In order to develop a new device for automatic quality control of grapes stored in crates just before pressing, it is necessary to specify many parameters. Among these, lighting is particularly important, both for the recognition methods and for the control system physical design and cost. This study introduces a database of images of grapes in crates, created specifically for the study, and investigates the possibility of recognizing healthy grapes from other visible elements (diseases, leaves. . . ) with four different lighting conditions and two classifiers (SVM and CNN). The experimental results show the feasibility of the system and provide objective and quantified elements to guide its design.
Deep learning has reached excellent results in various applications of computer vision, such as image classification, segmentation or object detection. However, due to the lack of labeled data, it is not always possible to fully exploit the potential of this approach for target recognition in synthetic aperture radar (SAR) images. Indeed, most of the time, the targets are not available for a large range of aspect or depression angles. Moreover, unlike in computer vision, common data augmentation cannot be considered because of the physical mechanisms arising in SAR imaging. To overcome these difficulties, we can use simulators based on physical models. Unfortunately, these models are either too simplified to generate realistic SAR images or require too much calculation time. Moreover, even the most accurate model cannot include all physical phenomena. Thus, fine-tuning or domain adaptation methods should be implemented. Another way, considered in this paper, consists in using Generative Adversarial Networks (GAN) to generate synthetic SAR images. However, training GANs from a small database is still a challenging problem. In this contribution, to complete the missing aspect angles in the database, we explore several GANs with class and aspect angle conditions. Numerical results show that they allow to improve the performance of classifiers.
The main goal of object detection is to localize objects in a given image and assign to each object fits corresponding class label. Performing effective approaches in infrared images is a challenging problem due to the variation of the target signature caused by changes in the environment, viewpoint variation or the state of the target. Convolutional Neural Networks (CNN) models already lead to accurate performances on traditional computer vision problems, and they have also show their capabilities to more specific applications like radar, sonar or infrared imaging. For target detection, two main approaches can be used: two-stage detector or one-stage detector. In this contribution we investigate the two-stage Faster-RCNN approach and propose to use a compact CNN model as backbone in order to speed-up the computational time without damaging the detection performance. The proposed model is evaluated on the dataset SENSIAC, made of 16 bits gray-value image sequences, and compared to Faster-RCNN with VGG19 as backbone and the one-stage model SSD.
Performing reliable target recognition in infrared imagery is a challenging problem due to the variation of the signatures of the targets caused by changes in the environment, the viewpoint or the state of the targets. Due to their state-of-the-art performance on several computer vision problems, Convolutional Neural Networks (CNNs) are particularly appealing in this context. However, CNNs may provide wrong classification results with high confidence. Robustness to disturbed inputs can be mitigated through the implementation of specific training strategies to improve classification performances. But they would generally require retraining or fine-tuning the CNN to face new forms of disturbed inputs. Besides, such strategies do not necessarily tackle novelty detection without training an auxiliary classifier. In this paper we propose two solutions to give the ability of a trained CNN to deal with both adversarial examples and novelty detection during inference. The first approach is based on one-class support vector machines (SVM) and the second one relies on the Local Outlier Factor (LOF) algorithm for example detection. We benchmark our contributions on SENSIAC database for a pre-trained network and evaluate how they may help mitigate false classifications on outliers and adversarial inputs.
KEYWORDS: Wavelets, Denoising, Wavelet transforms, Signal processing, Signal to noise ratio, Filtering (signal processing), Algorithms, Signal analyzers, Electronic filtering, Projection systems
This paper deals with the rational wavelet transform apply to a wavelet shrinkage problem. The rational multiresolution analysis (MRA) allows a better adaptation of the scale factor to the signal components than the dyadic one. The theory of the rational MRA is reviewed and a pyramidal algorithm for the computation of the fast orthogonal wavelet transform is proposed. Both, the analysis and the synthesis parts of the process are detailed. Moreover, using filters defined in Fourier domain, the implementation of the proposed algorithm is extended to this space. To illustrate the potential of rational analysis for signal processing, a wavelet shrinkage
application is presented.
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