 2011 to June 2013

We report photometric observations for comet C/2012 S1 (ISON) obtained during the time period immediately after discovery (r=6.28 AU) until it moved into solar conjunction in mid-2013 June using the UH2.2m, and Gemini North 8-m telescopes on Mauna Kea, the Lowell 1.8m in Flagstaff, the Calar Alto 1.2m telescope in Spain, the VYSOS-5 telescopes on Mauna Loa Hawaii and data from the CARA network. Additional pre-discovery data from the Pan STARRS1 survey extends the light curve back to 2011 September 30 (r=9.4 AU). The images showed a similar tail morphology due to small micron sized particles throughout 2013. Observations at sub-mm wavelengths using the JCMT on 15 nights between 2013 March 9 (r=4.52 AU) and June 16 (r=3.35 AU) were used to search for CO and HCN rotation lines. No gas was detected, with upper limits for CO ranging between (3.5-4.5)E27 molec/s. Combined with published water production rate estimates we have generated ice sublimation models consistent with the photometric light curve. The inbound light curve is likely controlled by sublimation of CO2. At these distances water is not a strong contributor to the outgassing. We also infer that there was a long slow outburst of activity beginning in late 2011 peaking in mid-2013 January (r~5 AU) at which point the activity decreased again through 2013 June. We suggest that this outburst was driven by CO injecting large water ice grains into the coma. Observations as the comet came out of solar conjunction seem to confirm our models.Comment: 8 pages, 2 figures, 3 table

Shape Similarity Measurement for Known-Object Localization: A New Normalized Assessment

This paper presents a new, normalized measure for assessing a contour-based object pose. Regarding binary images, the algorithm enables supervised assessment of known-object recognition and localization. A performance measure is computed to quantify differences between a reference edge map and a candidate image. Normalization is appropriate for interpreting the result of the pose assessment. Furthermore, the new measure is well motivated by highlighting the limitations of existing metrics to the main shape variations (translation, rotation, and scaling), by showing how the proposed measure is more robust to them. Indeed, this measure can determine to what extent an object shape differs from a desired position. In comparison with 6 other approaches, experiments performed on real images at different sizes/scales demonstrate the suitability of the new method for object-pose or shape-matching estimation

Détection de Contours et Diffusion Anisotropique dans les Images

Ce manuscrit rapporte des travaux développés pendant un an et demi puisque j'ai changé de sujet de thèse en 2010 et également de laboratoire. Pendant ce peu de temps, de nombreux thèmes ont été abordés et plusieurs travaux ont été développés, mais certaines études méritent d'être terminées.This thesis is devoted to the anisotropic edge detection and the anisotropic diffusion in images. Based on Gaussian half filters, these half filters scan all orientations around a pixel with a defined step. From oriented derivatives of Gaussian half filters, an approach to edge detection in color images has been developed. Since the filter is long and thin, this method allows to extract anisotropically the contours of the large structures but also of small objects, even in the presence of noise. Gamma curves enhance dark or over-illuminated areas of an image. The combined use of these half filters and gamma curves provide an additional robustness to the method as it is able to extract edges of objects, even in areas under and over-exposed of an image. The second work in this thesis is concerned with the extraction of ridges in the images using the difference of two anisotropic half Gaussians. This new method is very accurate for roof edges even if they are strongly curved or bended. Moreover, the use of half filters provide additional information at level of the junctions which correspond to the local maxima. The final topic of this thesis is dedicated to the anisotropic diffusion in images. Thus, three new regularization schemes have been developed either for removing the textures or for denoising. This last scheme provides interesting results, even in highly noisy color images.Ce mémoire de thèse est consacré à la détection anisotrope de contours et à la diffusion anisotropique dans les images qui utilisent des demi-filtres gaussiens. Ces demi-filtres parcourent toutes les orientations possibles autour d'un pixel avec un pas de discrétisation choisi. A partir de demi-filtres orientés de dérivées de gaussiennes anisotropes, une nouvelle approche de détection de bords dans les images couleurs a été développée. Puisque le filtre est allongé et fin, cette méthode permet d'extraire les contours des larges structures mais également des petits objets, même en présence de bruit. Les courbes gamma mettent en évidence des parties sombres ou sur-éclairées d'une image. L'utilisation combinée de ces demi-filtres et des courbes gamma apporte une robustesse supplémentaire au filtre puisqu'il est capable d'extraire les bords des objets, même dans les parties sous et sur-exposées d'une image. Le deuxième travail dans cette thèse concerne l'extraction de lignes de crêtes dans les images à partir de différence de deux demi-gaussiennes anisotropiques. Cette nouvelle méthode rend très précise la détection de ce type de contours même au niveau des lignes fortement courbées ou coudées. De plus, l'utilisation de demi-filtres apporte une information supplémentaire au niveau des jonctions qui correspondent aux maxima locaux. Enfin, le dernier thème de cette thèse est dédié à la diffusion anisotropique dans les images. Ainsi, trois nouveaux schémas de régularisation ont été développés dont deux pour supprimer les textures et un pour le débruitage. Ce dernier schéma donne des résultats intéressants, même dans des images en couleurs fortement bruitées