ResSeg: Residual encoder-decoder convolutional neural network for food segmentation

This paper presents the implementation and evaluation of different convolutional neural network architectures focused on food segmentation. To perform this task, it is proposed the recognition of 6 categories, among which are the main food groups (protein, grains, fruit, vegetables) and two additional groups, rice and drink or juice. In addition, to make the recognition more complex, it is decided to test the networks with food dishes already started, i.e. during different moments, from its serving to its finishing, in order to verify the capability to see when there is no more food on the plate. Finally, a comparison is made between the two best resulting networks, a SegNet with architecture VGG-16 and a network proposed in this work, called Residual Segmentation Convolutional Neural Network or ResSeg, with which accuracies greater than 90% and interception-over-union greater than 75% were obtained. This demonstrates the ability, not only of SegNet architectures for food segmentation, but the use of residual layers to improve the contour of the segmentation and segmentation of complex distribution or initiated of food dishes, opening the field of application of this type of networks to be implemented in feeding assistants or in automated restaurants, including also for dietary control for the amount of food consumed

Offline signature verification using DAG-CNN

This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures

Fruit Identification and Quality Detection by Means of DAG-CNN

The design of quality control systems in food has become essential in research to guarantee an adequate state for its consumption. It is necessary to develop automatic and efficient systems that can verify its state before its distribution. This paper presents an algorithm based on deep learning for the identification of fruits and the state they are in, oriented to changes in camera focus, capture angles, lighting variations, and change of backgrounds. In this case, 8 types of fruit are chosen to identify what kind of fruit is being observed and if it is in good condition or not, establishing a total of 16 categories that the network must classify. A convolutional neural network with a DAG structure is proposed for the learning of fruits and their state. A graphic user interface is designed to allow the acquisition of the image of the fruit and its subsequent classification in some of the categories. A 94.43% accuracy was obtained in the 1600 test images classification, with approximate processing times of 45-55 milliseconds. Therefore, it can be concluded that the proposed system based on Deep learning can adequately perform a process of detection of types of fruits and their state